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Sample records for survival model applied

  1. Applied survival analysis using R

    CERN Document Server

    Moore, Dirk F

    2016-01-01

    Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics...

  2. A Log Logistic Survival Model Applied to Hypobaric Decompression Sickness

    Science.gov (United States)

    Conkin, Johnny

    2001-01-01

    Decompression sickness (DCS) is a complex, multivariable problem. A mathematical description or model of the likelihood of DCS requires a large amount of quality research data, ideas on how to define a decompression dose using physical and physiological variables, and an appropriate analytical approach. It also requires a high-performance computer with specialized software. I have used published DCS data to develop my decompression doses, which are variants of equilibrium expressions for evolved gas plus other explanatory variables. My analytical approach is survival analysis, where the time of DCS occurrence is modeled. My conclusions can be applied to simple hypobaric decompressions - ascents lasting from 5 to 30 minutes - and, after minutes to hours, to denitrogenation (prebreathing). They are also applicable to long or short exposures, and can be used whether the sufferer of DCS is at rest or exercising at altitude. Ultimately I would like my models to be applied to astronauts to reduce the risk of DCS during spacewalks, as well as to future spaceflight crews on the Moon and Mars.

  3. Repair models of cell survival and corresponding computer program for survival curve fitting

    International Nuclear Information System (INIS)

    Shen Xun; Hu Yiwei

    1992-01-01

    Some basic concepts and formulations of two repair models of survival, the incomplete repair (IR) model and the lethal-potentially lethal (LPL) model, are introduced. An IBM-PC computer program for survival curve fitting with these models was developed and applied to fit the survivals of human melanoma cells HX118 irradiated at different dose rates. Comparison was made between the repair models and two non-repair models, the multitar get-single hit model and the linear-quadratic model, in the fitting and analysis of the survival-dose curves. It was shown that either IR model or LPL model can fit a set of survival curves of different dose rates with same parameters and provide information on the repair capacity of cells. These two mathematical models could be very useful in quantitative study on the radiosensitivity and repair capacity of cells

  4. Modelling survival

    DEFF Research Database (Denmark)

    Ashauer, Roman; Albert, Carlo; Augustine, Starrlight

    2016-01-01

    The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test...

  5. Modelling the joint distribution of competing risks survival times using copula functions

    OpenAIRE

    Kaishev, V. K.; Haberman, S.; Dimitrova, D. S.

    2005-01-01

    The problem of modelling the joint distribution of survival times in a competing risks model, using copula functions is considered. In order to evaluate this joint distribution and the related overall survival function, a system of non-linear differential equations is solved, which relates the crude and net survival functions of the modelled competing risks, through the copula. A similar approach to modelling dependent multiple decrements was applied by Carriere (1994) who used a Gaussian cop...

  6. Determining factors influencing survival of breast cancer by fuzzy logistic regression model.

    Science.gov (United States)

    Nikbakht, Roya; Bahrampour, Abbas

    2017-01-01

    Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.

  7. Survival analysis models and applications

    CERN Document Server

    Liu, Xian

    2012-01-01

    Survival analysis concerns sequential occurrences of events governed by probabilistic laws.  Recent decades have witnessed many applications of survival analysis in various disciplines. This book introduces both classic survival models and theories along with newly developed techniques. Readers will learn how to perform analysis of survival data by following numerous empirical illustrations in SAS. Survival Analysis: Models and Applications: Presents basic techniques before leading onto some of the most advanced topics in survival analysis.Assumes only a minimal knowledge of SAS whilst enablin

  8. Predictive model for survival in patients with gastric cancer.

    Science.gov (United States)

    Goshayeshi, Ladan; Hoseini, Benyamin; Yousefli, Zahra; Khooie, Alireza; Etminani, Kobra; Esmaeilzadeh, Abbas; Golabpour, Amin

    2017-12-01

    Gastric cancer is one of the most prevalent cancers in the world. Characterized by poor prognosis, it is a frequent cause of cancer in Iran. The aim of the study was to design a predictive model of survival time for patients suffering from gastric cancer. This was a historical cohort conducted between 2011 and 2016. Study population were 277 patients suffering from gastric cancer. Data were gathered from the Iranian Cancer Registry and the laboratory of Emam Reza Hospital in Mashhad, Iran. Patients or their relatives underwent interviews where it was needed. Missing values were imputed by data mining techniques. Fifteen factors were analyzed. Survival was addressed as a dependent variable. Then, the predictive model was designed by combining both genetic algorithm and logistic regression. Matlab 2014 software was used to combine them. Of the 277 patients, only survival of 80 patients was available whose data were used for designing the predictive model. Mean ?SD of missing values for each patient was 4.43?.41 combined predictive model achieved 72.57% accuracy. Sex, birth year, age at diagnosis time, age at diagnosis time of patients' family, family history of gastric cancer, and family history of other gastrointestinal cancers were six parameters associated with patient survival. The study revealed that imputing missing values by data mining techniques have a good accuracy. And it also revealed six parameters extracted by genetic algorithm effect on the survival of patients with gastric cancer. Our combined predictive model, with a good accuracy, is appropriate to forecast the survival of patients suffering from Gastric cancer. So, we suggest policy makers and specialists to apply it for prediction of patients' survival.

  9. Locally Applied Valproate Enhances Survival in Rats after Neocortical Treatment with Tetanus Toxin and Cobalt Chloride

    Directory of Open Access Journals (Sweden)

    Dirk-Matthias Altenmüller

    2013-01-01

    Full Text Available Purpose. In neocortical epilepsies not satisfactorily responsive to systemic antiepileptic drug therapy, local application of antiepileptic agents onto the epileptic focus may enhance treatment efficacy and tolerability. We describe the effects of focally applied valproate (VPA in a newly emerging rat model of neocortical epilepsy induced by tetanus toxin (TeT plus cobalt chloride (CoCl2. Methods. In rats, VPA ( or sodium chloride (NaCl ( containing polycaprolactone (PCL implants were applied onto the right motor cortex treated before with a triple injection of 75 ng TeT plus 15 mg CoCl2. Video-EEG monitoring was performed with intracortical depth electrodes. Results. All rats randomized to the NaCl group died within one week after surgery. In contrast, the rats treated with local VPA survived significantly longer (. In both groups, witnessed deaths occurred in the context of seizures. At least of the rats surviving the first postoperative day developed neocortical epilepsy with recurrent spontaneous seizures. Conclusions. The novel TeT/CoCl2 approach targets at a new model of neocortical epilepsy in rats and allows the investigation of local epilepsy therapy strategies. In this vehicle-controlled study, local application of VPA significantly enhanced survival in rats, possibly by focal antiepileptic or antiepileptogenic mechanisms.

  10. Radiobilogical cell survival models

    International Nuclear Information System (INIS)

    Zackrisson, B.

    1992-01-01

    A central issue in clinical radiobiological research is the prediction of responses to different radiation qualities. The choice of cell survival and dose-response model greatly influences the results. In this context the relationship between theory and model is emphasized. Generally, the interpretations of experimental data depend on the model. Cell survival models are systematized with respect to their relations to radiobiological theories of cell kill. The growing knowlegde of biological, physical, and chemical mechanisms is reflected in the formulation of new models. The present overview shows that recent modelling has been more oriented towards the stochastic fluctuations connected to radiation energy deposition. This implies that the traditional cell surivival models ought to be complemented by models of stochastic energy deposition processes and repair processes at the intracellular level. (orig.)

  11. Exploring location influences on firm survival rates using parametric duration models

    NARCIS (Netherlands)

    Manzato, G.G.; Arentze, T.A.; Timmermans, H.J.P.; Ettema, D.F.; Timmermans, H.J.P.; Vries, de B.

    2010-01-01

    Using parametric duration models applied to an office firm dataset, we carried out an exploratory study about the location influences on firm survival rates. Amongst the variables included, we found that accessibility to infrastructure supply, regional effects, demographic and economic aspects, and

  12. Exploring location influences on firm survival rates using parametric duration models

    NARCIS (Netherlands)

    Manzato, G.G.; Arentze, T.A.; Timmermans, H.J.P.; Ettema, D.F.

    2011-01-01

    Using parametric duration models applied to an office firm dataset, we carried out an exploratory study about the location influences on firm survival rates. Amongst the variables included, we found that accessibility to infrastructure supply, regional effects, demographic and economic aspects, and

  13. Probabilistic Survivability Versus Time Modeling

    Science.gov (United States)

    Joyner, James J., Sr.

    2016-01-01

    This presentation documents Kennedy Space Center's Independent Assessment work completed on three assessments for the Ground Systems Development and Operations (GSDO) Program to assist the Chief Safety and Mission Assurance Officer during key programmatic reviews and provided the GSDO Program with analyses of how egress time affects the likelihood of astronaut and ground worker survival during an emergency. For each assessment, a team developed probability distributions for hazard scenarios to address statistical uncertainty, resulting in survivability plots over time. The first assessment developed a mathematical model of probabilistic survivability versus time to reach a safe location using an ideal Emergency Egress System at Launch Complex 39B (LC-39B); the second used the first model to evaluate and compare various egress systems under consideration at LC-39B. The third used a modified LC-39B model to determine if a specific hazard decreased survivability more rapidly than other events during flight hardware processing in Kennedy's Vehicle Assembly Building.

  14. Exploration of location influences on firm survival rates using parametric duration models

    NARCIS (Netherlands)

    Manzato, G.G.; Arentze, T.A.; Timmermans, H.J.P.; Ettema, D.F.

    2011-01-01

    This study explored the influences of location on business firm survival rates with the use of parametric duration models applied to a data set. Of the variables included, those found to be the most significant were accessibility to infrastructure supply, regional effects, demographic and economic

  15. Predicting and Modelling of Survival Data when Cox's Regression Model does not hold

    DEFF Research Database (Denmark)

    Scheike, Thomas H.; Zhang, Mei-Jie

    2002-01-01

    Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects...

  16. Modelling survival and connectivity of Mnemiopsis leidyi in the south-western North Sea and Scheldt estuaries

    DEFF Research Database (Denmark)

    van der Molen, J.; van Beek, J.; Augustine, Starrlight

    2015-01-01

    Three different models were applied to study the reproduction, survival and dispersal of Mnemiopsis leidyi in the Scheldt estuaries and the southern North Sea: a high-resolution particle tracking model with passive particles, a low-resolution particle tracking model with a reproduction model coup...

  17. Stage-specific predictive models for breast cancer survivability.

    Science.gov (United States)

    Kate, Rohit J; Nadig, Ramya

    2017-01-01

    Survivability rates vary widely among various stages of breast cancer. Although machine learning models built in past to predict breast cancer survivability were given stage as one of the features, they were not trained or evaluated separately for each stage. To investigate whether there are differences in performance of machine learning models trained and evaluated across different stages for predicting breast cancer survivability. Using three different machine learning methods we built models to predict breast cancer survivability separately for each stage and compared them with the traditional joint models built for all the stages. We also evaluated the models separately for each stage and together for all the stages. Our results show that the most suitable model to predict survivability for a specific stage is the model trained for that particular stage. In our experiments, using additional examples of other stages during training did not help, in fact, it made it worse in some cases. The most important features for predicting survivability were also found to be different for different stages. By evaluating the models separately on different stages we found that the performance widely varied across them. We also demonstrate that evaluating predictive models for survivability on all the stages together, as was done in the past, is misleading because it overestimates performance. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  18. Life-Cycle Models for Survivable Systems

    National Research Council Canada - National Science Library

    Linger, Richard

    2002-01-01

    .... Current software development life-cycle models are not focused on creating survivable systems, and exhibit shortcomings when the goal is to develop systems with a high degree of assurance of survivability...

  19. A generalized additive regression model for survival times

    DEFF Research Database (Denmark)

    Scheike, Thomas H.

    2001-01-01

    Additive Aalen model; counting process; disability model; illness-death model; generalized additive models; multiple time-scales; non-parametric estimation; survival data; varying-coefficient models......Additive Aalen model; counting process; disability model; illness-death model; generalized additive models; multiple time-scales; non-parametric estimation; survival data; varying-coefficient models...

  20. Modeling survival: application of the Andersen-Gill model to Yellowstone grizzly bears

    Science.gov (United States)

    Johnson, Christopher J.; Boyce, Mark S.; Schwartz, Charles C.; Haroldson, Mark A.

    2004-01-01

     Wildlife ecologists often use the Kaplan-Meier procedure or Cox proportional hazards model to estimate survival rates, distributions, and magnitude of risk factors. The Andersen-Gill formulation (A-G) of the Cox proportional hazards model has seen limited application to mark-resight data but has a number of advantages, including the ability to accommodate left-censored data, time-varying covariates, multiple events, and discontinuous intervals of risks. We introduce the A-G model including structure of data, interpretation of results, and assessment of assumptions. We then apply the model to 22 years of radiotelemetry data for grizzly bears (Ursus arctos) of the Greater Yellowstone Grizzly Bear Recovery Zone in Montana, Idaho, and Wyoming, USA. We used Akaike's Information Criterion (AICc) and multi-model inference to assess a number of potentially useful predictive models relative to explanatory covariates for demography, human disturbance, and habitat. Using the most parsimonious models, we generated risk ratios, hypothetical survival curves, and a map of the spatial distribution of high-risk areas across the recovery zone. Our results were in agreement with past studies of mortality factors for Yellowstone grizzly bears. Holding other covariates constant, mortality was highest for bears that were subjected to repeated management actions and inhabited areas with high road densities outside Yellowstone National Park. Hazard models developed with covariates descriptive of foraging habitats were not the most parsimonious, but they suggested that high-elevation areas offered lower risks of mortality when compared to agricultural areas.

  1. Effect of topically applied minoxidil on the survival of rat dorsal skin flap.

    Science.gov (United States)

    Gümüş, Nazım; Odemiş, Yusuf; Yılmaz, Sarper; Tuncer, Ersin

    2012-12-01

    Flap necrosis still is a challenging problem in reconstructive surgery that results in irreversible tissue loss. This study evaluated the effect of topically applied minoxidil on angiogenesis and survival of a caudally based dorsal rat skin flap. For this study, 24 male Wistar rats were randomly divided into three groups of eight each. A caudally based dorsal skin flap with the dimensions of 9 × 3 cm was raised. After elevation of the flaps, they were sutured back into their initial positions. In group 1 (control group), 1 ml of isotonic saline was applied topically to the flaps of all the animals for 14 days. In group 2, minoxidil solution was spread uniformly over the flap surface for 7 days after the flap elevation. In group 3, minoxidil solution was applied topically to the flap surface during a 14-day period. On day 7 after the flap elevation, the rats were killed. The average area of flap survival was determined for each rat. Subdermal vascular architecture and angiogenesis were evaluated under a light microscope after two full-thickness skin biopsy specimens had been obtained from the midline of the flaps. The lowest flap survival rate was observed in group 1, and no difference was observed between groups 1 and 2. Compared with groups 1 and 2, group 3 had a significantly increased percentage of flap survival (P minoxidil is vasodilation and that prolonged use before flap elevation leads to angiogenesis, increasing flap viability. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

  2. Predictive modelling of Lactobacillus casei KN291 survival in fermented soy beverage.

    Science.gov (United States)

    Zielińska, Dorota; Dorota, Zielińska; Kołożyn-Krajewska, Danuta; Danuta, Kołożyn-Krajewska; Goryl, Antoni; Antoni, Goryl; Motyl, Ilona

    2014-02-01

    The aim of the study was to construct and verify predictive growth and survival models of a potentially probiotic bacteria in fermented soy beverage. The research material included natural soy beverage (Polgrunt, Poland) and the strain of lactic acid bacteria (LAB) - Lactobacillus casei KN291. To construct predictive models for the growth and survival of L. casei KN291 bacteria in the fermented soy beverage we design an experiment which allowed the collection of CFU data. Fermented soy beverage samples were stored at various temperature conditions (5, 10, 15, and 20°C) for 28 days. On the basis of obtained data concerning the survival of L. casei KN291 bacteria in soy beverage at different temperature and time conditions, two non-linear models (r(2)= 0.68-0.93) and two surface models (r(2)=0.76-0.79) were constructed; these models described the behaviour of the bacteria in the product to a satisfactory extent. Verification of the surface models was carried out utilizing the validation data - at 7°C during 28 days. It was found that applied models were well fitted and charged with small systematic errors, which is evidenced by accuracy factor - Af, bias factor - Bf and mean squared error - MSE. The constructed microbiological growth and survival models of L. casei KN291 in fermented soy beverage enable the estimation of products shelf life period, which in this case is defined by the requirement for the level of the bacteria to be above 10(6) CFU/cm(3). The constructed models may be useful as a tool for the manufacture of probiotic foods to estimate of their shelf life period.

  3. Survivability Assessment: Modeling A Recovery Process

    OpenAIRE

    Paputungan, Irving Vitra; Abdullah, Azween

    2009-01-01

    Survivability is the ability of a system to continue operating, in a timely manner, in the presence ofattacks, failures, or accidents. Recovery in survivability is a process of a system to heal or recover from damageas early as possible to fulfill its mission as condition permit. In this paper, we show a preliminary recoverymodel to enhance the system survivability. The model focuses on how we preserve the system and resumes itscritical service under attacks as soon as possible.Keywords: surv...

  4. Comparison of hypertabastic survival model with other unimodal hazard rate functions using a goodness-of-fit test.

    Science.gov (United States)

    Tahir, M Ramzan; Tran, Quang X; Nikulin, Mikhail S

    2017-05-30

    We studied the problem of testing a hypothesized distribution in survival regression models when the data is right censored and survival times are influenced by covariates. A modified chi-squared type test, known as Nikulin-Rao-Robson statistic, is applied for the comparison of accelerated failure time models. This statistic is used to test the goodness-of-fit for hypertabastic survival model and four other unimodal hazard rate functions. The results of simulation study showed that the hypertabastic distribution can be used as an alternative to log-logistic and log-normal distribution. In statistical modeling, because of its flexible shape of hazard functions, this distribution can also be used as a competitor of Birnbaum-Saunders and inverse Gaussian distributions. The results for the real data application are shown. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  5. Extensions and Applications of the Cox-Aalen Survival Model

    DEFF Research Database (Denmark)

    Scheike, Thomas H.; Zhang, Mei-Jie

    2003-01-01

    Aalen additive risk model; competing risk; counting processes; Cox model; cumulative incidence function; goodness of fit; prediction of survival probability; time-varying effects......Aalen additive risk model; competing risk; counting processes; Cox model; cumulative incidence function; goodness of fit; prediction of survival probability; time-varying effects...

  6. Re-evaluating neonatal-age models for ungulates: does model choice affect survival estimates?

    Directory of Open Access Journals (Sweden)

    Troy W Grovenburg

    Full Text Available New-hoof growth is regarded as the most reliable metric for predicting age of newborn ungulates, but variation in estimated age among hoof-growth equations that have been developed may affect estimates of survival in staggered-entry models. We used known-age newborns to evaluate variation in age estimates among existing hoof-growth equations and to determine the consequences of that variation on survival estimates. During 2001-2009, we captured and radiocollared 174 newborn (≤24-hrs old ungulates: 76 white-tailed deer (Odocoileus virginianus in Minnesota and South Dakota, 61 mule deer (O. hemionus in California, and 37 pronghorn (Antilocapra americana in South Dakota. Estimated age of known-age newborns differed among hoof-growth models and varied by >15 days for white-tailed deer, >20 days for mule deer, and >10 days for pronghorn. Accuracy (i.e., the proportion of neonates assigned to the correct age in aging newborns using published equations ranged from 0.0% to 39.4% in white-tailed deer, 0.0% to 3.3% in mule deer, and was 0.0% for pronghorns. Results of survival modeling indicated that variability in estimates of age-at-capture affected short-term estimates of survival (i.e., 30 days for white-tailed deer and mule deer, and survival estimates over a longer time frame (i.e., 120 days for mule deer. Conversely, survival estimates for pronghorn were not affected by estimates of age. Our analyses indicate that modeling survival in daily intervals is too fine a temporal scale when age-at-capture is unknown given the potential inaccuracies among equations used to estimate age of neonates. Instead, weekly survival intervals are more appropriate because most models accurately predicted ages within 1 week of the known age. Variation among results of neonatal-age models on short- and long-term estimates of survival for known-age young emphasizes the importance of selecting an appropriate hoof-growth equation and appropriately defining intervals (i

  7. Developing a scalable modeling architecture for studying survivability technologies

    Science.gov (United States)

    Mohammad, Syed; Bounker, Paul; Mason, James; Brister, Jason; Shady, Dan; Tucker, David

    2006-05-01

    To facilitate interoperability of models in a scalable environment, and provide a relevant virtual environment in which Survivability technologies can be evaluated, the US Army Research Development and Engineering Command (RDECOM) Modeling Architecture for Technology Research and Experimentation (MATREX) Science and Technology Objective (STO) program has initiated the Survivability Thread which will seek to address some of the many technical and programmatic challenges associated with the effort. In coordination with different Thread customers, such as the Survivability branches of various Army labs, a collaborative group has been formed to define the requirements for the simulation environment that would in turn provide them a value-added tool for assessing models and gauge system-level performance relevant to Future Combat Systems (FCS) and the Survivability requirements of other burgeoning programs. An initial set of customer requirements has been generated in coordination with the RDECOM Survivability IPT lead, through the Survivability Technology Area at RDECOM Tank-automotive Research Development and Engineering Center (TARDEC, Warren, MI). The results of this project are aimed at a culminating experiment and demonstration scheduled for September, 2006, which will include a multitude of components from within RDECOM and provide the framework for future experiments to support Survivability research. This paper details the components with which the MATREX Survivability Thread was created and executed, and provides insight into the capabilities currently demanded by the Survivability faculty within RDECOM.

  8. Joint modelling of longitudinal CEA tumour marker progression and survival data on breast cancer

    Science.gov (United States)

    Borges, Ana; Sousa, Inês; Castro, Luis

    2017-06-01

    This work proposes the use of Biostatistics methods to study breast cancer in patients of Braga's Hospital Senology Unit, located in Portugal. The primary motivation is to contribute to the understanding of the progression of breast cancer, within the Portuguese population, using a more complex statistical model assumptions than the traditional analysis that take into account a possible existence of a serial correlation structure within a same subject observations. We aim to infer which risk factors aect the survival of Braga's Hospital patients, diagnosed with breast tumour. Whilst analysing risk factors that aect a tumour markers used on the surveillance of disease progression the Carcinoembryonic antigen (CEA). As survival and longitudinal processes may be associated, it is important to model these two processes together. Hence, a joint modelling of these two processes to infer on the association of these was conducted. A data set of 540 patients, along with 50 variables, was collected from medical records of the Hospital. A joint model approach was used to analyse these data. Two dierent joint models were applied to the same data set, with dierent parameterizations which give dierent interpretations to model parameters. These were used by convenience as the ones implemented in R software. Results from the two models were compared. Results from joint models, showed that the longitudinal CEA values were signicantly associated with the survival probability of these patients. A comparison between parameter estimates obtained in this analysis and previous independent survival[4] and longitudinal analysis[5][6], lead us to conclude that independent analysis brings up bias parameter estimates. Hence, an assumption of association between the two processes in a joint model of breast cancer data is necessary. Results indicate that the longitudinal progression of CEA is signicantly associated with the probability of survival of these patients. Hence, an assumption of

  9. Flexible Modeling of Survival Data with Covariates Subject to Detection Limits via Multiple Imputation.

    Science.gov (United States)

    Bernhardt, Paul W; Wang, Huixia Judy; Zhang, Daowen

    2014-01-01

    Models for survival data generally assume that covariates are fully observed. However, in medical studies it is not uncommon for biomarkers to be censored at known detection limits. A computationally-efficient multiple imputation procedure for modeling survival data with covariates subject to detection limits is proposed. This procedure is developed in the context of an accelerated failure time model with a flexible seminonparametric error distribution. The consistency and asymptotic normality of the multiple imputation estimator are established and a consistent variance estimator is provided. An iterative version of the proposed multiple imputation algorithm that approximates the EM algorithm for maximum likelihood is also suggested. Simulation studies demonstrate that the proposed multiple imputation methods work well while alternative methods lead to estimates that are either biased or more variable. The proposed methods are applied to analyze the dataset from a recently-conducted GenIMS study.

  10. Modeling time-to-event (survival) data using classification tree analysis.

    Science.gov (United States)

    Linden, Ariel; Yarnold, Paul R

    2017-12-01

    Time to the occurrence of an event is often studied in health research. Survival analysis differs from other designs in that follow-up times for individuals who do not experience the event by the end of the study (called censored) are accounted for in the analysis. Cox regression is the standard method for analysing censored data, but the assumptions required of these models are easily violated. In this paper, we introduce classification tree analysis (CTA) as a flexible alternative for modelling censored data. Classification tree analysis is a "decision-tree"-like classification model that provides parsimonious, transparent (ie, easy to visually display and interpret) decision rules that maximize predictive accuracy, derives exact P values via permutation tests, and evaluates model cross-generalizability. Using empirical data, we identify all statistically valid, reproducible, longitudinally consistent, and cross-generalizable CTA survival models and then compare their predictive accuracy to estimates derived via Cox regression and an unadjusted naïve model. Model performance is assessed using integrated Brier scores and a comparison between estimated survival curves. The Cox regression model best predicts average incidence of the outcome over time, whereas CTA survival models best predict either relatively high, or low, incidence of the outcome over time. Classification tree analysis survival models offer many advantages over Cox regression, such as explicit maximization of predictive accuracy, parsimony, statistical robustness, and transparency. Therefore, researchers interested in accurate prognoses and clear decision rules should consider developing models using the CTA-survival framework. © 2017 John Wiley & Sons, Ltd.

  11. Modelling survival: exposure pattern, species sensitivity and uncertainty.

    Science.gov (United States)

    Ashauer, Roman; Albert, Carlo; Augustine, Starrlight; Cedergreen, Nina; Charles, Sandrine; Ducrot, Virginie; Focks, Andreas; Gabsi, Faten; Gergs, André; Goussen, Benoit; Jager, Tjalling; Kramer, Nynke I; Nyman, Anna-Maija; Poulsen, Veronique; Reichenberger, Stefan; Schäfer, Ralf B; Van den Brink, Paul J; Veltman, Karin; Vogel, Sören; Zimmer, Elke I; Preuss, Thomas G

    2016-07-06

    The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans.

  12. Efficient estimation of semiparametric copula models for bivariate survival data

    KAUST Repository

    Cheng, Guang

    2014-01-01

    A semiparametric copula model for bivariate survival data is characterized by a parametric copula model of dependence and nonparametric models of two marginal survival functions. Efficient estimation for the semiparametric copula model has been recently studied for the complete data case. When the survival data are censored, semiparametric efficient estimation has only been considered for some specific copula models such as the Gaussian copulas. In this paper, we obtain the semiparametric efficiency bound and efficient estimation for general semiparametric copula models for possibly censored data. We construct an approximate maximum likelihood estimator by approximating the log baseline hazard functions with spline functions. We show that our estimates of the copula dependence parameter and the survival functions are asymptotically normal and efficient. Simple consistent covariance estimators are also provided. Numerical results are used to illustrate the finite sample performance of the proposed estimators. © 2013 Elsevier Inc.

  13. Causal inference for long-term survival in randomised trials with treatment switching: Should re-censoring be applied when estimating counterfactual survival times?

    OpenAIRE

    Latimer, N.R.; White, I.R.; Abrams, K.R.; Sieburt, U.

    2017-01-01

    Treatment switching often has a crucial impact on estimates of effectiveness and cost-effectiveness of new oncology treatments. Rank preserving structural failure time models (RPSFTM) and two-stage estimation (TSE) methods estimate ‘counterfactual’ (i.e. had there been no switching) survival times and incorporate re-censoring to guard against informative censoring in the counterfactual dataset. However, re-censoring causes a loss of longer term survival information which is problematic when e...

  14. Modelling survival after treatment of intraocular melanoma using artificial neural networks and Bayes theorem

    International Nuclear Information System (INIS)

    Taktak, Azzam F G; Fisher, Anthony C; Damato, Bertil E

    2004-01-01

    This paper describes the development of an artificial intelligence (AI) system for survival prediction from intraocular melanoma. The system used artificial neural networks (ANNs) with five input parameters: coronal and sagittal tumour location, anterior tumour margin, largest basal tumour diameter and the cell type. After excluding records with missing data, 2331 patients were included in the study. These were split randomly into training and test sets. Date censorship was applied to the records to deal with patients who were lost to follow-up and patients who died from general causes. Bayes theorem was then applied to the ANN output to construct survival probability curves. A validation set with 34 patients unseen to both training and test sets was used to compare the AI system with Cox's regression (CR) and Kaplan-Meier (KM) analyses. Results showed large differences in the mean 5 year survival probability figures when the number of records with matching characteristics was small. However, as the number of matches increased to >100 the system tended to agree with CR and KM. The validation set was also used to compare the system with a clinical expert in predicting time to metastatic death. The rms error was 3.7 years for the system and 4.3 years for the clinical expert for 15 years survival. For <10 years survival, these figures were 2.7 and 4.2, respectively. We concluded that the AI system can match if not better the clinical expert's prediction. There were significant differences with CR and KM analyses when the number of records was small, but it was not known which model is more accurate

  15. Modelling survival after treatment of intraocular melanoma using artificial neural networks and Bayes theorem

    Energy Technology Data Exchange (ETDEWEB)

    Taktak, Azzam F G [Department of Clinical Engineering, Duncan Building, Royal Liverpool University Hospital, Liverpool L7 8XP (United Kingdom); Fisher, Anthony C [Department of Clinical Engineering, Duncan Building, Royal Liverpool University Hospital, Liverpool L7 8XP (United Kingdom); Damato, Bertil E [Department of Ophthalmology, Royal Liverpool University Hospital, Liverpool L7 8XP (United Kingdom)

    2004-01-07

    This paper describes the development of an artificial intelligence (AI) system for survival prediction from intraocular melanoma. The system used artificial neural networks (ANNs) with five input parameters: coronal and sagittal tumour location, anterior tumour margin, largest basal tumour diameter and the cell type. After excluding records with missing data, 2331 patients were included in the study. These were split randomly into training and test sets. Date censorship was applied to the records to deal with patients who were lost to follow-up and patients who died from general causes. Bayes theorem was then applied to the ANN output to construct survival probability curves. A validation set with 34 patients unseen to both training and test sets was used to compare the AI system with Cox's regression (CR) and Kaplan-Meier (KM) analyses. Results showed large differences in the mean 5 year survival probability figures when the number of records with matching characteristics was small. However, as the number of matches increased to >100 the system tended to agree with CR and KM. The validation set was also used to compare the system with a clinical expert in predicting time to metastatic death. The rms error was 3.7 years for the system and 4.3 years for the clinical expert for 15 years survival. For <10 years survival, these figures were 2.7 and 4.2, respectively. We concluded that the AI system can match if not better the clinical expert's prediction. There were significant differences with CR and KM analyses when the number of records was small, but it was not known which model is more accurate.

  16. Partitioning of excess mortality in population-based cancer patient survival studies using flexible parametric survival models

    Directory of Open Access Journals (Sweden)

    Eloranta Sandra

    2012-06-01

    Full Text Available Abstract Background Relative survival is commonly used for studying survival of cancer patients as it captures both the direct and indirect contribution of a cancer diagnosis on mortality by comparing the observed survival of the patients to the expected survival in a comparable cancer-free population. However, existing methods do not allow estimation of the impact of isolated conditions (e.g., excess cardiovascular mortality on the total excess mortality. For this purpose we extend flexible parametric survival models for relative survival, which use restricted cubic splines for the baseline cumulative excess hazard and for any time-dependent effects. Methods In the extended model we partition the excess mortality associated with a diagnosis of cancer through estimating a separate baseline excess hazard function for the outcomes under investigation. This is done by incorporating mutually exclusive background mortality rates, stratified by the underlying causes of death reported in the Swedish population, and by introducing cause of death as a time-dependent effect in the extended model. This approach thereby enables modeling of temporal trends in e.g., excess cardiovascular mortality and remaining cancer excess mortality simultaneously. Furthermore, we illustrate how the results from the proposed model can be used to derive crude probabilities of death due to the component parts, i.e., probabilities estimated in the presence of competing causes of death. Results The method is illustrated with examples where the total excess mortality experienced by patients diagnosed with breast cancer is partitioned into excess cardiovascular mortality and remaining cancer excess mortality. Conclusions The proposed method can be used to simultaneously study disease patterns and temporal trends for various causes of cancer-consequent deaths. Such information should be of interest for patients and clinicians as one way of improving prognosis after cancer is

  17. Survival prediction model for postoperative hepatocellular carcinoma patients.

    Science.gov (United States)

    Ren, Zhihui; He, Shasha; Fan, Xiaotang; He, Fangping; Sang, Wei; Bao, Yongxing; Ren, Weixin; Zhao, Jinming; Ji, Xuewen; Wen, Hao

    2017-09-01

    This study is to establish a predictive index (PI) model of 5-year survival rate for patients with hepatocellular carcinoma (HCC) after radical resection and to evaluate its prediction sensitivity, specificity, and accuracy.Patients underwent HCC surgical resection were enrolled and randomly divided into prediction model group (101 patients) and model evaluation group (100 patients). Cox regression model was used for univariate and multivariate survival analysis. A PI model was established based on multivariate analysis and receiver operating characteristic (ROC) curve was drawn accordingly. The area under ROC (AUROC) and PI cutoff value was identified.Multiple Cox regression analysis of prediction model group showed that neutrophil to lymphocyte ratio, histological grade, microvascular invasion, positive resection margin, number of tumor, and postoperative transcatheter arterial chemoembolization treatment were the independent predictors for the 5-year survival rate for HCC patients. The model was PI = 0.377 × NLR + 0.554 × HG + 0.927 × PRM + 0.778 × MVI + 0.740 × NT - 0.831 × transcatheter arterial chemoembolization (TACE). In the prediction model group, AUROC was 0.832 and the PI cutoff value was 3.38. The sensitivity, specificity, and accuracy were 78.0%, 80%, and 79.2%, respectively. In model evaluation group, AUROC was 0.822, and the PI cutoff value was well corresponded to the prediction model group with sensitivity, specificity, and accuracy of 85.0%, 83.3%, and 84.0%, respectively.The PI model can quantify the mortality risk of hepatitis B related HCC with high sensitivity, specificity, and accuracy.

  18. Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data

    Directory of Open Access Journals (Sweden)

    Raftery Adrian E

    2009-02-01

    Full Text Available Abstract Background Microarray technology is increasingly used to identify potential biomarkers for cancer prognostics and diagnostics. Previously, we have developed the iterative Bayesian Model Averaging (BMA algorithm for use in classification. Here, we extend the iterative BMA algorithm for application to survival analysis on high-dimensional microarray data. The main goal in applying survival analysis to microarray data is to determine a highly predictive model of patients' time to event (such as death, relapse, or metastasis using a small number of selected genes. Our multivariate procedure combines the effectiveness of multiple contending models by calculating the weighted average of their posterior probability distributions. Our results demonstrate that our iterative BMA algorithm for survival analysis achieves high prediction accuracy while consistently selecting a small and cost-effective number of predictor genes. Results We applied the iterative BMA algorithm to two cancer datasets: breast cancer and diffuse large B-cell lymphoma (DLBCL data. On the breast cancer data, the algorithm selected a total of 15 predictor genes across 84 contending models from the training data. The maximum likelihood estimates of the selected genes and the posterior probabilities of the selected models from the training data were used to divide patients in the test (or validation dataset into high- and low-risk categories. Using the genes and models determined from the training data, we assigned patients from the test data into highly distinct risk groups (as indicated by a p-value of 7.26e-05 from the log-rank test. Moreover, we achieved comparable results using only the 5 top selected genes with 100% posterior probabilities. On the DLBCL data, our iterative BMA procedure selected a total of 25 genes across 3 contending models from the training data. Once again, we assigned the patients in the validation set to significantly distinct risk groups (p

  19. Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science.

    Science.gov (United States)

    Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

    2016-01-01

    One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.

  20. Estimating the average treatment effect on survival based on observational data and using partly conditional modeling.

    Science.gov (United States)

    Gong, Qi; Schaubel, Douglas E

    2017-03-01

    Treatments are frequently evaluated in terms of their effect on patient survival. In settings where randomization of treatment is not feasible, observational data are employed, necessitating correction for covariate imbalances. Treatments are usually compared using a hazard ratio. Most existing methods which quantify the treatment effect through the survival function are applicable to treatments assigned at time 0. In the data structure of our interest, subjects typically begin follow-up untreated; time-until-treatment, and the pretreatment death hazard are both heavily influenced by longitudinal covariates; and subjects may experience periods of treatment ineligibility. We propose semiparametric methods for estimating the average difference in restricted mean survival time attributable to a time-dependent treatment, the average effect of treatment among the treated, under current treatment assignment patterns. The pre- and posttreatment models are partly conditional, in that they use the covariate history up to the time of treatment. The pre-treatment model is estimated through recently developed landmark analysis methods. For each treated patient, fitted pre- and posttreatment survival curves are projected out, then averaged in a manner which accounts for the censoring of treatment times. Asymptotic properties are derived and evaluated through simulation. The proposed methods are applied to liver transplant data in order to estimate the effect of liver transplantation on survival among transplant recipients under current practice patterns. © 2016, The International Biometric Society.

  1. Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization.

    Science.gov (United States)

    Liang, Yong; Chai, Hua; Liu, Xiao-Ying; Xu, Zong-Ben; Zhang, Hai; Leung, Kwong-Sak

    2016-03-01

    One of the most important objectives of the clinical cancer research is to diagnose cancer more accurately based on the patients' gene expression profiles. Both Cox proportional hazards model (Cox) and accelerated failure time model (AFT) have been widely adopted to the high risk and low risk classification or survival time prediction for the patients' clinical treatment. Nevertheless, two main dilemmas limit the accuracy of these prediction methods. One is that the small sample size and censored data remain a bottleneck for training robust and accurate Cox classification model. In addition to that, similar phenotype tumours and prognoses are actually completely different diseases at the genotype and molecular level. Thus, the utility of the AFT model for the survival time prediction is limited when such biological differences of the diseases have not been previously identified. To try to overcome these two main dilemmas, we proposed a novel semi-supervised learning method based on the Cox and AFT models to accurately predict the treatment risk and the survival time of the patients. Moreover, we adopted the efficient L1/2 regularization approach in the semi-supervised learning method to select the relevant genes, which are significantly associated with the disease. The results of the simulation experiments show that the semi-supervised learning model can significant improve the predictive performance of Cox and AFT models in survival analysis. The proposed procedures have been successfully applied to four real microarray gene expression and artificial evaluation datasets. The advantages of our proposed semi-supervised learning method include: 1) significantly increase the available training samples from censored data; 2) high capability for identifying the survival risk classes of patient in Cox model; 3) high predictive accuracy for patients' survival time in AFT model; 4) strong capability of the relevant biomarker selection. Consequently, our proposed semi

  2. A survivability model for ejection of green compacts in powder metallurgy technology

    Directory of Open Access Journals (Sweden)

    Payman Ahi

    2012-01-01

    Full Text Available Reliability and quality assurance have become major considerations in the design and manufacture of today’s parts and products. Survivability of green compact using powder metallurgy technology is considered as one of the major quality attributes in manufacturing systems today. During powder metallurgy (PM production, the compaction conditions and behavior of the metal powder dictate the stress and density distribution in the green compact prior to sintering. These parameters greatly influence the mechanical properties and overall strength of the final component. In order to improve these properties, higher compaction pressures are usually employed, which make unloading and ejection of green compacts more challenging, especially for the powder-compacted parts with relatively complicated shapes. This study looked at a mathematical survivability model concerning green compact characteristics in PM technology and the stress-strength failure model in reliability engineering. This model depicts the relationship between mechanical loads (stress during ejection, experimentally determined green strength and survivability of green compact. The resulting survivability is the probability that a green compact survives during and after ejection. This survivability model can be used as an efficient tool for selecting the appropriate parameters for the process planning stage in PM technology. A case study is presented here in order to demonstrate the application of the proposed survivability model.

  3. SEMI-COMPETING RISKS ON A TRIVARIATE WEIBULL SURVIVAL MODEL

    Directory of Open Access Journals (Sweden)

    Jenq-Daw Lee

    2008-07-01

    Full Text Available A setting of a trivairate survival function using semi-competing risks concept is proposed, in which a terminal event can only occur after other events. The Stanford Heart Transplant data is reanalyzed using a trivariate Weibull distribution model with the proposed survival function.

  4. Machine learning models in breast cancer survival prediction.

    Science.gov (United States)

    Montazeri, Mitra; Montazeri, Mohadeseh; Montazeri, Mahdieh; Beigzadeh, Amin

    2016-01-01

    Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets. The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival. We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve. Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%). This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of

  5. Discrete dynamic modeling of T cell survival signaling networks

    Science.gov (United States)

    Zhang, Ranran

    2009-03-01

    Biochemistry-based frameworks are often not applicable for the modeling of heterogeneous regulatory systems that are sparsely documented in terms of quantitative information. As an alternative, qualitative models assuming a small set of discrete states are gaining acceptance. This talk will present a discrete dynamic model of the signaling network responsible for the survival and long-term competence of cytotoxic T cells in the blood cancer T-LGL leukemia. We integrated the signaling pathways involved in normal T cell activation and the known deregulations of survival signaling in leukemic T-LGL, and formulated the regulation of each network element as a Boolean (logic) rule. Our model suggests that the persistence of two signals is sufficient to reproduce all known deregulations in leukemic T-LGL. It also indicates the nodes whose inactivity is necessary and sufficient for the reversal of the T-LGL state. We have experimentally validated several model predictions, including: (i) Inhibiting PDGF signaling induces apoptosis in leukemic T-LGL. (ii) Sphingosine kinase 1 and NFκB are essential for the long-term survival of T cells in T-LGL leukemia. (iii) T box expressed in T cells (T-bet) is constitutively activated in the T-LGL state. The model has identified potential therapeutic targets for T-LGL leukemia and can be used for generating long-term competent CTL necessary for tumor and cancer vaccine development. The success of this model, and of other discrete dynamic models, suggests that the organization of signaling networks has an determining role in their dynamics. Reference: R. Zhang, M. V. Shah, J. Yang, S. B. Nyland, X. Liu, J. K. Yun, R. Albert, T. P. Loughran, Jr., Network Model of Survival Signaling in LGL Leukemia, PNAS 105, 16308-16313 (2008).

  6. Ensemble of cell survival experiments after ion irradiation for validation of RBE models

    Energy Technology Data Exchange (ETDEWEB)

    Friedrich, Thomas; Scholz, Uwe; Scholz, Michael [GSI Helmholtzzentrum fuer Schwerionenforschung, Darmstadt (Germany); Durante, Marco [GSI Helmholtzzentrum fuer Schwerionenforschung, Darmstadt (Germany); Institut fuer Festkoerperphysik, TU Darmstadt, Darmstadt (Germany)

    2012-07-01

    There is persistent interest in understanding the systematics of the relative biological effectiveness (RBE). Models such as the Local Effect Model (LEM) or the Microdosimetric Kinetic Model have the goal to predict the RBE. For the validation of these models a collection of many in-vitro cell survival experiments is most appropriate. The set-up of an ensemble of in-vitro cell survival data comprising about 850 survival experiments after both ion and photon irradiation is reported. The survival curves have been taken out from publications. The experiments encompass survival curves obtained in different labs, using different ion species from protons to uranium, varying irradiation modalities (shaped or monoenergetic beam), various energies and linear energy transfers, and a whole variety of cell types (human or rodent; normal, mutagenic or tumor; radioresistant or -sensitive). Each cell survival curve has been parameterized by the linear-quadratic model. The photon parameters have been added to the data base to allow to calculate the experimental RBE to any survival level. We report on experimental trends found within the data ensemble. The data will serve as a testing ground for RBE models such as the LEM. Finally, a roadmap for further validation and first model results using the data base in combination with the LEM are presented.

  7. Dose-rate dependent stochastic effects in radiation cell-survival models

    International Nuclear Information System (INIS)

    Sachs, R.K.; Hlatky, L.R.

    1990-01-01

    When cells are subjected to ionizing radiation the specific energy rate (microscopic analog of dose-rate) varies from cell to cell. Within one cell, this rate fluctuates during the course of time; a crossing of a sensitive cellular site by a high energy charged particle produces many ionizations almost simultaneously, but during the interval between events no ionizations occur. In any cell-survival model one can incorporate the effect of such fluctuations without changing the basic biological assumptions. Using stochastic differential equations and Monte Carlo methods to take into account stochastic effects we calculated the dose-survival rfelationships in a number of current cell survival models. Some of the models assume quadratic misrepair; others assume saturable repair enzyme systems. It was found that a significant effect of random fluctuations is to decrease the theoretically predicted amount of dose-rate sparing. In the limit of low dose-rates neglecting the stochastic nature of specific energy rates often leads to qualitatively misleading results by overestimating the surviving fraction drastically. In the opposite limit of acute irradiation, analyzing the fluctuations in rates merely amounts to analyzing fluctuations in total specific energy via the usual microdosimetric specific energy distribution function, and neglecting fluctuations usually underestimates the surviving fraction. The Monte Carlo methods interpolate systematically between the low dose-rate and high dose-rate limits. As in other approaches, the slope of the survival curve at low dose-rates is virtually independent of dose and equals the initial slope of the survival curve for acute radiation. (orig.)

  8. Predicting water main failures using Bayesian model averaging and survival modelling approach

    International Nuclear Information System (INIS)

    Kabir, Golam; Tesfamariam, Solomon; Sadiq, Rehan

    2015-01-01

    To develop an effective preventive or proactive repair and replacement action plan, water utilities often rely on water main failure prediction models. However, in predicting the failure of water mains, uncertainty is inherent regardless of the quality and quantity of data used in the model. To improve the understanding of water main failure, a Bayesian framework is developed for predicting the failure of water mains considering uncertainties. In this study, Bayesian model averaging method (BMA) is presented to identify the influential pipe-dependent and time-dependent covariates considering model uncertainties whereas Bayesian Weibull Proportional Hazard Model (BWPHM) is applied to develop the survival curves and to predict the failure rates of water mains. To accredit the proposed framework, it is implemented to predict the failure of cast iron (CI) and ductile iron (DI) pipes of the water distribution network of the City of Calgary, Alberta, Canada. Results indicate that the predicted 95% uncertainty bounds of the proposed BWPHMs capture effectively the observed breaks for both CI and DI water mains. Moreover, the performance of the proposed BWPHMs are better compare to the Cox-Proportional Hazard Model (Cox-PHM) for considering Weibull distribution for the baseline hazard function and model uncertainties. - Highlights: • Prioritize rehabilitation and replacements (R/R) strategies of water mains. • Consider the uncertainties for the failure prediction. • Improve the prediction capability of the water mains failure models. • Identify the influential and appropriate covariates for different models. • Determine the effects of the covariates on failure

  9. Statistical study of clone survival curves after irradiation in one or two stages. Comparison and generalization of different models

    International Nuclear Information System (INIS)

    Lachet, Bernard.

    1975-01-01

    A statistical study was carried out on 208 survival curves for chlorella subjected to γ or particle radiations. The computing programmes used were written in Fortran. The different experimental causes contributing to the variance of a survival rate are analyzed and consequently the experiments can be planned. Each curve was fitted to four models by the weighted least squares method applied to non-linear functions. The validity of the fits obtained can be checked by the F test. It was possible to define the confidence and prediction zones around an adjusted curve by weighting of the residual variance, in spite of error on the doses delivered; the confidence limits can them be fixed for a dose estimated from an exact or measured survival. The four models adopted were compared for the precision of their fit (by a non-parametric simultaneous comparison test) and the scattering of their adjusted parameters: Wideroe's model gives a very good fit with the experimental points in return for a scattering of its parameters, which robs them of their presumed meaning. The principal component analysis showed the statistical equivalence of the 1 and 2 hit target models. Division of the irradiation into two doses, the first fixed by the investigator, leads to families of curves for which the equation was established from that of any basic model expressing the dose survival relationship in one-stage irradiation [fr

  10. Statistical models and methods for reliability and survival analysis

    CERN Document Server

    Couallier, Vincent; Huber-Carol, Catherine; Mesbah, Mounir; Huber -Carol, Catherine; Limnios, Nikolaos; Gerville-Reache, Leo

    2013-01-01

    Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Statistical

  11. Assessing survivability to support power grid investment decisions

    International Nuclear Information System (INIS)

    Koziolek, Anne; Avritzer, Alberto; Suresh, Sindhu; Menasché, Daniel S.; Diniz, Morganna; Souza e Silva, Edmundo de; Leão, Rosa M.; Trivedi, Kishor; Happe, Lucia

    2016-01-01

    The reliability of power grids has been subject of study for the past few decades. Traditionally, detailed models are used to assess how the system behaves after failures. Such models, based on power flow analysis and detailed simulations, yield accurate characterizations of the system under study. However, they fall short on scalability. In this paper, we propose an efficient and scalable approach to assess the survivability of power systems. Our approach takes into account the phased-recovery of the system after a failure occurs. The proposed phased-recovery model yields metrics such as the expected accumulated energy not supplied between failure and full recovery. Leveraging the predictive power of the model, we use it as part of an optimization framework to assist in investment decisions. Given a budget and an initial circuit to be upgraded, we propose heuristics to sample the solution space in a principled way accounting for survivability-related metrics. We have evaluated the feasibility of this approach by applying it to the design of a benchmark distribution automation circuit. Our empirical results indicate that the combination of survivability and power flow analysis can provide meaningful investment decision support for power systems engineers. - Highlights: • We propose metrics and models for scalable survivability analysis of power systems. • The survivability model captures the system phased-recovery, from failure to repair. • The survivability model is used as a building block of an optimization framework. • Heuristics assist in investment options accounting for survivability-related metrics.

  12. Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic.

    Science.gov (United States)

    Wang, Ming; Long, Qi

    2016-09-01

    Prediction models for disease risk and prognosis play an important role in biomedical research, and evaluating their predictive accuracy in the presence of censored data is of substantial interest. The standard concordance (c) statistic has been extended to provide a summary measure of predictive accuracy for survival models. Motivated by a prostate cancer study, we address several issues associated with evaluating survival prediction models based on c-statistic with a focus on estimators using the technique of inverse probability of censoring weighting (IPCW). Compared to the existing work, we provide complete results on the asymptotic properties of the IPCW estimators under the assumption of coarsening at random (CAR), and propose a sensitivity analysis under the mechanism of noncoarsening at random (NCAR). In addition, we extend the IPCW approach as well as the sensitivity analysis to high-dimensional settings. The predictive accuracy of prediction models for cancer recurrence after prostatectomy is assessed by applying the proposed approaches. We find that the estimated predictive accuracy for the models in consideration is sensitive to NCAR assumption, and thus identify the best predictive model. Finally, we further evaluate the performance of the proposed methods in both settings of low-dimensional and high-dimensional data under CAR and NCAR through simulations. © 2016, The International Biometric Society.

  13. Estimating true instead of apparent survival using spatial Cormack-Jolly-Seber models

    Science.gov (United States)

    Schaub, Michael; Royle, J. Andrew

    2014-01-01

    Survival is often estimated from capture–recapture data using Cormack–Jolly–Seber (CJS) models, where mortality and emigration cannot be distinguished, and the estimated apparent survival probability is the product of the probabilities of true survival and of study area fidelity. Consequently, apparent survival is lower than true survival unless study area fidelity equals one. Underestimation of true survival from capture–recapture data is a main limitation of the method.

  14. Using cure models for analyzing the influence of pathogens on salmon survival

    Science.gov (United States)

    Ray, Adam R; Perry, Russell W.; Som, Nicholas A.; Bartholomew, Jerri L

    2014-01-01

    Parasites and pathogens influence the size and stability of wildlife populations, yet many population models ignore the population-level effects of pathogens. Standard survival analysis methods (e.g., accelerated failure time models) are used to assess how survival rates are influenced by disease. However, they assume that each individual is equally susceptible and will eventually experience the event of interest; this assumption is not typically satisfied with regard to pathogens of wildlife populations. In contrast, mixture cure models, which comprise logistic regression and survival analysis components, allow for different covariates to be entered into each part of the model and provide better predictions of survival when a fraction of the population is expected to survive a disease outbreak. We fitted mixture cure models to the host–pathogen dynamics of Chinook Salmon Oncorhynchus tshawytscha and Coho Salmon O. kisutch and the myxozoan parasite Ceratomyxa shasta. Total parasite concentration, water temperature, and discharge were used as covariates to predict the observed parasite-induced mortality in juvenile salmonids collected as part of a long-term monitoring program in the Klamath River, California. The mixture cure models predicted the observed total mortality well, but some of the variability in observed mortality rates was not captured by the models. Parasite concentration and water temperature were positively associated with total mortality and the mortality rate of both Chinook Salmon and Coho Salmon. Discharge was positively associated with total mortality for both species but only affected the mortality rate for Coho Salmon. The mixture cure models provide insights into how daily survival rates change over time in Chinook Salmon and Coho Salmon after they become infected with C. shasta.

  15. Neyman, Markov processes and survival analysis.

    Science.gov (United States)

    Yang, Grace

    2013-07-01

    J. Neyman used stochastic processes extensively in his applied work. One example is the Fix and Neyman (F-N) competing risks model (1951) that uses finite homogeneous Markov processes to analyse clinical trials with breast cancer patients. We revisit the F-N model, and compare it with the Kaplan-Meier (K-M) formulation for right censored data. The comparison offers a way to generalize the K-M formulation to include risks of recovery and relapses in the calculation of a patient's survival probability. The generalization is to extend the F-N model to a nonhomogeneous Markov process. Closed-form solutions of the survival probability are available in special cases of the nonhomogeneous processes, like the popular multiple decrement model (including the K-M model) and Chiang's staging model, but these models do not consider recovery and relapses while the F-N model does. An analysis of sero-epidemiology current status data with recurrent events is illustrated. Fix and Neyman used Neyman's RBAN (regular best asymptotic normal) estimates for the risks, and provided a numerical example showing the importance of considering both the survival probability and the length of time of a patient living a normal life in the evaluation of clinical trials. The said extension would result in a complicated model and it is unlikely to find analytical closed-form solutions for survival analysis. With ever increasing computing power, numerical methods offer a viable way of investigating the problem.

  16. Estimation of direct effects for survival data by using the Aalen additive hazards model

    DEFF Research Database (Denmark)

    Martinussen, Torben; Vansteelandt, Stijn; Gerster, Mette

    2011-01-01

    We extend the definition of the controlled direct effect of a point exposure on a survival outcome, other than through some given, time-fixed intermediate variable, to the additive hazard scale. We propose two-stage estimators for this effect when the exposure is dichotomous and randomly assigned...... Aalen's additive regression for the event time, given exposure, intermediate variable and confounders. The second stage involves applying Aalen's additive model, given the exposure alone, to a modified stochastic process (i.e. a modification of the observed counting process based on the first...

  17. Estimation of age- and stage-specific Catalan breast cancer survival functions using US and Catalan survival data

    Science.gov (United States)

    2009-01-01

    Background During the last part of the 1990s the chance of surviving breast cancer increased. Changes in survival functions reflect a mixture of effects. Both, the introduction of adjuvant treatments and early screening with mammography played a role in the decline in mortality. Evaluating the contribution of these interventions using mathematical models requires survival functions before and after their introduction. Furthermore, required survival functions may be different by age groups and are related to disease stage at diagnosis. Sometimes detailed information is not available, as was the case for the region of Catalonia (Spain). Then one may derive the functions using information from other geographical areas. This work presents the methodology used to estimate age- and stage-specific Catalan breast cancer survival functions from scarce Catalan survival data by adapting the age- and stage-specific US functions. Methods Cubic splines were used to smooth data and obtain continuous hazard rate functions. After, we fitted a Poisson model to derive hazard ratios. The model included time as a covariate. Then the hazard ratios were applied to US survival functions detailed by age and stage to obtain Catalan estimations. Results We started estimating the hazard ratios for Catalonia versus the USA before and after the introduction of screening. The hazard ratios were then multiplied by the age- and stage-specific breast cancer hazard rates from the USA to obtain the Catalan hazard rates. We also compared breast cancer survival in Catalonia and the USA in two time periods, before cancer control interventions (USA 1975–79, Catalonia 1980–89) and after (USA and Catalonia 1990–2001). Survival in Catalonia in the 1980–89 period was worse than in the USA during 1975–79, but the differences disappeared in 1990–2001. Conclusion Our results suggest that access to better treatments and quality of care contributed to large improvements in survival in Catalonia. On

  18. Malicious Botnet Survivability Mechanism Evolution Forecasting by Means of a Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Nikolaj Goranin

    2012-04-01

    Full Text Available Botnets are considered to be among the most dangerous modern malware types and the biggest current threats to global IT infrastructure. Botnets are rapidly evolving, and therefore forecasting their survivability strategies is important for the development of countermeasure techniques. The article propose the botnet-oriented genetic algorithm based model framework, which aimed at forecasting botnet survivability mechanisms. The model may be used as a framework for forecasting the evolution of other characteristics. The efficiency of different survivability mechanisms is evaluated by applying the proposed fitness function. The model application area also covers scientific botnet research and modelling tasks.

  19. Prognostic Factors for Survival in Patients with Gastric Cancer using a Random Survival Forest

    Science.gov (United States)

    Adham, Davoud; Abbasgholizadeh, Nategh; Abazari, Malek

    2017-01-01

    Background: Gastric cancer is the fifth most common cancer and the third top cause of cancer related death with about 1 million new cases and 700,000 deaths in 2012. The aim of this investigation was to identify important factors for outcome using a random survival forest (RSF) approach. Materials and Methods: Data were collected from 128 gastric cancer patients through a historical cohort study in Hamedan-Iran from 2007 to 2013. The event under consideration was death due to gastric cancer. The random survival forest model in R software was applied to determine the key factors affecting survival. Four split criteria were used to determine importance of the variables in the model including log-rank, conversation?? of events, log-rank score, and randomization. Efficiency of the model was confirmed in terms of Harrell’s concordance index. Results: The mean age of diagnosis was 63 ±12.57 and mean and median survival times were 15.2 (95%CI: 13.3, 17.0) and 12.3 (95%CI: 11.0, 13.4) months, respectively. The one-year, two-year, and three-year rates for survival were 51%, 13%, and 5%, respectively. Each RSF approach showed a slightly different ranking order. Very important covariates in nearly all the 4 RSF approaches were metastatic status, age at diagnosis and tumor size. The performance of each RSF approach was in the range of 0.29-0.32 and the best error rate was obtained by the log-rank splitting rule; second, third, and fourth ranks were log-rank score, conservation of events, and the random splitting rule, respectively. Conclusion: Low survival rate of gastric cancer patients is an indication of absence of a screening program for early diagnosis of the disease. Timely diagnosis in early phases increases survival and decreases mortality. Creative Commons Attribution License

  20. Comparison of Cox and Gray's survival models in severe sepsis

    DEFF Research Database (Denmark)

    Kasal, Jan; Andersen, Zorana Jovanovic; Clermont, Gilles

    2004-01-01

    Although survival is traditionally modeled using Cox proportional hazards modeling, this approach may be inappropriate in sepsis, in which the proportional hazards assumption does not hold. Newer, more flexible models, such as Gray's model, may be more appropriate....

  1. Estimation and model selection of semiparametric multivariate survival functions under general censorship.

    Science.gov (United States)

    Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

    2010-07-01

    We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root- n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided.

  2. Joint survival probability via truncated invariant copula

    International Nuclear Information System (INIS)

    Kim, Jeong-Hoon; Ma, Yong-Ki; Park, Chan Yeol

    2016-01-01

    Highlights: • We have studied an issue of dependence structure between default intensities. • We use a multivariate shot noise intensity process, where jumps occur simultaneously and their sizes are correlated. • We obtain the joint survival probability of the integrated intensities by using a copula. • We apply our theoretical result to pricing basket default swap spread. - Abstract: Given an intensity-based credit risk model, this paper studies dependence structure between default intensities. To model this structure, we use a multivariate shot noise intensity process, where jumps occur simultaneously and their sizes are correlated. Through very lengthy algebra, we obtain explicitly the joint survival probability of the integrated intensities by using the truncated invariant Farlie–Gumbel–Morgenstern copula with exponential marginal distributions. We also apply our theoretical result to pricing basket default swap spreads. This result can provide a useful guide for credit risk management.

  3. Experiential Strategies for the Survival of Small Cities in Europe

    DEFF Research Database (Denmark)

    Allingham, Peter

    2009-01-01

    The aim of the article is to analyse, discuss and evaluate different methods of branding applied in experiential strategies for the survival of small cities in Europe. After the introduction that refers to the advent of the experience economy in the post-Fordist era, the article introduces various...... branding methods applied in experiential strategies. Then follows an analysis of how these branding methods are applied in experiential strategies for the development and survival of two small cities in Germany, Dresden and Wolfsburg, in which car production and city development have been combined....... The article concludes with an evaluation of the branding methods, which includes considerations of whether they can be used as models of survival for other small European cities. The evaluation refers to recent views on the question of representation and authenticity, and the role of cultural heritage...

  4. Analysis of survival data with dependent censoring copula-based approaches

    CERN Document Server

    Emura, Takeshi

    2018-01-01

    This book introduces readers to copula-based statistical methods for analyzing survival data involving dependent censoring. Primarily focusing on likelihood-based methods performed under copula models, it is the first book solely devoted to the problem of dependent censoring. The book demonstrates the advantages of the copula-based methods in the context of medical research, especially with regard to cancer patients’ survival data. Needless to say, the statistical methods presented here can also be applied to many other branches of science, especially in reliability, where survival analysis plays an important role. The book can be used as a textbook for graduate coursework or a short course aimed at (bio-) statisticians. To deepen readers’ understanding of copula-based approaches, the book provides an accessible introduction to basic survival analysis and explains the mathematical foundations of copula-based survival models.

  5. SU-E-T-131: Artificial Neural Networks Applied to Overall Survival Prediction for Patients with Periampullary Carcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Gong, Y; Yu, J; Yeung, V; Palmer, J; Yu, Y; Lu, B; Babinsky, L; Burkhart, R; Leiby, B; Siow, V; Lavu, H; Rosato, E; Winter, J; Lewis, N; Sama, A; Mitchell, E; Anne, P; Hurwitz, M; Yeo, C; Bar-Ad, V [Thomas Jefferson University Hospital, Philadelphia, PA (United States); and others

    2015-06-15

    Purpose: Artificial neural networks (ANN) can be used to discover complex relations within datasets to help with medical decision making. This study aimed to develop an ANN method to predict two-year overall survival of patients with peri-ampullary cancer (PAC) following resection. Methods: Data were collected from 334 patients with PAC following resection treated in our institutional pancreatic tumor registry between 2006 and 2012. The dataset contains 14 variables including age, gender, T-stage, tumor differentiation, positive-lymph-node ratio, positive resection margins, chemotherapy, radiation therapy, and tumor histology.After censoring for two-year survival analysis, 309 patients were left, of which 44 patients (∼15%) were randomly selected to form testing set. The remaining 265 cases were randomly divided into training set (211 cases, ∼80% of 265) and validation set (54 cases, ∼20% of 265) for 20 times to build 20 ANN models. Each ANN has one hidden layer with 5 units. The 20 ANN models were ranked according to their concordance index (c-index) of prediction on validation sets. To further improve prediction, the top 10% of ANN models were selected, and their outputs averaged for prediction on testing set. Results: By random division, 44 cases in testing set and the remaining 265 cases have approximately equal two-year survival rates, 36.4% and 35.5% respectively. The 20 ANN models, which were trained and validated on the 265 cases, yielded mean c-indexes as 0.59 and 0.63 on validation sets and the testing set, respectively. C-index was 0.72 when the two best ANN models (top 10%) were used in prediction on testing set. The c-index of Cox regression analysis was 0.63. Conclusion: ANN improved survival prediction for patients with PAC. More patient data and further analysis of additional factors may be needed for a more robust model, which will help guide physicians in providing optimal post-operative care. This project was supported by PA CURE Grant.

  6. SU-E-T-131: Artificial Neural Networks Applied to Overall Survival Prediction for Patients with Periampullary Carcinoma

    International Nuclear Information System (INIS)

    Gong, Y; Yu, J; Yeung, V; Palmer, J; Yu, Y; Lu, B; Babinsky, L; Burkhart, R; Leiby, B; Siow, V; Lavu, H; Rosato, E; Winter, J; Lewis, N; Sama, A; Mitchell, E; Anne, P; Hurwitz, M; Yeo, C; Bar-Ad, V

    2015-01-01

    Purpose: Artificial neural networks (ANN) can be used to discover complex relations within datasets to help with medical decision making. This study aimed to develop an ANN method to predict two-year overall survival of patients with peri-ampullary cancer (PAC) following resection. Methods: Data were collected from 334 patients with PAC following resection treated in our institutional pancreatic tumor registry between 2006 and 2012. The dataset contains 14 variables including age, gender, T-stage, tumor differentiation, positive-lymph-node ratio, positive resection margins, chemotherapy, radiation therapy, and tumor histology.After censoring for two-year survival analysis, 309 patients were left, of which 44 patients (∼15%) were randomly selected to form testing set. The remaining 265 cases were randomly divided into training set (211 cases, ∼80% of 265) and validation set (54 cases, ∼20% of 265) for 20 times to build 20 ANN models. Each ANN has one hidden layer with 5 units. The 20 ANN models were ranked according to their concordance index (c-index) of prediction on validation sets. To further improve prediction, the top 10% of ANN models were selected, and their outputs averaged for prediction on testing set. Results: By random division, 44 cases in testing set and the remaining 265 cases have approximately equal two-year survival rates, 36.4% and 35.5% respectively. The 20 ANN models, which were trained and validated on the 265 cases, yielded mean c-indexes as 0.59 and 0.63 on validation sets and the testing set, respectively. C-index was 0.72 when the two best ANN models (top 10%) were used in prediction on testing set. The c-index of Cox regression analysis was 0.63. Conclusion: ANN improved survival prediction for patients with PAC. More patient data and further analysis of additional factors may be needed for a more robust model, which will help guide physicians in providing optimal post-operative care. This project was supported by PA CURE Grant

  7. Modeling the effect of temperature on survival rate of Salmonella Enteritidis in yogurt.

    Science.gov (United States)

    Szczawiński, J; Szczawińska, M E; Łobacz, A; Jackowska-Tracz, A

    2014-01-01

    The aim of the study was to determine the inactivation rates of Salmonella Enteritidis in commercially produced yogurt and to generate primary and secondary mathematical models to predict the behaviour of these bacteria during storage at different temperatures. The samples were inoculated with the mixture of three S. Enteritidis strains and stored at 5 degrees C, 10 degrees C, 15 degrees C, 20 degrees C and 25 degrees C for 24 h. The number of salmonellae was determined every two hours. It was found that the number of bacteria decreased linearly with storage time in all samples. Storage temperature and pH of yogurt significantly influenced survival rate of S. Enteritidis (p bacteria was the most dynamic. The natural logarithm of mean inactivation rates of Salmonella calculated from primary model was fitted to two secondary models: linear and polynomial. Equations obtained from both secondary models can be applied as a tool for prediction of inactivation rate of Salmonella in yogurt stored under temperature range from 5 to 25 degrees C; however, polynomial model gave the better fit to the experimental data.

  8. Combining Gene Signatures Improves Prediction of Breast Cancer Survival

    Science.gov (United States)

    Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

    2011-01-01

    Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast

  9. Combining gene signatures improves prediction of breast cancer survival.

    Directory of Open Access Journals (Sweden)

    Xi Zhao

    Full Text Available BACKGROUND: Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123 and test set (n = 81, respectively. Gene sets from eleven previously published gene signatures are included in the study. PRINCIPAL FINDINGS: To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014. Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001. The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. CONCLUSION: Combining the predictive strength of multiple gene signatures improves

  10. Direct Survival Analysis: a new stock assessment method

    Directory of Open Access Journals (Sweden)

    Eduardo Ferrandis

    2007-03-01

    Full Text Available In this work, a new stock assessment method, Direct Survival Analysis, is proposed and described. The parameter estimation of the Weibull survival model proposed by Ferrandis (2007 is obtained using trawl survey data. This estimation is used to establish a baseline survival function, which is in turn used to estimate the specific survival functions in the different cohorts considered through an adaptation of the separable model of the fishing mortality rates introduced by Pope and Shepherd (1982. It is thus possible to test hypotheses on the evolution of survival during the period studied and to identify trends in recruitment. A link is established between the preceding analysis of trawl survey data and the commercial catch-at-age data that are generally obtained to evaluate the population using analytical models. The estimated baseline survival, with the proposed versions of the stock and catch equations and the adaptation of the Separable Model, may be applied to commercial catch-at-age data. This makes it possible to estimate the survival corresponding to the landing data, the initial size of the cohort and finally, an effective age of first capture, in order to complete the parameter model estimation and consequently the estimation of the whole survival and mortality, along with the reference parameters that are useful for management purposes. Alternatively, this estimation of an effective age of first capture may be obtained by adapting the demographic structure of trawl survey data to that of the commercial fleet through suitable selectivity models of the commercial gears. The complete model provides the evaluation of the stock at any age. The coherence (and hence the mutual “calibration” between the two kinds of information may be analysed and compared with results obtained by other methods, such as virtual population analysis (VPA, in order to improve the diagnosis of the state of exploitation of the population. The model may be

  11. Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

    DEFF Research Database (Denmark)

    Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

    2014-01-01

    A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented co...... applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed......A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant...

  12. Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

    DEFF Research Database (Denmark)

    Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

    2013-01-01

    A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented co...... applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed......A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant...

  13. Modeling the effect of temperature on survival rate of Listeria monocytogenes in yogurt.

    Science.gov (United States)

    Szczawiński, J; Szczawińska, M E; Łobacz, A; Jackowska-Tracz, A

    2016-01-01

    The aim of the study was to (i) evaluate the behavior of Listeria monocytogenes in a commercially produced yogurt, (ii) determine the survival/inactivation rates of L. monocytogenes during cold storage of yogurt and (iii) to generate primary and secondary mathematical models to predict the behavior of these bacteria during storage at different temperatures. The samples of yogurt were inoculated with the mixture of three L. monocytogenes strains and stored at 3, 6, 9, 12 and 15°C for 16 days. The number of listeriae was determined after 0, 1, 2, 3, 5, 7, 9, 12, 14 and 16 days of storage. From each sample a series of decimal dilutions were prepared and plated onto ALOA agar (agar for Listeria according to Ottaviani and Agosti). It was found that applied temperature and storage time significantly influenced the survival rate of listeriae (pbacteria was found in the samples stored at 6°C (D-10 value = 243.9 h), whereas the highest reduction in the number of the bacteria was observed in the samples stored at 15°C (D-10 value = 87.0 h). The number of L. monocytogenes was correlated with the pH value of the samples (pyogurt stored under temperature range from 3 to 15°C, however, the polynomial model gave a better fit to the experimental data.

  14. Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting

    Science.gov (United States)

    Dazard, Jean-Eudes; Ishwaran, Hemant; Mehlotra, Rajeev; Weinberg, Aaron; Zimmerman, Peter

    2018-01-01

    Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance. Using various linear and nonlinear time-to-events survival models in simulation studies, we first show the efficiency of our approach: true pairwise interaction-effects between variables are uncovered, while they may not be accompanied with their corresponding main-effects, and may not be detected by standard semi-parametric regression modeling and test statistics used in survival analysis. Moreover, using a RSF-based cross-validation scheme for generating prediction estimators, we show that informative predictors may be inferred. We applied our approach to an HIV cohort study recording key host gene polymorphisms and their association with HIV change of tropism or AIDS progression. Altogether, this shows how linear or nonlinear pairwise statistical interactions of variables may be efficiently detected with a predictive value in observational studies with time-to-event outcomes. PMID:29453930

  15. Causal Mediation Analysis of Survival Outcome with Multiple Mediators.

    Science.gov (United States)

    Huang, Yen-Tsung; Yang, Hwai-I

    2017-05-01

    Mediation analyses have been a popular approach to investigate the effect of an exposure on an outcome through a mediator. Mediation models with multiple mediators have been proposed for continuous and dichotomous outcomes. However, development of multimediator models for survival outcomes is still limited. We present methods for multimediator analyses using three survival models: Aalen additive hazard models, Cox proportional hazard models, and semiparametric probit models. Effects through mediators can be characterized by path-specific effects, for which definitions and identifiability assumptions are provided. We derive closed-form expressions for path-specific effects for the three models, which are intuitively interpreted using a causal diagram. Mediation analyses using Cox models under the rare-outcome assumption and Aalen additive hazard models consider effects on log hazard ratio and hazard difference, respectively; analyses using semiparametric probit models consider effects on difference in transformed survival time and survival probability. The three models were applied to a hepatitis study where we investigated effects of hepatitis C on liver cancer incidence mediated through baseline and/or follow-up hepatitis B viral load. The three methods show consistent results on respective effect scales, which suggest an adverse estimated effect of hepatitis C on liver cancer not mediated through hepatitis B, and a protective estimated effect mediated through the baseline (and possibly follow-up) of hepatitis B viral load. Causal mediation analyses of survival outcome with multiple mediators are developed for additive hazard and proportional hazard and probit models with utility demonstrated in a hepatitis study.

  16. Hepatic retransplantation in New England--a regional experience and survival model.

    Science.gov (United States)

    Powelson, J A; Cosimi, A B; Lewis, W D; Rohrer, R J; Freeman, R B; Vacanti, J P; Jonas, M; Lorber, M I; Marks, W H; Bradley, J

    1993-04-01

    Hepatic retransplantation (reTx) offers the only alternative to death for patients who have failed primary hepatic transplantation (PTx). Assuming a finite number of donor organs, reTx also denies the chance of survival for some patients awaiting PTx. The impact of reTx on overall survival (i.e., the survival of all candidates for transplantation) must therefore be clarified. Between 1983 and 1991, 651 patients from the New England Organ Bank underwent liver transplantation, and 73 reTx were performed in 71 patients (11% reTx rate). The 1-year actuarial survival for reTx (48%) was significantly less than for PTx (70%, P 365 days, 83%). Patients on the regional waiting list had an 18% mortality rate while awaiting transplantation. These results were incorporated into a mathematical model describing survival as a function of reTx rate, assuming a limited supply of donor livers. ReTx improves the 1-year survival rate for patients undergoing PTx but decreases overall survival (survival of all candidates) for liver transplantation. In the current era of persistently insufficient donor numbers, strategies based on minimizing the use of reTx, especially in the case of patients in whom chances of success are minimal, will result in the best overall rate of patient survival.

  17. Survival of the scarcer in space

    International Nuclear Information System (INIS)

    Dos Santos, Renato Vieira; Dickman, Ronald

    2013-01-01

    The dynamics leading to extinction or coexistence of competing species is of great interest in ecology and related fields. Recently a model of intra- and interspecific competition between two species was proposed by Gabel et al, in which the scarcer species (i.e., with smaller stationary population size) can be more resistant to extinction when it holds a competitive advantage; the latter study considered populations without spatial variation. Here we verify this phenomenon in populations distributed in space. We extend the model of Gabel et al to a d-dimensional lattice, and study its population dynamics both analytically and numerically. Survival of the scarcer in space is verified for situations in which the more competitive species is closer to the threshold for extinction than is the less competitive species, when considered in isolation. The conditions for survival of the scarcer species, as obtained applying renormalization group analysis and Monte Carlo simulation, differ in detail from those found in the spatially homogeneous case. Simulations highlight the speed of invasion waves in determining the survival times of the competing species. (paper)

  18. Cell survival in carbon beams - comparison of amorphous track model predictions

    DEFF Research Database (Denmark)

    Grzanka, L.; Greilich, S.; Korcyl, M.

    Introduction: Predictions of the radiobiological effectiveness (RBE) play an essential role in treatment planning with heavy charged particles. Amorphous track models ( [1] , [2] , also referred to as track structure models) provide currently the most suitable description of cell survival under i....... Amorphous track modelling of luminescence detector efficiency in proton and carbon beams. 4.Tsuruoka C, Suzuki M, Kanai T, et al. LET and ion species dependence for cell killing in normal human skin fibroblasts. Radiat Res. 2005;163:494-500.......Introduction: Predictions of the radiobiological effectiveness (RBE) play an essential role in treatment planning with heavy charged particles. Amorphous track models ( [1] , [2] , also referred to as track structure models) provide currently the most suitable description of cell survival under ion....... [2] . In addition, a new approach based on microdosimetric distributions is presented and investigated [3] . Material and methods: A suitable software library embrasing the mentioned amorphous track models including numerous submodels with respect to delta-electron range models, radial dose...

  19. Survival Old Model Tamping on Bugis House in Kampong of Bunne Regency of Soppeng South Sulawesi Indonesia

    Science.gov (United States)

    Abidah, Andi

    2017-10-01

    Tamping is space circulation from terrace to inside home and also as space for sitting space for low rank social community. Position tamping is one of side of main house. The floor of tamping slightly low than main house floor, this model has seldom found today which community more refer on new tamping model. The new model of tamping today, the same level on main house floor. Even new Bugis house model without tamping. Old model house use tamping but the tamping and watangpola ha the same floor level. This model consists of four modules which three modules on main house and one module tamping. In the past, old model of tamping is different level floor between watangpola and tamping floor now this tamping floor of old Bugis house model gone the same level of watangpola. While new model called eppa-eppa house, did not use tamping. Community in Kampung Bunne is till survive on old model of tamping on their house although several house has change its tamping like community applied now. This model is still found around 45 house of total number of house in the kampung. This study will explore applying old model of tamping of Bugis house in kampong Bunne Regency of Soppeng South Sulawesi. Qualitative research is used on this study. The study was developed base in sketch, photograph and interview.

  20. Mediation Analysis with Survival Outcomes: Accelerated Failure Time vs. Proportional Hazards Models.

    Science.gov (United States)

    Gelfand, Lois A; MacKinnon, David P; DeRubeis, Robert J; Baraldi, Amanda N

    2016-01-01

    Survival time is an important type of outcome variable in treatment research. Currently, limited guidance is available regarding performing mediation analyses with survival outcomes, which generally do not have normally distributed errors, and contain unobserved (censored) events. We present considerations for choosing an approach, using a comparison of semi-parametric proportional hazards (PH) and fully parametric accelerated failure time (AFT) approaches for illustration. We compare PH and AFT models and procedures in their integration into mediation models and review their ability to produce coefficients that estimate causal effects. Using simulation studies modeling Weibull-distributed survival times, we compare statistical properties of mediation analyses incorporating PH and AFT approaches (employing SAS procedures PHREG and LIFEREG, respectively) under varied data conditions, some including censoring. A simulated data set illustrates the findings. AFT models integrate more easily than PH models into mediation models. Furthermore, mediation analyses incorporating LIFEREG produce coefficients that can estimate causal effects, and demonstrate superior statistical properties. Censoring introduces bias in the coefficient estimate representing the treatment effect on outcome-underestimation in LIFEREG, and overestimation in PHREG. With LIFEREG, this bias can be addressed using an alternative estimate obtained from combining other coefficients, whereas this is not possible with PHREG. When Weibull assumptions are not violated, there are compelling advantages to using LIFEREG over PHREG for mediation analyses involving survival-time outcomes. Irrespective of the procedures used, the interpretation of coefficients, effects of censoring on coefficient estimates, and statistical properties should be taken into account when reporting results.

  1. Mediation Analysis with Survival Outcomes: Accelerated Failure Time vs. Proportional Hazards Models

    Science.gov (United States)

    Gelfand, Lois A.; MacKinnon, David P.; DeRubeis, Robert J.; Baraldi, Amanda N.

    2016-01-01

    Objective: Survival time is an important type of outcome variable in treatment research. Currently, limited guidance is available regarding performing mediation analyses with survival outcomes, which generally do not have normally distributed errors, and contain unobserved (censored) events. We present considerations for choosing an approach, using a comparison of semi-parametric proportional hazards (PH) and fully parametric accelerated failure time (AFT) approaches for illustration. Method: We compare PH and AFT models and procedures in their integration into mediation models and review their ability to produce coefficients that estimate causal effects. Using simulation studies modeling Weibull-distributed survival times, we compare statistical properties of mediation analyses incorporating PH and AFT approaches (employing SAS procedures PHREG and LIFEREG, respectively) under varied data conditions, some including censoring. A simulated data set illustrates the findings. Results: AFT models integrate more easily than PH models into mediation models. Furthermore, mediation analyses incorporating LIFEREG produce coefficients that can estimate causal effects, and demonstrate superior statistical properties. Censoring introduces bias in the coefficient estimate representing the treatment effect on outcome—underestimation in LIFEREG, and overestimation in PHREG. With LIFEREG, this bias can be addressed using an alternative estimate obtained from combining other coefficients, whereas this is not possible with PHREG. Conclusions: When Weibull assumptions are not violated, there are compelling advantages to using LIFEREG over PHREG for mediation analyses involving survival-time outcomes. Irrespective of the procedures used, the interpretation of coefficients, effects of censoring on coefficient estimates, and statistical properties should be taken into account when reporting results. PMID:27065906

  2. Mediation Analysis with Survival Outcomes: Accelerated Failure Time Versus Proportional Hazards Models

    Directory of Open Access Journals (Sweden)

    Lois A Gelfand

    2016-03-01

    Full Text Available Objective: Survival time is an important type of outcome variable in treatment research. Currently, limited guidance is available regarding performing mediation analyses with survival outcomes, which generally do not have normally distributed errors, and contain unobserved (censored events. We present considerations for choosing an approach, using a comparison of semi-parametric proportional hazards (PH and fully parametric accelerated failure time (AFT approaches for illustration.Method: We compare PH and AFT models and procedures in their integration into mediation models and review their ability to produce coefficients that estimate causal effects. Using simulation studies modeling Weibull-distributed survival times, we compare statistical properties of mediation analyses incorporating PH and AFT approaches (employing SAS procedures PHREG and LIFEREG, respectively under varied data conditions, some including censoring. A simulated data set illustrates the findings.Results: AFT models integrate more easily than PH models into mediation models. Furthermore, mediation analyses incorporating LIFEREG produce coefficients that can estimate causal effects, and demonstrate superior statistical properties. Censoring introduces bias in the coefficient estimate representing the treatment effect on outcome – underestimation in LIFEREG, and overestimation in PHREG. With LIFEREG, this bias can be addressed using an alternative estimate obtained from combining other coefficients, whereas this is not possible with PHREG.Conclusions: When Weibull assumptions are not violated, there are compelling advantages to using LIFEREG over PHREG for mediation analyses involving survival-time outcomes. Irrespective of the procedures used, the interpretation of coefficients, effects of censoring on coefficient estimates, and statistical properties should be taken into account when reporting results.

  3. A Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients

    Energy Technology Data Exchange (ETDEWEB)

    Oberije, Cary, E-mail: cary.oberije@maastro.nl [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands); De Ruysscher, Dirk [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands); Universitaire Ziekenhuizen Leuven, KU Leuven (Belgium); Houben, Ruud [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands); Heuvel, Michel van de; Uyterlinde, Wilma [Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam (Netherlands); Deasy, Joseph O. [Memorial Sloan Kettering Cancer Center, New York (United States); Belderbos, Jose [Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam (Netherlands); Dingemans, Anne-Marie C. [Department of Pulmonology, University Hospital Maastricht, Research Institute GROW of Oncology, Maastricht (Netherlands); Rimner, Andreas; Din, Shaun [Memorial Sloan Kettering Cancer Center, New York (United States); Lambin, Philippe [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands)

    2015-07-15

    Purpose: Although patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing and validating a model that can provide physicians with a survival probability for an individual NSCLC patient. Methods and Materials: Data from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130). Results: The final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient's survival probability ( (www.predictcancer.org)). The data set can be downloaded at (https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048). Conclusions: The prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system.

  4. Robust estimation of the expected survival probabilities from high-dimensional Cox models with biomarker-by-treatment interactions in randomized clinical trials

    Directory of Open Access Journals (Sweden)

    Nils Ternès

    2017-05-01

    Full Text Available Abstract Background Thanks to the advances in genomics and targeted treatments, more and more prediction models based on biomarkers are being developed to predict potential benefit from treatments in a randomized clinical trial. Despite the methodological framework for the development and validation of prediction models in a high-dimensional setting is getting more and more established, no clear guidance exists yet on how to estimate expected survival probabilities in a penalized model with biomarker-by-treatment interactions. Methods Based on a parsimonious biomarker selection in a penalized high-dimensional Cox model (lasso or adaptive lasso, we propose a unified framework to: estimate internally the predictive accuracy metrics of the developed model (using double cross-validation; estimate the individual survival probabilities at a given timepoint; construct confidence intervals thereof (analytical or bootstrap; and visualize them graphically (pointwise or smoothed with spline. We compared these strategies through a simulation study covering scenarios with or without biomarker effects. We applied the strategies to a large randomized phase III clinical trial that evaluated the effect of adding trastuzumab to chemotherapy in 1574 early breast cancer patients, for which the expression of 462 genes was measured. Results In our simulations, penalized regression models using the adaptive lasso estimated the survival probability of new patients with low bias and standard error; bootstrapped confidence intervals had empirical coverage probability close to the nominal level across very different scenarios. The double cross-validation performed on the training data set closely mimicked the predictive accuracy of the selected models in external validation data. We also propose a useful visual representation of the expected survival probabilities using splines. In the breast cancer trial, the adaptive lasso penalty selected a prediction model with 4

  5. Estimating haplotype effects for survival data.

    Science.gov (United States)

    Scheike, Thomas H; Martinussen, Torben; Silver, Jeremy D

    2010-09-01

    Genetic association studies often investigate the effect of haplotypes on an outcome of interest. Haplotypes are not observed directly, and this complicates the inclusion of such effects in survival models. We describe a new estimating equations approach for Cox's regression model to assess haplotype effects for survival data. These estimating equations are simple to implement and avoid the use of the EM algorithm, which may be slow in the context of the semiparametric Cox model with incomplete covariate information. These estimating equations also lead to easily computable, direct estimators of standard errors, and thus overcome some of the difficulty in obtaining variance estimators based on the EM algorithm in this setting. We also develop an easily implemented goodness-of-fit procedure for Cox's regression model including haplotype effects. Finally, we apply the procedures presented in this article to investigate possible haplotype effects of the PAF-receptor on cardiovascular events in patients with coronary artery disease, and compare our results to those based on the EM algorithm. © 2009, The International Biometric Society.

  6. Nest survival modelling using a multi-species approach in forests managed for timber and biofuel feedstock

    Science.gov (United States)

    Loman, Zachary G.; Monroe, Adrian; Riffell, Samuel K.; Miller, Darren A.; Vilella, Francisco; Wheat, Bradley R.; Rush, Scott A.; Martin, James A.

    2018-01-01

    Switchgrass (Panicum virgatum) intercropping is a novel forest management practice for biomass production intended to generate cellulosic feedstocks within intensively managed loblolly pine‐dominated landscapes. These pine plantations are important for early‐successional bird species, as short rotation times continually maintain early‐successional habitat. We tested the efficacy of using community models compared to individual surrogate species models in understanding influences on nest survival. We analysed nest data to test for differences in habitat use for 14 bird species in plots managed for switchgrass intercropping and controls within loblolly pine (Pinus taeda) plantations in Mississippi, USA.We adapted hierarchical models using hyper‐parameters to incorporate information from both common and rare species to understand community‐level nest survival. This approach incorporates rare species that are often discarded due to low sample sizes, but can inform community‐level demographic parameter estimates. We illustrate use of this approach in generating both species‐level and community‐wide estimates of daily survival rates for songbird nests. We were able to include rare species with low sample size (minimum n = 5) to inform a hyper‐prior, allowing us to estimate effects of covariates on daily survival at the community level, then compare this with a single‐species approach using surrogate species. Using single‐species models, we were unable to generate estimates below a sample size of 21 nests per species.Community model species‐level survival and parameter estimates were similar to those generated by five single‐species models, with improved precision in community model parameters.Covariates of nest placement indicated that switchgrass at the nest site (<4 m) reduced daily nest survival, although intercropping at the forest stand level increased daily nest survival.Synthesis and applications. Community models represent a viable

  7. A hands-on approach for fitting long-term survival models under the GAMLSS framework.

    Science.gov (United States)

    de Castro, Mário; Cancho, Vicente G; Rodrigues, Josemar

    2010-02-01

    In many data sets from clinical studies there are patients insusceptible to the occurrence of the event of interest. Survival models which ignore this fact are generally inadequate. The main goal of this paper is to describe an application of the generalized additive models for location, scale, and shape (GAMLSS) framework to the fitting of long-term survival models. In this work the number of competing causes of the event of interest follows the negative binomial distribution. In this way, some well known models found in the literature are characterized as particular cases of our proposal. The model is conveniently parameterized in terms of the cured fraction, which is then linked to covariates. We explore the use of the gamlss package in R as a powerful tool for inference in long-term survival models. The procedure is illustrated with a numerical example. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

  8. Applied impulsive mathematical models

    CERN Document Server

    Stamova, Ivanka

    2016-01-01

    Using the theory of impulsive differential equations, this book focuses on mathematical models which reflect current research in biology, population dynamics, neural networks and economics. The authors provide the basic background from the fundamental theory and give a systematic exposition of recent results related to the qualitative analysis of impulsive mathematical models. Consisting of six chapters, the book presents many applicable techniques, making them available in a single source easily accessible to researchers interested in mathematical models and their applications. Serving as a valuable reference, this text is addressed to a wide audience of professionals, including mathematicians, applied researchers and practitioners.

  9. A special case of reduced rank models for identification and modelling of time varying effects in survival analysis.

    Science.gov (United States)

    Perperoglou, Aris

    2016-12-10

    Flexible survival models are in need when modelling data from long term follow-up studies. In many cases, the assumption of proportionality imposed by a Cox model will not be valid. Instead, a model that can identify time varying effects of fixed covariates can be used. Although there are several approaches that deal with this problem, it is not always straightforward how to choose which covariates should be modelled having time varying effects and which not. At the same time, it is up to the researcher to define appropriate time functions that describe the dynamic pattern of the effects. In this work, we suggest a model that can deal with both fixed and time varying effects and uses simple hypotheses tests to distinguish which covariates do have dynamic effects. The model is an extension of the parsimonious reduced rank model of rank 1. As such, the number of parameters is kept low, and thus, a flexible set of time functions, such as b-splines, can be used. The basic theory is illustrated along with an efficient fitting algorithm. The proposed method is applied to a dataset of breast cancer patients and compared with a multivariate fractional polynomials approach for modelling time-varying effects. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  10. A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling.

    Science.gov (United States)

    Leger, Stefan; Zwanenburg, Alex; Pilz, Karoline; Lohaus, Fabian; Linge, Annett; Zöphel, Klaus; Kotzerke, Jörg; Schreiber, Andreas; Tinhofer, Inge; Budach, Volker; Sak, Ali; Stuschke, Martin; Balermpas, Panagiotis; Rödel, Claus; Ganswindt, Ute; Belka, Claus; Pigorsch, Steffi; Combs, Stephanie E; Mönnich, David; Zips, Daniel; Krause, Mechthild; Baumann, Michael; Troost, Esther G C; Löck, Steffen; Richter, Christian

    2017-10-16

    Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco-regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e.g. C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints.

  11. On the analysis of clonogenic survival data: Statistical alternatives to the linear-quadratic model

    International Nuclear Information System (INIS)

    Unkel, Steffen; Belka, Claus; Lauber, Kirsten

    2016-01-01

    The most frequently used method to quantitatively describe the response to ionizing irradiation in terms of clonogenic survival is the linear-quadratic (LQ) model. In the LQ model, the logarithm of the surviving fraction is regressed linearly on the radiation dose by means of a second-degree polynomial. The ratio of the estimated parameters for the linear and quadratic term, respectively, represents the dose at which both terms have the same weight in the abrogation of clonogenic survival. This ratio is known as the α/β ratio. However, there are plausible scenarios in which the α/β ratio fails to sufficiently reflect differences between dose-response curves, for example when curves with similar α/β ratio but different overall steepness are being compared. In such situations, the interpretation of the LQ model is severely limited. Colony formation assays were performed in order to measure the clonogenic survival of nine human pancreatic cancer cell lines and immortalized human pancreatic ductal epithelial cells upon irradiation at 0-10 Gy. The resulting dataset was subjected to LQ regression and non-linear log-logistic regression. Dimensionality reduction of the data was performed by cluster analysis and principal component analysis. Both the LQ model and the non-linear log-logistic regression model resulted in accurate approximations of the observed dose-response relationships in the dataset of clonogenic survival. However, in contrast to the LQ model the non-linear regression model allowed the discrimination of curves with different overall steepness but similar α/β ratio and revealed an improved goodness-of-fit. Additionally, the estimated parameters in the non-linear model exhibit a more direct interpretation than the α/β ratio. Dimensionality reduction of clonogenic survival data by means of cluster analysis was shown to be a useful tool for classifying radioresistant and sensitive cell lines. More quantitatively, principal component analysis allowed

  12. Validation of a Predictive Model for Survival in Metastatic Cancer Patients Attending an Outpatient Palliative Radiotherapy Clinic

    International Nuclear Information System (INIS)

    Chow, Edward; Abdolell, Mohamed; Panzarella, Tony; Harris, Kristin; Bezjak, Andrea; Warde, Padraig; Tannock, Ian

    2009-01-01

    Purpose: To validate a predictive model for survival of patients attending a palliative radiotherapy clinic. Methods and Materials: We described previously a model that had good predictive value for survival of patients referred during 1999 (1). The six prognostic factors (primary cancer site, site of metastases, Karnofsky performance score, and the fatigue, appetite and shortness-of-breath items from the Edmonton Symptom Assessment Scale) identified in this training set were extracted from the prospective database for the year 2000. We generated a partial score whereby each prognostic factor was assigned a value proportional to its prognostic weight. The sum of the partial scores for each patient was used to construct a survival prediction score (SPS). Patients were also grouped according to the number of these risk factors (NRF) that they possessed. The probability of survival at 3, 6, and 12 months was generated. The models were evaluated for their ability to predict survival in this validation set with appropriate statistical tests. Results: The median survival and survival probabilities of the training and validation sets were similar when separated into three groups using both SPS and NRF methods. There was no statistical difference in the performance of the SPS and NRF methods in survival prediction. Conclusion: Both the SPS and NRF models for predicting survival in patients referred for palliative radiotherapy have been validated. The NRF model is preferred because it is simpler and avoids the need to remember the weightings among the prognostic factors

  13. A comparison of two-component and quadratic models to assess survival of irradiated stage-7 oocytes of Drosophila melanogaster

    International Nuclear Information System (INIS)

    Peres, C.A.; Koo, J.O.

    1981-01-01

    In this paper, the quadratic model to analyse data of this kind, i.e. S/S 0 = exp(-αD-bD 2 ), where S and Ssub(o) are defined as before is proposed is shown that the same biological interpretation can be given to the parameters α and A and to the parameters β and B. Furthermore it is shown that the quadratic model involves one probabilistic stage more than the two-component model, and therefore the quadratic model would perhaps be more appropriate as a dose-response model for survival of irradiated stage-7 oocytes of Drosophila melanogaster. In order to apply these results, the data presented by Sankaranarayanan and Sankaranarayanan and Volkers are reanalysed using the quadratic model. It is shown that the quadratic model fits better than the two-component model to the data in most situations. (orig./AJ)

  14. Monte Carlo model to simulate the effects of DNA damage resulting from accumulation of 125I decays during development of colonies and clonogenic survival assays

    International Nuclear Information System (INIS)

    Lobachevsky, P.; Karagiannis, T.; Martin, R.F.

    1998-01-01

    Full text: Exposure of cultured cells to an internal source of ionising radiation, such as a radioactive isotope, differs substantially from external irradiation in the determination of delivered dose. In some cases, the radioactive isotope cannot be quickly and completely removed from cells before plating for clonogenic survival assay. This provides an additional dose of irradiation which is not easy to calculate. The contribution of this phenomenon to the cell survival is especially important if a radioactive isotope is incorporated into DNA, or a DNA-binding ligand is labelled with the isotope. The correction of the cell survival due to additional dose cannot be calculated using a simple analytical expression, since the isotope is present in the cells during colony growth. We have developed a Monte Carlo model which simulates the process of the colony growth, and takes into account the extent of damage from isotope decays accumulated between consequent cell divisions. The model considers such factors as cell cycle time, radiosensitivity, colony growth inhibition, isotope specific (per cell) activity, partition of isotope in daughter cells, isotope half-life time, isotope efflux. The model allows estimation of the impact of the irradiation during colony formation on the distribution of colony size, and on the calculation of the survival correction factor, which depends mainly on the isotope cell-specific activity. We applied the model to interpret the difference in survival of K652 cells exposed to 125 I decays with various cell-specific activities: 0.45, 3.21 and 7.42 decays/cell/hour. The cells were treated with 125 I - labelled Hoechst 33258 which binds to DNA in cell nucleus. After accumulation of 125 I decays under non-growth conditions, cells were plated for clonogenic survival assay. The survival correction factors calculated from the model for the given values of 125 I cell-specific activity are in good correlation with differences between experimental

  15. Survival analysis

    International Nuclear Information System (INIS)

    Badwe, R.A.

    1999-01-01

    The primary endpoint in the majority of the studies has been either disease recurrence or death. This kind of analysis requires a special method since all patients in the study experience the endpoint. The standard method for estimating such survival distribution is Kaplan Meier method. The survival function is defined as the proportion of individuals who survive beyond certain time. Multi-variate comparison for survival has been carried out with Cox's proportional hazard model

  16. Parameter resolution in two models for cell survival after radiation

    International Nuclear Information System (INIS)

    Di Cera, E.; Andreasi Bassi, F.; Arcovito, G.

    1989-01-01

    The resolvability of model parameters for the linear-quadratic and the repair-misrepair models for cell survival after radiation has been studied by Monte Carlo simulations as a function of the number of experimental data points collected in a given dose range and the experimental error. Statistical analysis of the results reveals the range of experimental conditions under which the model parameters can be resolved with sufficient accuracy, and points out some differences in the operational aspects of the two models. (orig.)

  17. A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data.

    Science.gov (United States)

    Nasejje, Justine B; Mwambi, Henry; Dheda, Keertan; Lesosky, Maia

    2017-07-28

    Random survival forest (RSF) models have been identified as alternative methods to the Cox proportional hazards model in analysing time-to-event data. These methods, however, have been criticised for the bias that results from favouring covariates with many split-points and hence conditional inference forests for time-to-event data have been suggested. Conditional inference forests (CIF) are known to correct the bias in RSF models by separating the procedure for the best covariate to split on from that of the best split point search for the selected covariate. In this study, we compare the random survival forest model to the conditional inference model (CIF) using twenty-two simulated time-to-event datasets. We also analysed two real time-to-event datasets. The first dataset is based on the survival of children under-five years of age in Uganda and it consists of categorical covariates with most of them having more than two levels (many split-points). The second dataset is based on the survival of patients with extremely drug resistant tuberculosis (XDR TB) which consists of mainly categorical covariates with two levels (few split-points). The study findings indicate that the conditional inference forest model is superior to random survival forest models in analysing time-to-event data that consists of covariates with many split-points based on the values of the bootstrap cross-validated estimates for integrated Brier scores. However, conditional inference forests perform comparably similar to random survival forests models in analysing time-to-event data consisting of covariates with fewer split-points. Although survival forests are promising methods in analysing time-to-event data, it is important to identify the best forest model for analysis based on the nature of covariates of the dataset in question.

  18. A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data

    Directory of Open Access Journals (Sweden)

    Justine B. Nasejje

    2017-07-01

    Full Text Available Abstract Background Random survival forest (RSF models have been identified as alternative methods to the Cox proportional hazards model in analysing time-to-event data. These methods, however, have been criticised for the bias that results from favouring covariates with many split-points and hence conditional inference forests for time-to-event data have been suggested. Conditional inference forests (CIF are known to correct the bias in RSF models by separating the procedure for the best covariate to split on from that of the best split point search for the selected covariate. Methods In this study, we compare the random survival forest model to the conditional inference model (CIF using twenty-two simulated time-to-event datasets. We also analysed two real time-to-event datasets. The first dataset is based on the survival of children under-five years of age in Uganda and it consists of categorical covariates with most of them having more than two levels (many split-points. The second dataset is based on the survival of patients with extremely drug resistant tuberculosis (XDR TB which consists of mainly categorical covariates with two levels (few split-points. Results The study findings indicate that the conditional inference forest model is superior to random survival forest models in analysing time-to-event data that consists of covariates with many split-points based on the values of the bootstrap cross-validated estimates for integrated Brier scores. However, conditional inference forests perform comparably similar to random survival forests models in analysing time-to-event data consisting of covariates with fewer split-points. Conclusion Although survival forests are promising methods in analysing time-to-event data, it is important to identify the best forest model for analysis based on the nature of covariates of the dataset in question.

  19. Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods

    Science.gov (United States)

    Dazard, Jean-Eudes; Choe, Michael; LeBlanc, Michael; Rao, J. Sunil

    2015-01-01

    We introduce a framework to build a survival/risk bump hunting model with a censored time-to-event response. Our Survival Bump Hunting (SBH) method is based on a recursive peeling procedure that uses a specific survival peeling criterion derived from non/semi-parametric statistics such as the hazards-ratio, the log-rank test or the Nelson--Aalen estimator. To optimize the tuning parameter of the model and validate it, we introduce an objective function based on survival or prediction-error statistics, such as the log-rank test and the concordance error rate. We also describe two alternative cross-validation techniques adapted to the joint task of decision-rule making by recursive peeling and survival estimation. Numerical analyses show the importance of replicated cross-validation and the differences between criteria and techniques in both low and high-dimensional settings. Although several non-parametric survival models exist, none addresses the problem of directly identifying local extrema. We show how SBH efficiently estimates extreme survival/risk subgroups unlike other models. This provides an insight into the behavior of commonly used models and suggests alternatives to be adopted in practice. Finally, our SBH framework was applied to a clinical dataset. In it, we identified subsets of patients characterized by clinical and demographic covariates with a distinct extreme survival outcome, for which tailored medical interventions could be made. An R package PRIMsrc (Patient Rule Induction Method in Survival, Regression and Classification settings) is available on CRAN (Comprehensive R Archive Network) and GitHub. PMID:27034730

  20. Inference for shared-frailty survival models with left-truncated data

    NARCIS (Netherlands)

    van den Berg, G.J.; Drepper, B.

    2016-01-01

    Shared-frailty survival models specify that systematic unobserved determinants of duration outcomes are identical within groups of individuals. We consider random-effects likelihood-based statistical inference if the duration data are subject to left-truncation. Such inference with left-truncated

  1. Development of a likelihood of survival scoring system for hospitalized equine neonates using generalized boosted regression modeling.

    Directory of Open Access Journals (Sweden)

    Katarzyna A Dembek

    Full Text Available BACKGROUND: Medical management of critically ill equine neonates (foals can be expensive and labor intensive. Predicting the odds of foal survival using clinical information could facilitate the decision-making process for owners and clinicians. Numerous prognostic indicators and mathematical models to predict outcome in foals have been published; however, a validated scoring method to predict survival in sick foals has not been reported. The goal of this study was to develop and validate a scoring system that can be used by clinicians to predict likelihood of survival of equine neonates based on clinical data obtained on admission. METHODS AND RESULTS: Data from 339 hospitalized foals of less than four days of age admitted to three equine hospitals were included to develop the model. Thirty seven variables including historical information, physical examination and laboratory findings were analyzed by generalized boosted regression modeling (GBM to determine which ones would be included in the survival score. Of these, six variables were retained in the final model. The weight for each variable was calculated using a generalized linear model and the probability of survival for each total score was determined. The highest (7 and the lowest (0 scores represented 97% and 3% probability of survival, respectively. Accuracy of this survival score was validated in a prospective study on data from 283 hospitalized foals from the same three hospitals. Sensitivity, specificity, positive and negative predictive values for the survival score in the prospective population were 96%, 71%, 91%, and 85%, respectively. CONCLUSIONS: The survival score developed in our study was validated in a large number of foals with a wide range of diseases and can be easily implemented using data available in most equine hospitals. GBM was a useful tool to develop the survival score. Further evaluations of this scoring system in field conditions are needed.

  2. Sensitivity analysis approaches applied to systems biology models.

    Science.gov (United States)

    Zi, Z

    2011-11-01

    With the rising application of systems biology, sensitivity analysis methods have been widely applied to study the biological systems, including metabolic networks, signalling pathways and genetic circuits. Sensitivity analysis can provide valuable insights about how robust the biological responses are with respect to the changes of biological parameters and which model inputs are the key factors that affect the model outputs. In addition, sensitivity analysis is valuable for guiding experimental analysis, model reduction and parameter estimation. Local and global sensitivity analysis approaches are the two types of sensitivity analysis that are commonly applied in systems biology. Local sensitivity analysis is a classic method that studies the impact of small perturbations on the model outputs. On the other hand, global sensitivity analysis approaches have been applied to understand how the model outputs are affected by large variations of the model input parameters. In this review, the author introduces the basic concepts of sensitivity analysis approaches applied to systems biology models. Moreover, the author discusses the advantages and disadvantages of different sensitivity analysis methods, how to choose a proper sensitivity analysis approach, the available sensitivity analysis tools for systems biology models and the caveats in the interpretation of sensitivity analysis results.

  3. Bernstein - Von Mises theorem and its application in survival analysis

    Czech Academy of Sciences Publication Activity Database

    Timková, Jana

    2010-01-01

    Roč. 22, č. 3 (2010), s. 115-122 ISSN 1210-8022. [16. letní škola JČMF Robust 2010. Králíky, 30.01.2010-05.02.2010] R&D Projects: GA AV ČR(CZ) IAA101120604 Institutional research plan: CEZ:AV0Z10750506 Keywords : Cox model * bayesian asymptotics * survival function Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2010/SI/timkova-bernstein - von mises theorem and its application in survival analysis.pdf

  4. Comparison of six different models describing survival of mammalian cells after irradiation

    International Nuclear Information System (INIS)

    Sontag, W.

    1990-01-01

    Six different cell-survival models have been compared. All models are based on the similar assumption that irradiated cells are able to exist in one of three states. S A is the state of a totally repaired cell, in state S C the cell contains lethal lesions and in state S b the cell contains potentially lethal lesions i.e. those which either can be repaired or converted into lethal lesions. The differences between the six models lie in the different mathematical relationships between the three states. To test the six models, six different sets of experimental data were used which describe cell survival at different repair times after irradiation with sparsely ionizing irradiation. In order to compare the models, a goodness-of-fit function was used. The differences between the six models were tested by use of the nonparametric Mann-Whitney two sample test. Based on the 95% confidence limit, this required separation into three groups. (orig.)

  5. Italian regional health system structure and expected cancer survival.

    Science.gov (United States)

    Vercelli, Marina; Lillini, Roberto; Quaglia, Alberto; Capocaccia, Riccardo

    2014-01-01

    Few studies deal with the association of socioeconomic and health system resource variables with cancer survival at the Italian regional level, where the greatest number of decisions about social and health policies and resource allocations are taken. The present study aimed to describe the causal relationships between socioeconomic and health system resource factors and regional cancer survival and to compute the expected cancer survival at provincial, regional and area levels. Age-standardized relative survival at 5 years from diagnosis of cases incident in 1995-1998 and followed up to 2004 were derived by gender for 11 sites from the Italian Association of Cancer Registries data bank. The socioeconomic and health system resource variables, describing at a regional level the macro-economy, demography, labor market, and health resources for 1995-2005, came from the Health for All database. A principal components factor analysis was applied to the socioeconomic and health system resource variables. For every site, linear regression models were computed considering the relative survival at 5 years as a dependent variable and the principal components factor analysis factors as independent variables. The factors described the socioeconomic and health-related features of the regional systems and were causally related to the characteristics of the patient taken in charge. The models built by the factors allowed computation of the expected relative survival at 5 years with very good concordance with those observed at regional, macro-regional and national levels. In the regions without any cancer registry, survival was coherent with that of neighboring regions with similar socioeconomic and health system resources characteristics. The models highlighted the causal correlations between socioeconomic and health system resources and cancer survival, suggesting that they could be good evaluation tools for the efficiency of the resources allocation and use.

  6. Parametric and semiparametric models with applications to reliability, survival analysis, and quality of life

    CERN Document Server

    Nikulin, M; Mesbah, M; Limnios, N

    2004-01-01

    Parametric and semiparametric models are tools with a wide range of applications to reliability, survival analysis, and quality of life. This self-contained volume examines these tools in survey articles written by experts currently working on the development and evaluation of models and methods. While a number of chapters deal with general theory, several explore more specific connections and recent results in "real-world" reliability theory, survival analysis, and related fields.

  7. Meta-analysis of single-arm survival studies: a distribution-free approach for estimating summary survival curves with random effects.

    Science.gov (United States)

    Combescure, Christophe; Foucher, Yohann; Jackson, Daniel

    2014-07-10

    In epidemiologic studies and clinical trials with time-dependent outcome (for instance death or disease progression), survival curves are used to describe the risk of the event over time. In meta-analyses of studies reporting a survival curve, the most informative finding is a summary survival curve. In this paper, we propose a method to obtain a distribution-free summary survival curve by expanding the product-limit estimator of survival for aggregated survival data. The extension of DerSimonian and Laird's methodology for multiple outcomes is applied to account for the between-study heterogeneity. Statistics I(2)  and H(2) are used to quantify the impact of the heterogeneity in the published survival curves. A statistical test for between-strata comparison is proposed, with the aim to explore study-level factors potentially associated with survival. The performance of the proposed approach is evaluated in a simulation study. Our approach is also applied to synthesize the survival of untreated patients with hepatocellular carcinoma from aggregate data of 27 studies and synthesize the graft survival of kidney transplant recipients from individual data from six hospitals. Copyright © 2014 John Wiley & Sons, Ltd.

  8. Modeling the airborne survival of influenza virus in a residential setting: the impacts of home humidification

    Science.gov (United States)

    2010-01-01

    Background Laboratory research studies indicate that aerosolized influenza viruses survive for longer periods at low relative humidity (RH) conditions. Further analysis has shown that absolute humidity (AH) may be an improved predictor of virus survival in the environment. Maintaining airborne moisture levels that reduce survival of the virus in the air and on surfaces could be another tool for managing public health risks of influenza. Methods A multi-zone indoor air quality model was used to evaluate the ability of portable humidifiers to control moisture content of the air and the potential related benefit of decreasing survival of influenza viruses in single-family residences. We modeled indoor AH and influenza virus concentrations during winter months (Northeast US) using the CONTAM multi-zone indoor air quality model. A two-story residential template was used under two different ventilation conditions - forced hot air and radiant heating. Humidity was evaluated on a room-specific and whole house basis. Estimates of emission rates for influenza virus were particle-size specific and derived from published studies and included emissions during both tidal breathing and coughing events. The survival of the influenza virus was determined based on the established relationship between AH and virus survival. Results The presence of a portable humidifier with an output of 0.16 kg water per hour in the bedroom resulted in an increase in median sleeping hours AH/RH levels of 11 to 19% compared to periods without a humidifier present. The associated percent decrease in influenza virus survival was 17.5 - 31.6%. Distribution of water vapor through a residence was estimated to yield 3 to 12% increases in AH/RH and 7.8-13.9% reductions in influenza virus survival. Conclusion This modeling analysis demonstrates the potential benefit of portable residential humidifiers in reducing the survival of aerosolized influenza virus by controlling humidity indoors. PMID:20815876

  9. Modeling the airborne survival of influenza virus in a residential setting: the impacts of home humidification

    Directory of Open Access Journals (Sweden)

    Myatt Theodore A

    2010-09-01

    Full Text Available Abstract Background Laboratory research studies indicate that aerosolized influenza viruses survive for longer periods at low relative humidity (RH conditions. Further analysis has shown that absolute humidity (AH may be an improved predictor of virus survival in the environment. Maintaining airborne moisture levels that reduce survival of the virus in the air and on surfaces could be another tool for managing public health risks of influenza. Methods A multi-zone indoor air quality model was used to evaluate the ability of portable humidifiers to control moisture content of the air and the potential related benefit of decreasing survival of influenza viruses in single-family residences. We modeled indoor AH and influenza virus concentrations during winter months (Northeast US using the CONTAM multi-zone indoor air quality model. A two-story residential template was used under two different ventilation conditions - forced hot air and radiant heating. Humidity was evaluated on a room-specific and whole house basis. Estimates of emission rates for influenza virus were particle-size specific and derived from published studies and included emissions during both tidal breathing and coughing events. The survival of the influenza virus was determined based on the established relationship between AH and virus survival. Results The presence of a portable humidifier with an output of 0.16 kg water per hour in the bedroom resulted in an increase in median sleeping hours AH/RH levels of 11 to 19% compared to periods without a humidifier present. The associated percent decrease in influenza virus survival was 17.5 - 31.6%. Distribution of water vapor through a residence was estimated to yield 3 to 12% increases in AH/RH and 7.8-13.9% reductions in influenza virus survival. Conclusion This modeling analysis demonstrates the potential benefit of portable residential humidifiers in reducing the survival of aerosolized influenza virus by controlling humidity

  10. Connecting single-stock assessment models through correlated survival

    DEFF Research Database (Denmark)

    Albertsen, Christoffer Moesgaard; Nielsen, Anders; Thygesen, Uffe Høgsbro

    2017-01-01

    times. We propose a simple alternative. In three case studies each with two stocks, we improve the single-stock models, as measured by Akaike information criterion, by adding correlation in the cohort survival. To limit the number of parameters, the correlations are parameterized through...... the corresponding partial correlations. We consider six models where the partial correlation matrix between stocks follows a band structure ranging from independent assessments to complex correlation structures. Further, a simulation study illustrates the importance of handling correlated data sufficiently...... by investigating the coverage of confidence intervals for estimated fishing mortality. The results presented will allow managers to evaluate stock statuses based on a more accurate evaluation of model output uncertainty. The methods are directly implementable for stocks with an analytical assessment and do...

  11. The application of cure models in the presence of competing risks: a tool for improved risk communication in population-based cancer patient survival.

    Science.gov (United States)

    Eloranta, Sandra; Lambert, Paul C; Andersson, Therese M-L; Björkholm, Magnus; Dickman, Paul W

    2014-09-01

    Quantifying cancer patient survival from the perspective of cure is clinically relevant. However, most cure models estimate cure assuming no competing causes of death. We use a relative survival framework to demonstrate how flexible parametric cure models can be used in combination with competing-risks theory to incorporate noncancer deaths. Under a model that incorporates statistical cure, we present the probabilities that cancer patients (1) have died from their cancer, (2) have died from other causes, (3) will eventually die from their cancer, or (4) will eventually die from other causes, all as a function of time since diagnosis. We further demonstrate how conditional probabilities can be used to update the prognosis among survivors (eg, at 1 or 5 years after diagnosis) by summarizing the proportion of patients who will not die from their cancer. The proposed method is applied to Swedish population-based data for persons diagnosed with melanoma, colon cancer, or acute myeloid leukemia between 1973 and 2007.

  12. Survival analysis for customer satisfaction: A case study

    Science.gov (United States)

    Hadiyat, M. A.; Wahyudi, R. D.; Sari, Y.

    2017-11-01

    Most customer satisfaction surveys are conducted periodically to track their dynamics. One of the goals of this survey was to evaluate the service design by recognizing the trend of satisfaction score. Many researchers recommended in redesigning the service when the satisfaction scores were decreasing, so that the service life cycle could be predicted qualitatively. However, these scores were usually set in Likert scale and had quantitative properties. Thus, they should also be analyzed in quantitative model so that the predicted service life cycle would be done by applying the survival analysis. This paper discussed a starting point for customer satisfaction survival analysis with a case study in healthcare service.

  13. The design and analysis of salmonid tagging studies in the Columbia basin. Volume 8: A new model for estimating survival probabilities and residualization from a release-recapture study of fall chinook salmon (Oncorhynchus tschawytscha) smolts in the Snake River

    International Nuclear Information System (INIS)

    Lowther, A.B.; Skalski, J.

    1997-09-01

    Standard release-recapture analysis using Cormack-Jolly-Seber (CJS) models to estimate survival probabilities between hydroelectric facilities for Snake river fall chinook salmon (Oncorhynchus tschawytscha) ignore the possibility of individual fish residualizing and completing their migration in the year following tagging. These models do not utilize available capture history data from this second year and, thus, produce negatively biased estimates of survival probabilities. A new multinomial likelihood model was developed that results in biologically relevant, unbiased estimates of survival probabilities using the full two years of capture history data. This model was applied to 1995 Snake River fall chinook hatchery releases to estimate the true survival probability from one of three upstream release points (Asotin, Billy Creek, and Pittsburgh Landing) to Lower Granite Dam. In the data analyzed here, residualization is not a common physiological response and thus the use of CJS models did not result in appreciably different results than the true survival probability obtained using the new multinomial likelihood model

  14. A pioneering healthcare model applying large-scale production concepts: Principles and performance after more than 11,000 transplants at Hospital do Rim

    Directory of Open Access Journals (Sweden)

    José Medina Pestana

    Full Text Available Summary The kidney transplant program at Hospital do Rim (hrim is a unique healthcare model that applies the same principles of repetition of processes used in industrial production. This model, devised by Frederick Taylor, is founded on principles of scientific management that involve planning, rational execution of work, and distribution of responsibilities. The expected result is increased efficiency, improvement of results and optimization of resources. This model, almost completely subsidized by the Unified Health System (SUS, in the Portuguese acronym, has been used at the hrim in more than 11,000 transplants over the last 18 years. The hrim model consists of eight interconnected modules: organ procurement organization, preparation for the transplant, admission for transplant, surgical procedure, post-operative period, outpatient clinic, support units, and coordination and quality control. The flow of medical activities enables organized and systematic care of all patients. The improvement of the activities in each module is constant, with full monitoring of various administrative, health care, and performance indicators. The continuous improvement in clinical results confirms the efficiency of the program. Between 1998 and 2015, an increase was noted in graft survival (77.4 vs. 90.4%, p<0.001 and patient survival (90.5 vs. 95.1%, p=0.001. The high productivity, efficiency, and progressive improvement of the results obtained with this model suggest that it could be applied to other therapeutic areas that require large-scale care, preserving the humanistic characteristic of providing health care activity.

  15. A pioneering healthcare model applying large-scale production concepts: Principles and performance after more than 11,000 transplants at Hospital do Rim.

    Science.gov (United States)

    Pestana, José Medina

    2016-10-01

    The kidney transplant program at Hospital do Rim (hrim) is a unique healthcare model that applies the same principles of repetition of processes used in industrial production. This model, devised by Frederick Taylor, is founded on principles of scientific management that involve planning, rational execution of work, and distribution of responsibilities. The expected result is increased efficiency, improvement of results and optimization of resources. This model, almost completely subsidized by the Unified Health System (SUS, in the Portuguese acronym), has been used at the hrim in more than 11,000 transplants over the last 18 years. The hrim model consists of eight interconnected modules: organ procurement organization, preparation for the transplant, admission for transplant, surgical procedure, post-operative period, outpatient clinic, support units, and coordination and quality control. The flow of medical activities enables organized and systematic care of all patients. The improvement of the activities in each module is constant, with full monitoring of various administrative, health care, and performance indicators. The continuous improvement in clinical results confirms the efficiency of the program. Between 1998 and 2015, an increase was noted in graft survival (77.4 vs. 90.4%, p<0.001) and patient survival (90.5 vs. 95.1%, p=0.001). The high productivity, efficiency, and progressive improvement of the results obtained with this model suggest that it could be applied to other therapeutic areas that require large-scale care, preserving the humanistic characteristic of providing health care activity.

  16. Rethinking plant functional types in Earth System Models: pan-tropical analysis of tree survival across environmental gradients

    Science.gov (United States)

    Johnson, D. J.; Needham, J.; Xu, C.; Davies, S. J.; Bunyavejchewin, S.; Giardina, C. P.; Condit, R.; Cordell, S.; Litton, C. M.; Hubbell, S.; Kassim, A. R. B.; Shawn, L. K. Y.; Nasardin, M. B.; Ong, P.; Ostertag, R.; Sack, L.; Tan, S. K. S.; Yap, S.; McDowell, N. G.; McMahon, S.

    2016-12-01

    Terrestrial carbon cycling is a function of the growth and survival of trees. Current model representations of tree growth and survival at a global scale rely on coarse plant functional traits that are parameterized very generally. In view of the large biodiversity in the tropical forests, it is important that we account for the functional diversity in order to better predict tropical forest responses to future climate changes. Several next generation Earth System Models are moving towards a size-structured, trait-based approach to modelling vegetation globally, but the challenge of which and how many traits are necessary to capture forest complexity remains. Additionally, the challenge of collecting sufficient trait data to describe the vast species richness of tropical forests is enormous. We propose a more fundamental approach to these problems by characterizing forests by their patterns of survival. We expect our approach to distill real-world tree survival into a reasonable number of functional types. Using 10 large-area tropical forest plots that span geographic, edaphic and climatic gradients, we model tree survival as a function of tree size for hundreds of species. We found surprisingly few categories of size-survival functions emerge. This indicates some fundamental strategies at play across diverse forests to constrain the range of possible size-survival functions. Initial cluster analysis indicates that four to eight functional forms are necessary to describe variation in size-survival relations. Temporal variation in size-survival functions can be related to local environmental variation, allowing us to parameterize how demographically similar groups of species respond to perturbations in the ecosystem. We believe this methodology will yield a synthetic approach to classifying forest systems that will greatly reduce uncertainty and complexity in global vegetation models.

  17. Survival models for harvest management of mourning dove populations

    Science.gov (United States)

    Otis, D.L.

    2002-01-01

    Quantitative models of the relationship between annual survival and harvest rate of migratory game-bird populations are essential to science-based harvest management strategies. I used the best available band-recovery and harvest data for mourning doves (Zenaida macroura) to build a set of models based on different assumptions about compensatory harvest mortality. Although these models suffer from lack of contemporary data, they can be used in development of an initial set of population models that synthesize existing demographic data on a management-unit scale, and serve as a tool for prioritization of population demographic information needs. Credible harvest management plans for mourning dove populations will require a long-term commitment to population monitoring and iterative population analysis.

  18. [The survival prediction model of advanced gallbladder cancer based on Bayesian network: a multi-institutional study].

    Science.gov (United States)

    Tang, Z H; Geng, Z M; Chen, C; Si, S B; Cai, Z Q; Song, T Q; Gong, P; Jiang, L; Qiu, Y H; He, Y; Zhai, W L; Li, S P; Zhang, Y C; Yang, Y

    2018-05-01

    Objective: To investigate the clinical value of Bayesian network in predicting survival of patients with advanced gallbladder cancer(GBC)who underwent curative intent surgery. Methods: The clinical data of patients with advanced GBC who underwent curative intent surgery in 9 institutions from January 2010 to December 2015 were analyzed retrospectively.A median survival time model based on a tree augmented naïve Bayes algorithm was established by Bayesia Lab software.The survival time, number of metastatic lymph nodes(NMLN), T stage, pathological grade, margin, jaundice, liver invasion, age, sex and tumor morphology were included in this model.Confusion matrix, the receiver operating characteristic curve and area under the curve were used to evaluate the accuracy of the model.A priori statistical analysis of these 10 variables and a posterior analysis(survival time as the target variable, the remaining factors as the attribute variables)was performed.The importance rankings of each variable was calculated with the polymorphic Birnbaum importance calculation based on the posterior analysis results.The survival probability forecast table was constructed based on the top 4 prognosis factors. The survival curve was drawn by the Kaplan-Meier method, and differences in survival curves were compared using the Log-rank test. Results: A total of 316 patients were enrolled, including 109 males and 207 females.The ratio of male to female was 1.0∶1.9, the age was (62.0±10.8)years.There was 298 cases(94.3%) R0 resection and 18 cases(5.7%) R1 resection.T staging: 287 cases(90.8%) T3 and 29 cases(9.2%) T4.The median survival time(MST) was 23.77 months, and the 1, 3, 5-year survival rates were 67.4%, 40.8%, 32.0%, respectively.For the Bayesian model, the number of correctly predicted cases was 121(≤23.77 months) and 115(>23.77 months) respectively, leading to a 74.86% accuracy of this model.The prior probability of survival time was 0.503 2(≤23.77 months) and 0.496 8

  19. Regression models for interval censored survival data: Application to HIV infection in Danish homosexual men

    DEFF Research Database (Denmark)

    Carstensen, Bendix

    1996-01-01

    This paper shows how to fit excess and relative risk regression models to interval censored survival data, and how to implement the models in standard statistical software. The methods developed are used for the analysis of HIV infection rates in a cohort of Danish homosexual men.......This paper shows how to fit excess and relative risk regression models to interval censored survival data, and how to implement the models in standard statistical software. The methods developed are used for the analysis of HIV infection rates in a cohort of Danish homosexual men....

  20. Revealing the equivalence of two clonal survival models by principal component analysis

    International Nuclear Information System (INIS)

    Lachet, Bernard; Dufour, Jacques

    1976-01-01

    The principal component analysis of 21 chlorella cell survival curves, adjusted by one-hit and two-hit target models, lead to quite similar projections on the principal plan: the homologous parameters of these models are linearly correlated; the reason for the statistical equivalence of these two models, in the present state of experimental inaccuracy, is revealed [fr

  1. Survival behavior in the cyclic Lotka-Volterra model with a randomly switching reaction rate.

    Science.gov (United States)

    West, Robert; Mobilia, Mauro; Rucklidge, Alastair M

    2018-02-01

    We study the influence of a randomly switching reproduction-predation rate on the survival behavior of the nonspatial cyclic Lotka-Volterra model, also known as the zero-sum rock-paper-scissors game, used to metaphorically describe the cyclic competition between three species. In large and finite populations, demographic fluctuations (internal noise) drive two species to extinction in a finite time, while the species with the smallest reproduction-predation rate is the most likely to be the surviving one (law of the weakest). Here we model environmental (external) noise by assuming that the reproduction-predation rate of the strongest species (the fastest to reproduce and predate) in a given static environment randomly switches between two values corresponding to more and less favorable external conditions. We study the joint effect of environmental and demographic noise on the species survival probabilities and on the mean extinction time. In particular, we investigate whether the survival probabilities follow the law of the weakest and analyze their dependence on the external noise intensity and switching rate. Remarkably, when, on average, there is a finite number of switches prior to extinction, the survival probability of the predator of the species whose reaction rate switches typically varies nonmonotonically with the external noise intensity (with optimal survival about a critical noise strength). We also outline the relationship with the case where all reaction rates switch on markedly different time scales.

  2. Survival behavior in the cyclic Lotka-Volterra model with a randomly switching reaction rate

    Science.gov (United States)

    West, Robert; Mobilia, Mauro; Rucklidge, Alastair M.

    2018-02-01

    We study the influence of a randomly switching reproduction-predation rate on the survival behavior of the nonspatial cyclic Lotka-Volterra model, also known as the zero-sum rock-paper-scissors game, used to metaphorically describe the cyclic competition between three species. In large and finite populations, demographic fluctuations (internal noise) drive two species to extinction in a finite time, while the species with the smallest reproduction-predation rate is the most likely to be the surviving one (law of the weakest). Here we model environmental (external) noise by assuming that the reproduction-predation rate of the strongest species (the fastest to reproduce and predate) in a given static environment randomly switches between two values corresponding to more and less favorable external conditions. We study the joint effect of environmental and demographic noise on the species survival probabilities and on the mean extinction time. In particular, we investigate whether the survival probabilities follow the law of the weakest and analyze their dependence on the external noise intensity and switching rate. Remarkably, when, on average, there is a finite number of switches prior to extinction, the survival probability of the predator of the species whose reaction rate switches typically varies nonmonotonically with the external noise intensity (with optimal survival about a critical noise strength). We also outline the relationship with the case where all reaction rates switch on markedly different time scales.

  3. Survival associated pathway identification with group Lp penalized global AUC maximization

    Directory of Open Access Journals (Sweden)

    Liu Zhenqiu

    2010-08-01

    Full Text Available Abstract It has been demonstrated that genes in a cell do not act independently. They interact with one another to complete certain biological processes or to implement certain molecular functions. How to incorporate biological pathways or functional groups into the model and identify survival associated gene pathways is still a challenging problem. In this paper, we propose a novel iterative gradient based method for survival analysis with group Lp penalized global AUC summary maximization. Unlike LASSO, Lp (p 1. We first extend Lp for individual gene identification to group Lp penalty for pathway selection, and then develop a novel iterative gradient algorithm for penalized global AUC summary maximization (IGGAUCS. This method incorporates the genetic pathways into global AUC summary maximization and identifies survival associated pathways instead of individual genes. The tuning parameters are determined using 10-fold cross validation with training data only. The prediction performance is evaluated using test data. We apply the proposed method to survival outcome analysis with gene expression profile and identify multiple pathways simultaneously. Experimental results with simulation and gene expression data demonstrate that the proposed procedures can be used for identifying important biological pathways that are related to survival phenotype and for building a parsimonious model for predicting the survival times.

  4. A Survival Model for Shortleaf Pine Tress Growing in Uneven-Aged Stands

    Science.gov (United States)

    Thomas B. Lynch; Lawrence R. Gering; Michael M. Huebschmann; Paul A. Murphy

    1999-01-01

    A survival model for shortleaf pine (Pinus echinata Mill.) trees growing in uneven-aged stands was developed using data from permanently established plots maintained by an industrial forestry company in western Arkansas. Parameters were fitted to a logistic regression model with a Bernoulli dependent variable in which "0" represented...

  5. Survival Function Analysis of Planet Size Distribution

    OpenAIRE

    Zeng, Li; Jacobsen, Stein B.; Sasselov, Dimitar D.; Vanderburg, Andrew

    2018-01-01

    Applying the survival function analysis to the planet radius distribution of the Kepler exoplanet candidates, we have identified two natural divisions of planet radius at 4 Earth radii and 10 Earth radii. These divisions place constraints on planet formation and interior structure model. The division at 4 Earth radii separates small exoplanets from large exoplanets above. When combined with the recently-discovered radius gap at 2 Earth radii, it supports the treatment of planets 2-4 Earth rad...

  6. Bone-Marrow Stem-Cell Survival in the Non-Uniformly Exposed Mammal

    Energy Technology Data Exchange (ETDEWEB)

    Bond, V. P.; Robinson, C. V. [Brookhaven National Laboratory, Medical Research Center, Upton, Long Island, NY (United States)

    1967-07-15

    For comparison of the effectiveness of non-uniform versus uniform irradiations in causing haematological death in mammals, a model of the irradiated haemopoietic system has been proposed. The essential features of this model are: (1) that different parts of the haemopoietic system have numbers of stem cells which are proportioned to the amounts of active marrow in those parts as measured by {sup 59}Fe uptake, (2) that stem cells in the different parts are subject to the, same dose-survival relationship, and (3) that survival of the animal depends on survival of a critical fraction of the total number of stem cells independent of their distribution among the parts of the total marrow mass. To apply this model one needs to know: (a) the relative {sup 59}Fe uptakes of the different parts of the haemopoietic system, (b) the doses delivered to those parts by each of the exposures to be compared, and (c) the dose-survival curve applicable to the stem cells. From these one can calculate the fraction of stem cells surviving each exposure. In a preliminary communication the applicability of the model was investigated using data obtained entirely from the literature. Additional data, particularly on bone-marrow distribution, have since been obtained and are included here. The primary object of the present paper is to test further the validity of the above 'stem-cell survival model'. Data on bilateral (essentially uniform) versus unilateral and non-uniform rotational exposures in mammals are examined with respect to the surviving fraction of stem cells at the LD{sub 50/30} day dose level. Although an adequate test is not possible at present for lack of a full set of data in any one species, a partial test indicates compatibility with data for dogs and rats. Other possible mortality determinants such as doses or exposures at entrance, midline or exit, or the gram-rads or average dose to the marrow, appear to be less useful than the critical stem-cell survival fraction.

  7. Analyzing sickness absence with statistical models for survival data

    DEFF Research Database (Denmark)

    Christensen, Karl Bang; Andersen, Per Kragh; Smith-Hansen, Lars

    2007-01-01

    OBJECTIVES: Sickness absence is the outcome in many epidemiologic studies and is often based on summary measures such as the number of sickness absences per year. In this study the use of modern statistical methods was examined by making better use of the available information. Since sickness...... absence data deal with events occurring over time, the use of statistical models for survival data has been reviewed, and the use of frailty models has been proposed for the analysis of such data. METHODS: Three methods for analyzing data on sickness absences were compared using a simulation study...... involving the following: (i) Poisson regression using a single outcome variable (number of sickness absences), (ii) analysis of time to first event using the Cox proportional hazards model, and (iii) frailty models, which are random effects proportional hazards models. Data from a study of the relation...

  8. Xenograft survival in two species combinations using total-lymphoid irradiation and cyclosporine

    International Nuclear Information System (INIS)

    Knechtle, S.J.; Halperin, E.C.; Bollinger, R.R.

    1987-01-01

    Total lymphoid irradiation (TLI) has profound immunosuppressive actions and has been applied successfully to allotransplantation but not xenotransplantation. Cyclosporine (CsA) has not generally permitted successful xenotransplantation of organs but has not been used in combination with TLI. TLI and CsA were given alone and in combination to rats that were recipients of hamster or rabbit cardiac xenografts. Combined TLI and CsA prolonged survival of hamster-to-rat cardiac xenografts from three days in untreated controls to greater than 100 days in most recipients. TLI alone significantly prolonged rabbit to rat xenograft survival with doubling of survival time. However, combined treatment did not significantly prolong rabbit-to-rat cardiac xenograft survival compared with TLI alone. The hamster and rat are phylogenetically closely related. Transplants from hamsters to rat are concordant xenografts since the time course of unmodified rejection is similar to first-set rejection of allografts. Although the rabbit-to-rat transplant is also between concordant species (average survival of untreated controls: 3.2 days) the rabbit and rat are more distantly related. These results suggest that TLI is an effective immunosuppressant when applied to cardiac xenotransplants in these animal models; that the choice of species critically affects xenograft survival when TLI and/or CsA are used for immunosuppression; and that the closely related species combination tested has markedly prolonged (greater than 100 days) survival using combined TLI and CsA

  9. Prediction of lung cancer patient survival via supervised machine learning classification techniques.

    Science.gov (United States)

    Lynch, Chip M; Abdollahi, Behnaz; Fuqua, Joshua D; de Carlo, Alexandra R; Bartholomai, James A; Balgemann, Rayeanne N; van Berkel, Victor H; Frieboes, Hermann B

    2017-12-01

    Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time

  10. Statistical modelling of survival data with random effects h-likelihood approach

    CERN Document Server

    Ha, Il Do; Lee, Youngjo

    2017-01-01

    This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions. The approach presented in the book overcomes shortcomings in the traditional likelihood-based methods for clustered survival data such as intractable integration. The text includes technical materials such as derivations and proofs in each chapter, as well as recently developed software programs in R (“frailtyHL”), while the real-world data examples together with an R package, “frailtyHL” in CRAN, provide readers with useful hands-on tools. Reviewing new developments since the introduction of the h-likelihood to survival analysis (methods for interval estimation of the individual frailty and for variable selection of the fixed effects in the general class of frailty models) and guiding future directions, the book is of interest to research...

  11. Genetic parameters and factors influencing survival to 24 hrs after birth in Danish meat sheep breeds

    DEFF Research Database (Denmark)

    Maxa, J; Sharifi, A R; Pedersen, J

    2009-01-01

    In this study, influential factors and (co)variance components for survival to 24 h after birth were determined and estimated for Texel, Shropshire, and Oxford Down, the most common sheep breeds in Denmark. Data from 1992 to 2006 containing 138,813 survival records were extracted from the sheep...... recording database at the Danish Agricultural Advisory Service. Estimation of (co)variance components was carried out using univariate animal models, applying logistic link functions. The logistic functions were also used for estimation of fixed effects. Both direct and maternal additive genetic effects......, as well as common litter effects, were included in the models. The mean survival to 24 h after birth was 92.5, 91.7, and 88.5% for Texel, Shropshire, and Oxford Down, respectively. There was a curvilinear relationship between survival to 24 h after birth and birth weight, with survival less for light...

  12. Regulatory activity based risk model identifies survival of stage II and III colorectal carcinoma.

    Science.gov (United States)

    Liu, Gang; Dong, Chuanpeng; Wang, Xing; Hou, Guojun; Zheng, Yu; Xu, Huilin; Zhan, Xiaohui; Liu, Lei

    2017-11-17

    Clinical and pathological indicators are inadequate for prognosis of stage II and III colorectal carcinoma (CRC). In this study, we utilized the activity of regulatory factors, univariate Cox regression and random forest for variable selection and developed a multivariate Cox model to predict the overall survival of Stage II/III colorectal carcinoma in GSE39582 datasets (469 samples). Patients in low-risk group showed a significant longer overall survival and recurrence-free survival time than those in high-risk group. This finding was further validated in five other independent datasets (GSE14333, GSE17536, GSE17537, GSE33113, and GSE37892). Besides, associations between clinicopathological information and risk score were analyzed. A nomogram including risk score was plotted to facilitate the utilization of risk score. The risk score model is also demonstrated to be effective on predicting both overall and recurrence-free survival of chemotherapy received patients. After performing Gene Set Enrichment Analysis (GSEA) between high and low risk groups, we found that several cell-cell interaction KEGG pathways were identified. Funnel plot results showed that there was no publication bias in these datasets. In summary, by utilizing the regulatory activity in stage II and III colorectal carcinoma, the risk score successfully predicts the survival of 1021 stage II/III CRC patients in six independent datasets.

  13. A comparative study of generalized linear mixed modelling and artificial neural network approach for the joint modelling of survival and incidence of Dengue patients in Sri Lanka

    Science.gov (United States)

    Hapugoda, J. C.; Sooriyarachchi, M. R.

    2017-09-01

    Survival time of patients with a disease and the incidence of that particular disease (count) is frequently observed in medical studies with the data of a clustered nature. In many cases, though, the survival times and the count can be correlated in a way that, diseases that occur rarely could have shorter survival times or vice versa. Due to this fact, joint modelling of these two variables will provide interesting and certainly improved results than modelling these separately. Authors have previously proposed a methodology using Generalized Linear Mixed Models (GLMM) by joining the Discrete Time Hazard model with the Poisson Regression model to jointly model survival and count model. As Aritificial Neural Network (ANN) has become a most powerful computational tool to model complex non-linear systems, it was proposed to develop a new joint model of survival and count of Dengue patients of Sri Lanka by using that approach. Thus, the objective of this study is to develop a model using ANN approach and compare the results with the previously developed GLMM model. As the response variables are continuous in nature, Generalized Regression Neural Network (GRNN) approach was adopted to model the data. To compare the model fit, measures such as root mean square error (RMSE), absolute mean error (AME) and correlation coefficient (R) were used. The measures indicate the GRNN model fits the data better than the GLMM model.

  14. Applying the WEAP Model to Water Resource

    DEFF Research Database (Denmark)

    Gao, Jingjing; Christensen, Per; Li, Wei

    efficiency, treatment and reuse of water. The WEAP model was applied to the Ordos catchment where it was used for the first time in China. The changes in water resource utilization in Ordos basin were assessed with the model. It was found that the WEAP model is a useful tool for water resource assessment......Water resources assessment is a tool to provide decision makers with an appropriate basis to make informed judgments regarding the objectives and targets to be addressed during the Strategic Environmental Assessment (SEA) process. The study shows how water resources assessment can be applied in SEA...... in assessing the effects on water resources using a case study on a Coal Industry Development Plan in an arid region in North Western China. In the case the WEAP model (Water Evaluation And Planning System) were used to simulate various scenarios using a diversity of technological instruments like irrigation...

  15. Applied Integer Programming Modeling and Solution

    CERN Document Server

    Chen, Der-San; Dang, Yu

    2011-01-01

    An accessible treatment of the modeling and solution of integer programming problems, featuring modern applications and software In order to fully comprehend the algorithms associated with integer programming, it is important to understand not only how algorithms work, but also why they work. Applied Integer Programming features a unique emphasis on this point, focusing on problem modeling and solution using commercial software. Taking an application-oriented approach, this book addresses the art and science of mathematical modeling related to the mixed integer programming (MIP) framework and

  16. Immune phenotypes predict survival in patients with glioblastoma multiforme

    Directory of Open Access Journals (Sweden)

    Haouraa Mostafa

    2016-09-01

    Full Text Available Abstract Background Glioblastoma multiforme (GBM, a common primary malignant brain tumor, rarely disseminates beyond the central nervous system and has a very bad prognosis. The current study aimed at the analysis of immunological control in individual patients with GBM. Methods Immune phenotypes and plasma biomarkers of GBM patients were determined at the time of diagnosis using flow cytometry and ELISA, respectively. Results Using descriptive statistics, we found that immune anomalies were distinct in individual patients. Defined marker profiles proved highly relevant for survival. A remarkable relation between activated NK cells and improved survival in GBM patients was in contrast to increased CD39 and IL-10 in patients with a detrimental course and very short survival. Recursive partitioning analysis (RPA and Cox proportional hazards models substantiated the relevance of absolute numbers of CD8 cells and low numbers of CD39 cells for better survival. Conclusions Defined alterations of the immune system may guide the course of disease in patients with GBM and may be prognostically valuable for longitudinal studies or can be applied for immune intervention.

  17. Mechanisms and mediation in survival analysis: towards an integrated analytical framework

    Directory of Open Access Journals (Sweden)

    Jonathan Pratschke

    2016-02-01

    Full Text Available Abstract Background A wide-ranging debate has taken place in recent years on mediation analysis and causal modelling, raising profound theoretical, philosophical and methodological questions. The authors build on the results of these discussions to work towards an integrated approach to the analysis of research questions that situate survival outcomes in relation to complex causal pathways with multiple mediators. The background to this contribution is the increasingly urgent need for policy-relevant research on the nature of inequalities in health and healthcare. Methods The authors begin by summarising debates on causal inference, mediated effects and statistical models, showing that these three strands of research have powerful synergies. They review a range of approaches which seek to extend existing survival models to obtain valid estimates of mediation effects. They then argue for an alternative strategy, which involves integrating survival outcomes within Structural Equation Models via the discrete-time survival model. This approach can provide an integrated framework for studying mediation effects in relation to survival outcomes, an issue of great relevance in applied health research. The authors provide an example of how these techniques can be used to explore whether the social class position of patients has a significant indirect effect on the hazard of death from colon cancer. Results The results suggest that the indirect effects of social class on survival are substantial and negative (-0.23 overall. In addition to the substantial direct effect of this variable (-0.60, its indirect effects account for more than one quarter of the total effect. The two main pathways for this indirect effect, via emergency admission (-0.12, on the one hand, and hospital caseload, on the other, (-0.10 are of similar size. Conclusions The discrete-time survival model provides an attractive way of integrating time-to-event data within the field of

  18. Mechanisms and mediation in survival analysis: towards an integrated analytical framework.

    Science.gov (United States)

    Pratschke, Jonathan; Haase, Trutz; Comber, Harry; Sharp, Linda; de Camargo Cancela, Marianna; Johnson, Howard

    2016-02-29

    A wide-ranging debate has taken place in recent years on mediation analysis and causal modelling, raising profound theoretical, philosophical and methodological questions. The authors build on the results of these discussions to work towards an integrated approach to the analysis of research questions that situate survival outcomes in relation to complex causal pathways with multiple mediators. The background to this contribution is the increasingly urgent need for policy-relevant research on the nature of inequalities in health and healthcare. The authors begin by summarising debates on causal inference, mediated effects and statistical models, showing that these three strands of research have powerful synergies. They review a range of approaches which seek to extend existing survival models to obtain valid estimates of mediation effects. They then argue for an alternative strategy, which involves integrating survival outcomes within Structural Equation Models via the discrete-time survival model. This approach can provide an integrated framework for studying mediation effects in relation to survival outcomes, an issue of great relevance in applied health research. The authors provide an example of how these techniques can be used to explore whether the social class position of patients has a significant indirect effect on the hazard of death from colon cancer. The results suggest that the indirect effects of social class on survival are substantial and negative (-0.23 overall). In addition to the substantial direct effect of this variable (-0.60), its indirect effects account for more than one quarter of the total effect. The two main pathways for this indirect effect, via emergency admission (-0.12), on the one hand, and hospital caseload, on the other, (-0.10) are of similar size. The discrete-time survival model provides an attractive way of integrating time-to-event data within the field of Structural Equation Modelling. The authors demonstrate the efficacy

  19. Geostatistical methods applied to field model residuals

    DEFF Research Database (Denmark)

    Maule, Fox; Mosegaard, K.; Olsen, Nils

    consists of measurement errors and unmodelled signal), and is typically assumed to be uncorrelated and Gaussian distributed. We have applied geostatistical methods to analyse the residuals of the Oersted(09d/04) field model [http://www.dsri.dk/Oersted/Field_models/IGRF_2005_candidates/], which is based...

  20. Molecular modeling: An open invitation for applied mathematics

    Science.gov (United States)

    Mezey, Paul G.

    2013-10-01

    Molecular modeling methods provide a very wide range of challenges for innovative mathematical and computational techniques, where often high dimensionality, large sets of data, and complicated interrelations imply a multitude of iterative approximations. The physical and chemical basis of these methodologies involves quantum mechanics with several non-intuitive aspects, where classical interpretation and classical analogies are often misleading or outright wrong. Hence, instead of the everyday, common sense approaches which work so well in engineering, in molecular modeling one often needs to rely on rather abstract mathematical constraints and conditions, again emphasizing the high level of reliance on applied mathematics. Yet, the interdisciplinary aspects of the field of molecular modeling also generates some inertia and perhaps too conservative reliance on tried and tested methodologies, that is at least partially caused by the less than up-to-date involvement in the newest developments in applied mathematics. It is expected that as more applied mathematicians take up the challenge of employing the latest advances of their field in molecular modeling, important breakthroughs may follow. In this presentation some of the current challenges of molecular modeling are discussed.

  1. Applied Mathematics, Modelling and Computational Science

    CERN Document Server

    Kotsireas, Ilias; Makarov, Roman; Melnik, Roderick; Shodiev, Hasan

    2015-01-01

    The Applied Mathematics, Modelling, and Computational Science (AMMCS) conference aims to promote interdisciplinary research and collaboration. The contributions in this volume cover the latest research in mathematical and computational sciences, modeling, and simulation as well as their applications in natural and social sciences, engineering and technology, industry, and finance. The 2013 conference, the second in a series of AMMCS meetings, was held August 26–30 and organized in cooperation with AIMS and SIAM, with support from the Fields Institute in Toronto, and Wilfrid Laurier University. There were many young scientists at AMMCS-2013, both as presenters and as organizers. This proceedings contains refereed papers contributed by the participants of the AMMCS-2013 after the conference. This volume is suitable for researchers and graduate students, mathematicians and engineers, industrialists, and anyone who would like to delve into the interdisciplinary research of applied and computational mathematics ...

  2. Survival analysis using S analysis of time-to-event data

    CERN Document Server

    Tableman, Mara

    2003-01-01

    Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics. The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s). In a chapter ...

  3. A survival model for fractionated radiotherapy with an application to prostate cancer

    Energy Technology Data Exchange (ETDEWEB)

    Zaider, Marco [Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY (United States)]. E-mail: Zaiderm@mskcc.org; Zelefsky, Michael J.; Leibel, Steven A. [Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY (United States); Hanin, Leonid G. [Department of Mathematics, Idaho State University, Pocatello, ID (United States); Tsodikov, Alexander D.; Yakovlev, Andrei Y. [Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT (United States)

    2001-10-01

    This paper explores the applicability of a mechanistic survival model, based on the distribution of clonogens surviving a course of fractionated radiation therapy, to clinical data on patients with prostate cancer. The study was carried out using data on 1100 patients with clinically localized prostate cancer who were treated with three-dimensional conformal radiation therapy. The patients were stratified by radiation dose (group 1: <67.5 Gy; group 2: 67.5-72.5 Gy; group 3: 72.5-77.5 Gy; group 4: 77.5-87.5 Gy) and prognosis category (favourable, intermediate and unfavourable as defined by pre-treatment PSA and Gleason score). A relapse was recorded when tumour recurrence was diagnosed or when three successive prostate specific antigen (PSA) elevations were observed from a post-treatment nadir PSA level. PSA relapse-free survival was used as the primary end point. The model, which is based on an iterated Yule process, is specified in terms of three parameters: the mean number of tumour clonogens that survive the treatment, the mean of the progression time of post-treatment tumour development and its standard deviation. The model parameters were estimated by the maximum likelihood method. The fact that the proposed model provides an excellent description both of the survivor function and of the hazard rate is prima facie evidence of the validity of the model because closeness of the two survivor functions (empirical and model-based) does not generally imply closeness of the corresponding hazard rates. The estimated cure probabilities for the favourable group are 0.80, 0.74 and 0.87 (for dose groups 1-3, respectively); for the intermediate group: 0.25, 0.51, 0.58 and 0.78 (for dose groups 1-4, respectively) and for the unfavourable group: 0.0, 0.27, 0.33 and 0.64 (for dose groups 1-4, respectively). The distribution of progression time to tumour relapse was found to be independent of prognosis group but dependent on dose. As the dose increases the mean progression

  4. A nonlinear interface model applied to masonry structures

    Science.gov (United States)

    Lebon, Frédéric; Raffa, Maria Letizia; Rizzoni, Raffaella

    2015-12-01

    In this paper, a new imperfect interface model is presented. The model includes finite strains, micro-cracks and smooth roughness. The model is consistently derived by coupling a homogenization approach for micro-cracked media and arguments of asymptotic analysis. The model is applied to brick/mortar interfaces. Numerical results are presented.

  5. Modeling the kinetics of survival of Staphylococcus aureus in regional yogurt from goat's milk.

    Science.gov (United States)

    Bednarko-Młynarczyk, E; Szteyn, J; Białobrzewski, I; Wiszniewska-Łaszczych, A; Liedtke, K

    2015-01-01

    The aim of this study was to determine the kinetics of the survival of the test strain of Staphylococcus aureus in the product investigated. Yogurt samples were contaminated with S. aure to an initial level of 10(3)-10(4) cfu/g. The samples were then stored at four temperatures: 4, 6, 20, 22°C. During storage, the number of S. aureus forming colonies in a gram of yogurt was determined every two hours. Based on the results of the analysis culture the curves of survival were plotted. Three primary models were selected to describe the kinetics of changes in the count of bacteria: Cole's model, a modified model of Gompertz and the model of Baranyi and Roberts. Analysis of the model fit carried out based on the average values of Pearson's correlation coefficient, between the modeled and measured values, showed that the Cole's model had the worst fit. The modified Gompertz model showed the count of S. aureus as a negative value. These drawbacks were not observed in the model of Baranyi and Roberts. For this reason, this model best reflects the kinetics of changes in the number of staphylococci in yogurt.

  6. A critique of Katz's 'High LET constraint on low LET survival'

    International Nuclear Information System (INIS)

    Burch, P.R.J.; Chesters, M.S.

    1979-01-01

    Katz's interpretation of the connexion between RBE and LET is contrasted with a version published previously by Burch. The implications of Katz's model for dose-response relations apply only at ultra-high absorbed doses in Burch's model. In the latter, the shoulder on type-C survival curves for mammalian cells is explained in terms of Haynes' repair model. Under certain conditions the repair model becomes mathematically equivalent to the α-β model; under some other conditions it becomes equivalent to the 'two-component' model. The formulation of a new repair hypothesis, based on the idea of an inducible repair mechanism, is also set out. It is argued that Katz's supralinearity index' is appropriate to the induction of (rare) mutations but inappropriate to cell survival, for which an alternative index is proposed. Certain plausible hypotheses of radiobiological action conflict with Katz's 'logical constraint' which, it is contended, is neither logical nor valid. In conclusion, although experimental findings for some radiobiological systems conform to Katz's 'constraint', the frequently observed violations should not necessarily be regarded as artefacts. (author)

  7. Models for cell survival with low LET radiation

    International Nuclear Information System (INIS)

    Payne, M.G.; Garrett, W.R.

    1975-01-01

    A model for cell survival under low LET irradiation was developed in which the cell is considered to have N 0 -independent sensitive sites, each of which can exist in either an undamaged state (state A) or one of two damaged states. Radiation can change the sensitive sites from the undamaged state to either of two damaged states. The first damaged state (state B) can either be repaired or be promoted on the second damaged state (state C), which is irreparable. The promotion from the first damaged state to the second can occur due to any of the following: (1) further radiation damage, (2) an abortive attempt to repair the site, or (3) the arrival at a part of the cell cycle where the damage is ''fixed.'' Subject to the further assumptions that radiation damage can occur either indirectly (i.e., through radiation products) or due to direct interaction, and that repair of the first damaged state is a one-step process, expressions can be derived for P(N/sub A/, N/sub B/,t) = probability that after time t a cell will have N/sub A/ sites in state A and N/sub B/ in state B. The problem of determining P(N/sub A/, N/sub B/, t) is formulated for arbitrary time dependences of the radiation field and of all rate coefficients. A large family of cell-survival models can be described by interpreting the sensitive sites in different ways and by making different choices of rate coefficients and of the combinations of numbers of sites in different states that will lead to cell death. (U.S.)

  8. The Application of Extended Cox Proportional Hazard Method for Estimating Survival Time of Breast Cancer

    Science.gov (United States)

    Husain, Hartina; Astuti Thamrin, Sri; Tahir, Sulaiha; Mukhlisin, Ahmad; Mirna Apriani, M.

    2018-03-01

    Breast cancer is one type of cancer that is the leading cause of death worldwide. This study aims to model the factors that affect the survival time and rate of cure of breast cancer patients. The extended cox model, which is a modification of the proportional hazard cox model in which the proportional hazard assumptions are not met, is used in this study. The maximum likelihood estimation approach is used to estimate the parameters of the model. This method is then applied to medical record data of breast cancer patient in 2011-2016, which is taken from Hasanuddin University Education Hospital. The results obtained indicate that the factors that affect the survival time of breast cancer patients are malignancy and leukocyte levels.

  9. Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries.

    Science.gov (United States)

    Jochems, Arthur; Deist, Timo M; El Naqa, Issam; Kessler, Marc; Mayo, Chuck; Reeves, Jackson; Jolly, Shruti; Matuszak, Martha; Ten Haken, Randall; van Soest, Johan; Oberije, Cary; Faivre-Finn, Corinne; Price, Gareth; de Ruysscher, Dirk; Lambin, Philippe; Dekker, Andre

    2017-10-01

    Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (PLearning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that

  10. Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis.

    Science.gov (United States)

    Attiyeh, Marc A; Chakraborty, Jayasree; Doussot, Alexandre; Langdon-Embry, Liana; Mainarich, Shiana; Gönen, Mithat; Balachandran, Vinod P; D'Angelica, Michael I; DeMatteo, Ronald P; Jarnagin, William R; Kingham, T Peter; Allen, Peter J; Simpson, Amber L; Do, Richard K

    2018-04-01

    Pancreatic cancer is a highly lethal cancer with no established a priori markers of survival. Existing nomograms rely mainly on post-resection data and are of limited utility in directing surgical management. This study investigated the use of quantitative computed tomography (CT) features to preoperatively assess survival for pancreatic ductal adenocarcinoma (PDAC) patients. A prospectively maintained database identified consecutive chemotherapy-naive patients with CT angiography and resected PDAC between 2009 and 2012. Variation in CT enhancement patterns was extracted from the tumor region using texture analysis, a quantitative image analysis tool previously described in the literature. Two continuous survival models were constructed, with 70% of the data (training set) using Cox regression, first based only on preoperative serum cancer antigen (CA) 19-9 levels and image features (model A), and then on CA19-9, image features, and the Brennan score (composite pathology score; model B). The remaining 30% of the data (test set) were reserved for independent validation. A total of 161 patients were included in the analysis. Training and test sets contained 113 and 48 patients, respectively. Quantitative image features combined with CA19-9 achieved a c-index of 0.69 [integrated Brier score (IBS) 0.224] on the test data, while combining CA19-9, imaging, and the Brennan score achieved a c-index of 0.74 (IBS 0.200) on the test data. We present two continuous survival prediction models for resected PDAC patients. Quantitative analysis of CT texture features is associated with overall survival. Further work includes applying the model to an external dataset to increase the sample size for training and to determine its applicability.

  11. Applying a realistic evaluation model to occupational safety interventions

    DEFF Research Database (Denmark)

    Pedersen, Louise Møller

    2018-01-01

    Background: Recent literature characterizes occupational safety interventions as complex social activities, applied in complex and dynamic social systems. Hence, the actual outcomes of an intervention will vary, depending on the intervention, the implementation process, context, personal characte......Background: Recent literature characterizes occupational safety interventions as complex social activities, applied in complex and dynamic social systems. Hence, the actual outcomes of an intervention will vary, depending on the intervention, the implementation process, context, personal...... and qualitative methods. This revised model has, however, not been applied in a real life context. Method: The model is applied in a controlled, four-component, integrated behaviour-based and safety culture-based safety intervention study (2008-2010) in a medium-sized wood manufacturing company. The interventions...... involve the company’s safety committee, safety manager, safety groups and 130 workers. Results: The model provides a framework for more valid evidence of what works within injury prevention. Affective commitment and role behaviour among key actors are identified as crucial for the implementation...

  12. Selective histone deacetylase 6 inhibition prolongs survival in a lethal two-hit model.

    Science.gov (United States)

    Cheng, Xin; Liu, Zhengcai; Liu, Baoling; Zhao, Ting; Li, Yongqing; Alam, Hasan B

    2015-07-01

    Hemorrhagic shock (HS) followed by a subsequent insult ("second hit") often initiates an exaggerated systemic inflammatory response and multiple organ failure. We have previously demonstrated that valproic acid, a pan histone deacetylase inhibitor, could improve survival in a rodent "two-hit" model. In the present study, our goal was to determine whether selective inhibition of histone deacetylase 6 with Tubastatin A (Tub-A) could prolong survival in a two-hit model where HS was followed by sepsis from cecal ligation and puncture (CLP). C57Bl/6J mice were subjected to sublethal HS (30% blood loss) and then randomly divided into two groups (n = 13 per group) such as Tub-A group (treatment) and vehicle (VEH) group (control). The Tub-A group was given an intraperitoneal injection of Tub-A (70 mg/kg) dissolved in dimethyl sulfoxide (DMSO). The VEH group was injected with DMSO (1 μl/g body weight). After 24 h, all mice were subjected CLP followed immediately by another dose of Tub-A or DMSO. Survival was monitored for 10 d. In a parallel study, peritoneal irrigation fluid and liver tissue from Tub-A- or DMSO-treated mice were collected 3 h after CLP. Enzyme-linked immunosorbent assay was performed to quantify activity of the myeloperoxidase and concentrations of tumor necrosis factor-alpha (TNF-α) and interleukin 6 (IL-6) in the peritoneal irrigation fluid. RNA was isolated from the liver tissue, and real-time polymerase chain reaction was performed to measure relative messenger RNA levels of TNF-α and IL-6. Treatment with Tub-A significantly improved survival compared with that of the control (69.2% versus 15.4%). In addition, Tub-A significantly suppressed myeloperoxidase activity (169.9 ± 8.4 ng/mL versus 70.4 ± 17.4 ng/mL; P hit model. Copyright © 2015 Elsevier Inc. All rights reserved.

  13. Analysis of survival in breast cancer patients by using different parametric models

    Science.gov (United States)

    Enera Amran, Syahila; Asrul Afendi Abdullah, M.; Kek, Sie Long; Afiqah Muhamad Jamil, Siti

    2017-09-01

    In biomedical applications or clinical trials, right censoring was often arising when studying the time to event data. In this case, some individuals are still alive at the end of the study or lost to follow up at a certain time. It is an important issue to handle the censoring data in order to prevent any bias information in the analysis. Therefore, this study was carried out to analyze the right censoring data with three different parametric models; exponential model, Weibull model and log-logistic models. Data of breast cancer patients from Hospital Sultan Ismail, Johor Bahru from 30 December 2008 until 15 February 2017 was used in this study to illustrate the right censoring data. Besides, the covariates included in this study are the time of breast cancer infection patients survive t, age of each patients X1 and treatment given to the patients X2 . In order to determine the best parametric models in analysing survival of breast cancer patients, the performance of each model was compare based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood value using statistical software R. When analysing the breast cancer data, all three distributions were shown consistency of data with the line graph of cumulative hazard function resembles a straight line going through the origin. As the result, log-logistic model was the best fitted parametric model compared with exponential and Weibull model since it has the smallest value in AIC and BIC, also the biggest value in log-likelihood.

  14. Genetic evaluation of calf and heifer survival in Iranian Holstein cattle using linear and threshold models.

    Science.gov (United States)

    Forutan, M; Ansari Mahyari, S; Sargolzaei, M

    2015-02-01

    Calf and heifer survival are important traits in dairy cattle affecting profitability. This study was carried out to estimate genetic parameters of survival traits in female calves at different age periods, until nearly the first calving. Records of 49,583 female calves born during 1998 and 2009 were considered in five age periods as days 1-30, 31-180, 181-365, 366-760 and full period (day 1-760). Genetic components were estimated based on linear and threshold sire models and linear animal models. The models included both fixed effects (month of birth, dam's parity number, calving ease and twin/single) and random effects (herd-year, genetic effect of sire or animal and residual). Rates of death were 2.21, 3.37, 1.97, 4.14 and 12.4% for the above periods, respectively. Heritability estimates were very low ranging from 0.48 to 3.04, 0.62 to 3.51 and 0.50 to 4.24% for linear sire model, animal model and threshold sire model, respectively. Rank correlations between random effects of sires obtained with linear and threshold sire models and with linear animal and sire models were 0.82-0.95 and 0.61-0.83, respectively. The estimated genetic correlations between the five different periods were moderate and only significant for 31-180 and 181-365 (r(g) = 0.59), 31-180 and 366-760 (r(g) = 0.52), and 181-365 and 366-760 (r(g) = 0.42). The low genetic correlations in current study would suggest that survival at different periods may be affected by the same genes with different expression or by different genes. Even though the additive genetic variations of survival traits were small, it might be possible to improve these traits by traditional or genomic selection. © 2014 Blackwell Verlag GmbH.

  15. Prolonged heart xenograft survival using combined total lymphoid irradiation and cyclosporine

    International Nuclear Information System (INIS)

    Knechtle, S.J.; Halperin, E.C.; Saad, T.; Bollinger, R.R.

    1986-01-01

    Total lymphoid irradiation and cyclosporine have profound immunosuppressive properties and permit successful heart allotransplantation. Cyclosporine used alone has not permitted consistently successful transplantation between species in all cases. Total lymphoid irradiation has not been applied to xenotransplantation. The efficacy of total lymphoid irradiation alone and in combination with cyclosporine was examined using an animal model of heart xenotransplantation. Heterotopic heart transplants were performed using inbred Syrian hamsters as donors and Lewis rats as recipients. Total lymphoid irradiation was administered preoperatively over 3 weeks for a total dose of 15 gray. Cyclosporine was started on the day of surgery and was given as a daily intramuscular injection of 2.5, 5, or 10 mg/kg/day until rejection was complete. Neither total lymphoid irradiation nor cyclosporine alone markedly prolonged graft survival. However, combined total lymphoid irradiation and cyclosporine, 5 or 10 mg/kg/day, dramatically prolonged graft survival to greater than 100 days in most recipients. There were no treatment-related deaths. In conclusion, combined total lymphoid irradiation and cyclosporine permit successful long-term survival of heart xenotransplants in this hamster-to-rat model

  16. A predictive model for survival in metastatic cancer patients attending an outpatient palliative radiotherapy clinic

    International Nuclear Information System (INIS)

    Chow, Edward; Fung, KinWah; Panzarella, Tony; Bezjak, Andrea; Danjoux, Cyril; Tannock, Ian

    2002-01-01

    Purpose: To develop a predictive model for survival from the time of presentation in an outpatient palliative radiotherapy clinic. Methods and Materials: Sixteen factors were analyzed prospectively in 395 patients seen in a dedicated palliative radiotherapy clinic in a large tertiary cancer center using Cox's proportional hazards regression model. Results: Six prognostic factors had a statistically significant impact on survival, as follows: primary cancer site, site of metastases, Karnofsky performance score (KPS), and fatigue, appetite, and shortness of breath scores from the modified Edmonton Symptom Assessment Scale. Risk group stratification was performed (1) by assigning weights to the prognostic factors based on their levels of significance, and (2) by the number of risk factors present. The weighting method provided a Survival Prediction Score (SPS), ranging from 0 to 32. The survival probability at 3, 6, and 12 months was 83%, 70%, and 51%, respectively, for patients with SPS ≤13 (n=133); 67%, 41%, and 20% for patients with SPS 14-19 (n=129); and 36%, 18%, and 4% for patients with SPS ≥20 (n=133) (p<0.0001). Corresponding survival probabilities based on number of risk factors were as follows: 85%, 72%, and 52% (≤3 risk factors) (n=98); 68%, 47%, and 24% (4 risk factors) (n=117); and 46%, 24%, and 11% (≥5 factors) (n=180) (p<0.0001). Conclusion: Clinical prognostic factors can be used to predict prognosis among patients attending a palliative radiotherapy clinic. If validated in an independent series of patients, the model can be used to guide clinical decisions, plan supportive services, and allocate resource use

  17. A model of survival following pre-hospital cardiac arrest based on the Victorian Ambulance Cardiac Arrest Register.

    Science.gov (United States)

    Fridman, Masha; Barnes, Vanessa; Whyman, Andrew; Currell, Alex; Bernard, Stephen; Walker, Tony; Smith, Karen L

    2007-11-01

    This study describes the epidemiology of sudden cardiac arrest patients in Victoria, Australia, as captured via the Victorian Ambulance Cardiac Arrest Register (VACAR). We used the VACAR data to construct a new model of out-of-hospital cardiac arrest (OHCA), which was specified in accordance with observed trends. All cases of cardiac arrest in Victoria that were attended by Victorian ambulance services during the period of 2002-2005. Overall survival to hospital discharge was 3.8% among 18,827 cases of OHCA. Survival was 15.7% among 1726 bystander witnessed, adult cardiac arrests of presumed cardiac aetiology, presenting in ventricular fibrillation or ventricular tachycardia (VF/VT), where resuscitation was attempted. In multivariate logistic regression analysis, bystander CPR, cardiac arrest (CA) location, response time, age and sex were predictors of VF/VT, which, in turn, was a strong predictor of survival. The same factors that affected VF/VT made an additional contribution to survival. However, for bystander CPR, CA location and response time this additional contribution was limited to VF/VT patients only. There was no detectable association between survival and age younger than 60 years or response time over 15min. The new model accounts for relationships among predictors of survival. These relationships indicate that interventions such as reduced response times and bystander CPR act in multiple ways to improve survival.

  18. Implementing a novel movement-based approach to inferring parturition and neonate caribou calf survival.

    Directory of Open Access Journals (Sweden)

    Maegwin Bonar

    Full Text Available In ungulates, parturition is correlated with a reduction in movement rate. With advances in movement-based technologies comes an opportunity to develop new techniques to assess reproduction in wild ungulates that are less invasive and reduce biases. DeMars et al. (2013, Ecology and Evolution 3:4149-4160 proposed two promising new methods (individual- and population-based; the DeMars model that use GPS inter-fix step length of adult female caribou (Rangifer tarandus caribou to infer parturition and neonate survival. Our objective was to apply the DeMars model to caribou populations that may violate model assumptions for retrospective analysis of parturition and calf survival. We extended the use of the DeMars model after assigning parturition and calf mortality status by examining herd-wide distributions of parturition date, calf mortality date, and survival. We used the DeMars model to estimate parturition and calf mortality events and compared them with the known parturition and calf mortality events from collared adult females (n = 19. We also used the DeMars model to estimate parturition and calf mortality events for collared female caribou with unknown parturition and calf mortality events (n = 43 and instead derived herd-wide estimates of calf survival as well as distributions of parturition and calf mortality dates and compared them to herd-wide estimates generated from calves fitted with VHF collars (n = 134. For our data, the individual-based method was effective at predicting calf mortality, but was not effective at predicting parturition. The population-based method was more effective at predicting parturition but was not effective at predicting calf mortality. At the herd-level, the predicted distributions of parturition date from both methods differed from each other and from the distribution derived from the parturition dates of VHF-collared calves (log-ranked test: χ2 = 40.5, df = 2, p < 0.01. The predicted distributions of calf

  19. Metabolomics with Nuclear Magnetic Resonance Spectroscopy in a Drosophila melanogaster Model of Surviving Sepsis

    Science.gov (United States)

    Bakalov, Veli; Amathieu, Roland; Triba, Mohamed N.; Clément, Marie-Jeanne; Reyes Uribe, Laura; Le Moyec, Laurence; Kaynar, Ata Murat

    2016-01-01

    Patients surviving sepsis demonstrate sustained inflammation, which has been associated with long-term complications. One of the main mechanisms behind sustained inflammation is a metabolic switch in parenchymal and immune cells, thus understanding metabolic alterations after sepsis may provide important insights to the pathophysiology of sepsis recovery. In this study, we explored metabolomics in a novel Drosophila melanogaster model of surviving sepsis using Nuclear Magnetic Resonance (NMR), to determine metabolite profiles. We used a model of percutaneous infection in Drosophila melanogaster to mimic sepsis. We had three experimental groups: sepsis survivors (infected with Staphylococcus aureus and treated with oral linezolid), sham (pricked with an aseptic needle), and unmanipulated (positive control). We performed metabolic measurements seven days after sepsis. We then implemented metabolites detected in NMR spectra into the MetExplore web server in order to identify the metabolic pathway alterations in sepsis surviving Drosophila. Our NMR metabolomic approach in a Drosophila model of recovery from sepsis clearly distinguished between all three groups and showed two different metabolomic signatures of inflammation. Sham flies had decreased levels of maltose, alanine, and glutamine, while their level of choline was increased. Sepsis survivors had a metabolic signature characterized by decreased glucose, maltose, tyrosine, beta-alanine, acetate, glutamine, and succinate. PMID:28009836

  20. Analytic model of Applied-B ion diode impedance behavior

    International Nuclear Information System (INIS)

    Miller, P.A.; Mendel, C.W. Jr.

    1987-01-01

    An empirical analysis of impedance data from Applied-B ion diodes used in seven inertial confinement fusion research experiments was published recently. The diodes all operated with impedance values well below the Child's-law value. The analysis uncovered an unusual unifying relationship among data from the different experiments. The analysis suggested that closure of the anode-cathode gap by electrode plasma was not a dominant factor in the experiments, but was not able to elaborate the underlying physics. Here we present a new analytic model of Applied-B ion diodes coupled to accelerators. A critical feature of the diode model is based on magnetic insulation theory. The model successfully describes impedance behavior of these diodes and supports stimulating new viewpoints of the physics of Applied-B ion diode operation

  1. The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models.

    Science.gov (United States)

    Browne, William J; Steele, Fiona; Golalizadeh, Mousa; Green, Martin J

    2009-06-01

    We consider the application of Markov chain Monte Carlo (MCMC) estimation methods to random-effects models and in particular the family of discrete time survival models. Survival models can be used in many situations in the medical and social sciences and we illustrate their use through two examples that differ in terms of both substantive area and data structure. A multilevel discrete time survival analysis involves expanding the data set so that the model can be cast as a standard multilevel binary response model. For such models it has been shown that MCMC methods have advantages in terms of reducing estimate bias. However, the data expansion results in very large data sets for which MCMC estimation is often slow and can produce chains that exhibit poor mixing. Any way of improving the mixing will result in both speeding up the methods and more confidence in the estimates that are produced. The MCMC methodological literature is full of alternative algorithms designed to improve mixing of chains and we describe three reparameterization techniques that are easy to implement in available software. We consider two examples of multilevel survival analysis: incidence of mastitis in dairy cattle and contraceptive use dynamics in Indonesia. For each application we show where the reparameterization techniques can be used and assess their performance.

  2. Clustered survival data with left-truncation

    DEFF Research Database (Denmark)

    Eriksson, Frank; Martinussen, Torben; Scheike, Thomas H.

    2015-01-01

    Left-truncation occurs frequently in survival studies, and it is well known how to deal with this for univariate survival times. However, there are few results on how to estimate dependence parameters and regression effects in semiparametric models for clustered survival data with delayed entry....... Surprisingly, existing methods only deal with special cases. In this paper, we clarify different kinds of left-truncation and suggest estimators for semiparametric survival models under specific truncation schemes. The large-sample properties of the estimators are established. Small-sample properties...

  3. Applied probability models with optimization applications

    CERN Document Server

    Ross, Sheldon M

    1992-01-01

    Concise advanced-level introduction to stochastic processes that frequently arise in applied probability. Largely self-contained text covers Poisson process, renewal theory, Markov chains, inventory theory, Brownian motion and continuous time optimization models, much more. Problems and references at chapter ends. ""Excellent introduction."" - Journal of the American Statistical Association. Bibliography. 1970 edition.

  4. The comparison of proportional hazards and accelerated failure time models in analyzing the first birth interval survival data

    Science.gov (United States)

    Faruk, Alfensi

    2018-03-01

    Survival analysis is a branch of statistics, which is focussed on the analysis of time- to-event data. In multivariate survival analysis, the proportional hazards (PH) is the most popular model in order to analyze the effects of several covariates on the survival time. However, the assumption of constant hazards in PH model is not always satisfied by the data. The violation of the PH assumption leads to the misinterpretation of the estimation results and decreasing the power of the related statistical tests. On the other hand, the accelerated failure time (AFT) models do not assume the constant hazards in the survival data as in PH model. The AFT models, moreover, can be used as the alternative to PH model if the constant hazards assumption is violated. The objective of this research was to compare the performance of PH model and the AFT models in analyzing the significant factors affecting the first birth interval (FBI) data in Indonesia. In this work, the discussion was limited to three AFT models which were based on Weibull, exponential, and log-normal distribution. The analysis by using graphical approach and a statistical test showed that the non-proportional hazards exist in the FBI data set. Based on the Akaike information criterion (AIC), the log-normal AFT model was the most appropriate model among the other considered models. Results of the best fitted model (log-normal AFT model) showed that the covariates such as women’s educational level, husband’s educational level, contraceptive knowledge, access to mass media, wealth index, and employment status were among factors affecting the FBI in Indonesia.

  5. Soil-applied imidacloprid translocates to ornamental flowers and reduces survival of adult Coleomegilla maculata, Harmonia axyridis, and Hippodamia convergens lady beetles, and larval Danaus plexippus and Vanessa cardui butterflies.

    Science.gov (United States)

    Krischik, Vera; Rogers, Mary; Gupta, Garima; Varshney, Aruna

    2015-01-01

    Integrated Pest Management (IPM) is a decision making process used to manage pests that relies on many tactics, including cultural and biological control, which are practices that conserve beneficial insects and mites, and when needed, the use of conventional insecticides. However, systemic, soil-applied neonicotinoid insecticides are translocated to pollen and nectar of flowers, often for months, and may reduce survival of flower-feeding beneficial insects. Imidacloprid seed-treated crops (0.05 mg AI (active ingredient) /canola seed and 1.2 mg AI/corn seed) translocate less than 10 ppb to pollen and nectar. However, higher rates of soil-applied imidacloprid are used in nurseries and urban landscapes, such as 300 mg AI/10 L (3 gallon) pot and 69 g AI applied to the soil under a 61 (24 in) cm diam. tree. Translocation of imidacloprid from soil (300 mg AI) to flowers of Asclepias curassavica resulted in 6,030 ppb in 1X and 10,400 ppb in 2X treatments, which are similar to imidacloprid residues found in another plant species we studied. A second imidacloprid soil application 7 months later resulted in 21,000 ppb in 1X and 45,000 ppb in 2X treatments. Consequently, greenhouse/nursery use of imidacloprid applied to flowering plants can result in 793 to 1,368 times higher concentration compared to an imidacloprid seed treatment (7.6 ppb pollen in seed- treated canola), where most research has focused. These higher imidacloprid levels caused significant mortality in both 1X and 2X treatments in 3 lady beetle species, Coleomegilla maculata, Harmonia axyridis, and Hippodamia convergens, but not a fourth species, Coccinella septempunctata. Adult survival were not reduced for monarch, Danaus plexippus and painted lady, Vanessa cardui, butterflies, but larval survival was significantly reduced. The use of the neonicotinoid imidacloprid at greenhouse/nursery rates reduced survival of beneficial insects feeding on pollen and nectar and is incompatible with the principles of IPM.

  6. Soil-applied imidacloprid translocates to ornamental flowers and reduces survival of adult Coleomegilla maculata, Harmonia axyridis, and Hippodamia convergens lady beetles, and larval Danaus plexippus and Vanessa cardui butterflies.

    Directory of Open Access Journals (Sweden)

    Vera Krischik

    Full Text Available Integrated Pest Management (IPM is a decision making process used to manage pests that relies on many tactics, including cultural and biological control, which are practices that conserve beneficial insects and mites, and when needed, the use of conventional insecticides. However, systemic, soil-applied neonicotinoid insecticides are translocated to pollen and nectar of flowers, often for months, and may reduce survival of flower-feeding beneficial insects. Imidacloprid seed-treated crops (0.05 mg AI (active ingredient /canola seed and 1.2 mg AI/corn seed translocate less than 10 ppb to pollen and nectar. However, higher rates of soil-applied imidacloprid are used in nurseries and urban landscapes, such as 300 mg AI/10 L (3 gallon pot and 69 g AI applied to the soil under a 61 (24 in cm diam. tree. Translocation of imidacloprid from soil (300 mg AI to flowers of Asclepias curassavica resulted in 6,030 ppb in 1X and 10,400 ppb in 2X treatments, which are similar to imidacloprid residues found in another plant species we studied. A second imidacloprid soil application 7 months later resulted in 21,000 ppb in 1X and 45,000 ppb in 2X treatments. Consequently, greenhouse/nursery use of imidacloprid applied to flowering plants can result in 793 to 1,368 times higher concentration compared to an imidacloprid seed treatment (7.6 ppb pollen in seed- treated canola, where most research has focused. These higher imidacloprid levels caused significant mortality in both 1X and 2X treatments in 3 lady beetle species, Coleomegilla maculata, Harmonia axyridis, and Hippodamia convergens, but not a fourth species, Coccinella septempunctata. Adult survival were not reduced for monarch, Danaus plexippus and painted lady, Vanessa cardui, butterflies, but larval survival was significantly reduced. The use of the neonicotinoid imidacloprid at greenhouse/nursery rates reduced survival of beneficial insects feeding on pollen and nectar and is incompatible with the

  7. Soil-Applied Imidacloprid Translocates to Ornamental Flowers and Reduces Survival of Adult Coleomegilla maculata, Harmonia axyridis, and Hippodamia convergens Lady Beetles, and Larval Danaus plexippus and Vanessa cardui Butterflies

    Science.gov (United States)

    Krischik, Vera; Rogers, Mary; Gupta, Garima; Varshney, Aruna

    2015-01-01

    Integrated Pest Management (IPM) is a decision making process used to manage pests that relies on many tactics, including cultural and biological control, which are practices that conserve beneficial insects and mites, and when needed, the use of conventional insecticides. However, systemic, soil-applied neonicotinoid insecticides are translocated to pollen and nectar of flowers, often for months, and may reduce survival of flower-feeding beneficial insects. Imidacloprid seed-treated crops (0.05 mg AI (active ingredient) /canola seed and 1.2 mg AI/corn seed) translocate less than 10 ppb to pollen and nectar. However, higher rates of soil-applied imidacloprid are used in nurseries and urban landscapes, such as 300 mg AI/10 L (3 gallon) pot and 69 g AI applied to the soil under a 61 (24 in) cm diam. tree. Translocation of imidacloprid from soil (300 mg AI) to flowers of Asclepias curassavica resulted in 6,030 ppb in 1X and 10,400 ppb in 2X treatments, which are similar to imidacloprid residues found in another plant species we studied. A second imidacloprid soil application 7 months later resulted in 21,000 ppb in 1X and 45,000 ppb in 2X treatments. Consequently, greenhouse/nursery use of imidacloprid applied to flowering plants can result in 793 to 1,368 times higher concentration compared to an imidacloprid seed treatment (7.6 ppb pollen in seed- treated canola), where most research has focused. These higher imidacloprid levels caused significant mortality in both 1X and 2X treatments in 3 lady beetle species, Coleomegilla maculata, Harmonia axyridis, and Hippodamia convergens, but not a fourth species, Coccinella septempunctata. Adult survival were not reduced for monarch, Danaus plexippus and painted lady, Vanessa cardui, butterflies, but larval survival was significantly reduced. The use of the neonicotinoid imidacloprid at greenhouse/nursery rates reduced survival of beneficial insects feeding on pollen and nectar and is incompatible with the principles of IPM

  8. Survival of the Fittest: Why Terrorist Groups Endure

    Directory of Open Access Journals (Sweden)

    Joseph K. Young

    2014-04-01

    Full Text Available Why do terrorist groups endure? This question is relevant to scholars and policy makers alike. In the past, this issue was not been addressed in a systematic fashion. Recent work investigates this question using data on transnational groups and finds that factors associated with the home country can influence the duration the group endures. Applying the theory of outbidding to terrorist group survival, we argue that strategic competition among groups predicts group duration. Using the Global Terrorism Database, we develop a dataset using the terrorist group as the unit of analysis to model the duration of group activity and thus include the largest sample of groups yet. Controlling for previous explanations of both group duration and terrorism, we find a robust effect for the impact that group competition has on terrorist group survival.

  9. A comparative evaluation of risk-adjustment models for benchmarking amputation-free survival after lower extremity bypass.

    Science.gov (United States)

    Simons, Jessica P; Goodney, Philip P; Flahive, Julie; Hoel, Andrew W; Hallett, John W; Kraiss, Larry W; Schanzer, Andres

    2016-04-01

    Providing patients and payers with publicly reported risk-adjusted quality metrics for the purpose of benchmarking physicians and institutions has become a national priority. Several prediction models have been developed to estimate outcomes after lower extremity revascularization for critical limb ischemia, but the optimal model to use in contemporary practice has not been defined. We sought to identify the highest-performing risk-adjustment model for amputation-free survival (AFS) at 1 year after lower extremity bypass (LEB). We used the national Society for Vascular Surgery Vascular Quality Initiative (VQI) database (2003-2012) to assess the performance of three previously validated risk-adjustment models for AFS. The Bypass versus Angioplasty in Severe Ischaemia of the Leg (BASIL), Finland National Vascular (FINNVASC) registry, and the modified Project of Ex-vivo vein graft Engineering via Transfection III (PREVENT III [mPIII]) risk scores were applied to the VQI cohort. A novel model for 1-year AFS was also derived using the VQI data set and externally validated using the PIII data set. The relative discrimination (Harrell c-index) and calibration (Hosmer-May goodness-of-fit test) of each model were compared. Among 7754 patients in the VQI who underwent LEB for critical limb ischemia, the AFS was 74% at 1 year. Each of the previously published models for AFS demonstrated similar discriminative performance: c-indices for BASIL, FINNVASC, mPIII were 0.66, 0.60, and 0.64, respectively. The novel VQI-derived model had improved discriminative ability with a c-index of 0.71 and appropriate generalizability on external validation with a c-index of 0.68. The model was well calibrated in both the VQI and PIII data sets (goodness of fit P = not significant). Currently available prediction models for AFS after LEB perform modestly when applied to national contemporary VQI data. Moreover, the performance of each model was inferior to that of the novel VQI-derived model

  10. Modeling the decline of the Porcupine Caribou Herd, 1989-1998: the importance of survival vs. recruitment

    Directory of Open Access Journals (Sweden)

    Stephen M. Arthur

    2003-04-01

    Full Text Available The Porcupine caribou (Rangifer tarandus granti herd increased from approximately 100 000 animals during the 1970s to 178 000 in 1989, then declined to 129 000 by 1998. Our objective was to model the dynamics of this herd and investigate the potential that lower calf recruitment, as was observed during 1991-1993, produced the observed population changes. A deterministic model was prepared using estimates of birth and survival rates that reproduced the pattern of population growth from 1971-1989. Then, parameters were changed to simulate effects of lower calf recruitment and adult survival. Reducing recruitment for 3 years caused an immediate reduction in population size, but the population began to recover in 5-6 years. Even a dramatic temporary reduction in recruitment did not explain the continuing decline after 1995. In contrast, a slight but persistent reduction in adult survival caused a decline that closely followed the observed pattern. This suggests that survival of adults, and perhaps calves, has declined since the late 1980s.

  11. 46 CFR 117.204 - Survival craft-vessels operating on coastwise routes.

    Science.gov (United States)

    2010-10-01

    ... 46 Shipping 4 2010-10-01 2010-10-01 false Survival craft-vessels operating on coastwise routes... PASSENGERS LIFESAVING EQUIPMENT AND ARRANGEMENTS Number and Type of Survival Craft § 117.204 Survival craft... allowed, the following survival craft requirements apply when not engaged in an overnight voyage: (1...

  12. Microencapsulation increases survival of the probiotic Lactobacillus plantarum IS-10506, but not Enterococcus faecium IS-27526 in a dynamic, computer-controlled in vitro model of the upper gastrointestinal tract.

    Science.gov (United States)

    Surono, I; Verhoeven, J; Verbruggen, S; Venema, K

    2018-02-23

    To test the effect of microencapsulation on the survival of two probiotic strains isolated from Dadih, Indonesian fermented buffalo milk, in a dynamic, computer-controlled in vitro model of the upper gastrointestinal (GI) tract (TIM-1), simulating human adults. Free or microencapsulated probiotics, Lactobacillus plantarum IS-10506 or Enterococcus faecium IS-27526, resuspended in milk were studied for survival in the complete TIM-1 system (stomach + small intestine) or in the gastric compartment of TIM-1 only. Hourly samples collected after the ileal-caecal valve or after the pylorus were plated on MRS agar (for Lactobacillus) or S&B agar (for Enterococcus). Survival of the free cells after transit through the complete TIM-1 system was on average for the E. faecium and L. plantarum 15·0 and 18·5% respectively. Survival of the microencapsulated E. faecium and L. plantarum was 15·7 and 84·5% respectively. The free cells were further assessed in only the gastric compartment of TIM-1. E. faecium and L. plantarum showed an average survival of 39 and 32%, respectively, after gastric passage. There is similar sensitivity to gastric acid as well as survival after complete upper GI tract transit of free cells, but microencapsulation only protected L. plantarum. Survival of microencapsulated L. plantarum IS-10506 is increased compared to free cells in a validated in vitro model of the upper GI tract. It increases its use as an ingredient of functional foods. © 2018 The Society for Applied Microbiology.

  13. DNA-mediated adjuvant immunotherapy extends survival in two different mouse models of myeloid malignancies.

    Science.gov (United States)

    Le Pogam, Carole; Patel, Satyananda; Gorombei, Petra; Guerenne, Laura; Krief, Patricia; Omidvar, Nader; Tekin, Nilgun; Bernasconi, Elena; Sicre, Flore; Schlageter, Marie-Helene; Chopin, Martine; Noguera, Maria-Elena; West, Robert; Abu, Ansu; Mathews, Vikram; Pla, Marika; Fenaux, Pierre; Chomienne, Christine; Padua, Rose Ann

    2015-10-20

    We have previously shown that a specific promyelocytic leukemia-retinoic acid receptor alpha (PML-RARA) DNA vaccine combined with all-trans retinoic acid (ATRA) increases the number of long term survivors with enhanced immune responses in a mouse model of acute promyelocytic leukemia (APL). This study reports the efficacy of a non-specific DNA vaccine, pVAX14Flipper (pVAX14), in both APL and high risk myelodysplastic syndrome (HR-MDS) models. PVAX14 is comprised of novel immunogenic DNA sequences inserted into the pVAX1 therapeutic plasmid. APL mice treated with pVAX14 combined with ATRA had increased survival comparable to that obtained with a specific PML-RARA vaccine. Moreover, the survival advantage correlated with decreased PML-RARA transcript levels and increase in anti-RARA antibody production. In HR-MDS mice, pVAX14 significantly improved survival and reduced biomarkers of leukemic transformation such as phosphorylated mitogen-activated protein/extracellular signal-regulated kinase kinase (MEK) 1. In both preclinical models, pVAX14 vaccine significantly increased interferon gamma (IFNγ) production, memory T-cells (memT), reduced the number of colony forming units (CFU) and increased expression of the adapter molecule signalling to NF-κB, MyD88. These results demonstrate the adjuvant properties of pVAX14 providing thus new approaches to improve clinical outcome in two different models of myeloid malignancies, which may have potential for a broader applicability in other cancers.

  14. LEARNING SEMANTICS-ENHANCED LANGUAGE MODELS APPLIED TO UNSUEPRVISED WSD

    Energy Technology Data Exchange (ETDEWEB)

    VERSPOOR, KARIN [Los Alamos National Laboratory; LIN, SHOU-DE [Los Alamos National Laboratory

    2007-01-29

    An N-gram language model aims at capturing statistical syntactic word order information from corpora. Although the concept of language models has been applied extensively to handle a variety of NLP problems with reasonable success, the standard model does not incorporate semantic information, and consequently limits its applicability to semantic problems such as word sense disambiguation. We propose a framework that integrates semantic information into the language model schema, allowing a system to exploit both syntactic and semantic information to address NLP problems. Furthermore, acknowledging the limited availability of semantically annotated data, we discuss how the proposed model can be learned without annotated training examples. Finally, we report on a case study showing how the semantics-enhanced language model can be applied to unsupervised word sense disambiguation with promising results.

  15. A track-event theory of cell survival

    Energy Technology Data Exchange (ETDEWEB)

    Besserer, Juergen; Schneider, Uwe [Zuerich Univ. (Switzerland). Inst. of Physics; Radiotherapy Hirslanden, Zuerich (Switzerland)

    2015-09-01

    When fractionation schemes for hypofractionation and stereotactic body radiotherapy are considered, a reliable cell survival model at high dose is needed for calculating doses of similar biological effectiveness. In this work a simple model for cell survival which is valid also at high dose is developed from Poisson statistics. An event is defined by two double strand breaks (DSB) on the same or different chromosomes. An event is always lethal due to direct lethal damage or lethal binary misrepair by the formation of chromosome aberrations. Two different mechanisms can produce events: one-track events (OTE) or two-track-events (TTE). The target for an OTE is always a lethal event, the target for an TTE is one DSB. At least two TTEs on the same or different chromosomes are necessary to produce an event. Both, the OTE and the TTE are statistically independent. From the stochastic nature of cell kill which is described by the Poisson distribution the cell survival probability was derived. It was shown that a solution based on Poisson statistics exists for cell survival. It exhibits exponential cell survival at high dose and a finite gradient of cell survival at vanishing dose, which is in agreement with experimental cell studies. The model fits the experimental data nearly as well as the three-parameter formula of Hug-Kellerer and is only based on two free parameters. It is shown that the LQ formalism is an approximation of the model derived in this work. It could be also shown that the derived model predicts a fractionated cell survival experiment better than the LQ-model. It was shown that cell survival can be described with a simple analytical formula on the basis of Poisson statistics. This solution represents in the limit of large dose the typical exponential behavior and predicts cell survival after fractionated dose application better than the LQ-model.

  16. A track-event theory of cell survival

    International Nuclear Information System (INIS)

    Besserer, Juergen; Schneider, Uwe

    2015-01-01

    When fractionation schemes for hypofractionation and stereotactic body radiotherapy are considered, a reliable cell survival model at high dose is needed for calculating doses of similar biological effectiveness. In this work a simple model for cell survival which is valid also at high dose is developed from Poisson statistics. An event is defined by two double strand breaks (DSB) on the same or different chromosomes. An event is always lethal due to direct lethal damage or lethal binary misrepair by the formation of chromosome aberrations. Two different mechanisms can produce events: one-track events (OTE) or two-track-events (TTE). The target for an OTE is always a lethal event, the target for an TTE is one DSB. At least two TTEs on the same or different chromosomes are necessary to produce an event. Both, the OTE and the TTE are statistically independent. From the stochastic nature of cell kill which is described by the Poisson distribution the cell survival probability was derived. It was shown that a solution based on Poisson statistics exists for cell survival. It exhibits exponential cell survival at high dose and a finite gradient of cell survival at vanishing dose, which is in agreement with experimental cell studies. The model fits the experimental data nearly as well as the three-parameter formula of Hug-Kellerer and is only based on two free parameters. It is shown that the LQ formalism is an approximation of the model derived in this work. It could be also shown that the derived model predicts a fractionated cell survival experiment better than the LQ-model. It was shown that cell survival can be described with a simple analytical formula on the basis of Poisson statistics. This solution represents in the limit of large dose the typical exponential behavior and predicts cell survival after fractionated dose application better than the LQ-model.

  17. Standard model group: Survival of the fittest

    Science.gov (United States)

    Nielsen, H. B.; Brene, N.

    1983-09-01

    The essential content of this paper is related to random dynamics. We speculate that the world seen through a sub-Planck-scale microscope has a lattice structure and that the dynamics on this lattice is almost completely random, except for the requirement that the random (plaquette) action is invariant under some "world (gauge) group". We see that the randomness may lead to spontaneous symmetry breakdown in the vacuum (spontaneous collapse) without explicit appeal to any scalar field associated with the usual Higgs mechanism. We further argue that the subgroup which survives as the end product of a possible chain of collapses is likely to have certain properties; the most important is that it has a topologically connected center. The standard group, i.e. the group of the gauge theory which combines the Salam-Weinberg model with QCD, has this property.

  18. Standard model group: survival of the fittest

    International Nuclear Information System (INIS)

    Nielsen, H.B.; Brene, N.

    1983-01-01

    Th essential content of this paper is related to random dynamics. We speculate that the world seen through a sub-Planck-scale microscope has a lattice structure and that the dynamics on this lattice is almost completely random, except for the requirement that the random (plaquette) action is invariant under some ''world (gauge) group''. We see that the randomness may lead to spontaneous symmetry breakdown in the vacuum (spontaneous collapse) without explicit appeal to any scalar field associated with the usual Higgs mechanism. We further argue that the subgroup which survives as the end product of a possible chain of collapse is likely to have certain properties; the most important is that it has a topologically connected center. The standard group, i.e. the group of the gauge theory which combines the Salam-Weinberg model with QCD, has this property. (orig.)

  19. Modeling nest-survival data: a comparison of recently developed methods that can be implemented in MARK and SAS

    Directory of Open Access Journals (Sweden)

    Rotella, J. J.

    2004-06-01

    Full Text Available Estimating nest success and evaluating factors potentially related to the survival rates of nests are key aspects of many studies of avian populations. A strong interest in nest success has led to a rich literature detailing a variety of estimation methods for this vital rate. In recent years, modeling approaches have undergone especially rapid development. Despite these advances, most researchers still employ Mayfield’s ad-hoc method (Mayfield, 1961 or, in some cases, the maximum-likelihood estimator of Johnson (1979 and Bart & Robson (1982. Such methods permit analyses of stratified data but do not allow for more complex and realistic models of nest survival rate that include covariates that vary by individual, nest age, time, etc. and that may be continuous or categorical. Methods that allow researchers to rigorously assess the importance of a variety of biological factors that might affect nest survival rates can now be readily implemented in Program MARK and in SAS’s Proc GENMOD and Proc NLMIXED. Accordingly, use of Mayfield’s estimator without first evaluating the need for more complex models of nest survival rate cannot be justified. With the goal of increasing the use of more flexible methods, we first describe the likelihood used for these models and then consider the question of what the effective sample size is for computation of AICc. Next, we consider the advantages and disadvantages of these different programs in terms of ease of data input and model construction; utility/flexibility of generated estimates and predictions; ease of model selection; and ability to estimate variance components. An example data set is then analyzed using both MARK and SAS to demonstrate implementation of the methods with various models that contain nest-, group- (or block-, and time-specific covariates. Finally, we discuss improvements that would, if they became available, promote a better general understanding of nest survival rates.

  20. Survival after Liver Transplantation in the United States: A Disease-Specific Analysis of the UNOS database

    Czech Academy of Sciences Publication Activity Database

    Roberts, M.S.; Angus, D.C.; Bryce, C.L.; Valenta, Zdeněk; Weissfeld, L.

    2004-01-01

    Roč. 10, č. 7 (2004), s. 886-897 ISSN 1527-6465 Source of funding: V - iné verejné zdroje Keywords : disease-specific survival * liver transplantation * cox PH model Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.984, year: 2004

  1. Learning Survival Models with On-Line Simulation Activities in the Actuarial Science Degree

    Directory of Open Access Journals (Sweden)

    Antonio Fernandez-Morales

    2011-03-01

    Full Text Available The aim of this paper is to describe an on-line survival laboratory designed to enhance teaching and learning in the Statistics courses of the Actuarial Science Degree of the Uni-versity of Málaga. The objective of the on-line survival lab is to help students through a guided program of simulation activities with the understanding of the most important statistical concepts of the stochastic modeling of human survival, from an Actuarial point of view. The graphical interactive simulator is implemented as Java applets for the web version, and as a Javascript animation for a lite iPhone/iPod touch version. Finally, the results of a survey carried out at the end of the course are discussed to have a preliminary assessment of the students’ satisfaction with the resources, and their perception about the usefulness for their learning process.

  2. An Optic Nerve Crush Injury Murine Model to Study Retinal Ganglion Cell Survival

    Science.gov (United States)

    Tang, Zhongshu; Zhang, Shuihua; Lee, Chunsik; Kumar, Anil; Arjunan, Pachiappan; Li, Yang; Zhang, Fan; Li, Xuri

    2011-01-01

    Injury to the optic nerve can lead to axonal degeneration, followed by a gradual death of retinal ganglion cells (RGCs), which results in irreversible vision loss. Examples of such diseases in human include traumatic optic neuropathy and optic nerve degeneration in glaucoma. It is characterized by typical changes in the optic nerve head, progressive optic nerve degeneration, and loss of retinal ganglion cells, if uncontrolled, leading to vision loss and blindness. The optic nerve crush (ONC) injury mouse model is an important experimental disease model for traumatic optic neuropathy, glaucoma, etc. In this model, the crush injury to the optic nerve leads to gradual retinal ganglion cells apoptosis. This disease model can be used to study the general processes and mechanisms of neuronal death and survival, which is essential for the development of therapeutic measures. In addition, pharmacological and molecular approaches can be used in this model to identify and test potential therapeutic reagents to treat different types of optic neuropathy. Here, we provide a step by step demonstration of (I) Baseline retrograde labeling of retinal ganglion cells (RGCs) at day 1, (II) Optic nerve crush injury at day 4, (III) Harvest the retinae and analyze RGC survival at day 11, and (IV) Representative result. PMID:21540827

  3. Network ties and survival

    DEFF Research Database (Denmark)

    Acheampong, George; Narteh, Bedman; Rand, John

    2017-01-01

    Poultry farming has been touted as one of the major ways by which poverty can be reduced in low-income economies like Ghana. Yet, anecdotally there is a high failure rate among these poultry farms. This current study seeks to understand the relationship between network ties and survival chances...... of small commercial poultry farms (SCPFs). We utilize data from a 2-year network survey of SCPFs in rural Ghana. The survival of these poultry farms are modelled using a lagged probit model of farms that persisted from 2014 into 2015. We find that network ties are important to the survival chances...... but this probability reduces as the number of industry ties increases but moderation with dynamic capability of the firm reverses this trend. Our findings show that not all network ties aid survival and therefore small commercial poultry farmers need to be circumspect in the network ties they cultivate and develop....

  4. Modeling the survivability of brucella to exposure of Ultraviolet radiation and temperature

    Science.gov (United States)

    Howe, R.

    Accumulated summation of daily Ultra Violet-B (UV-B = 290? to 320 ? ) data? from The USDA Ultraviolet Radiation Monitoring Program show good correlation (R^2 = 77%) with daily temperature data during the five month period from February through June, 1998. Exposure of disease organisms, such as brucella to the effects of accumulated UV-B radiation, can be modeled for a 5 month period from February through June, 1998. Estimates of a lethal dosage for brucell of UV-B in the environment is dependent on minimum/maximum temperature and Solar Zenith Angle for the time period. The accumulated increase in temperature over this period also effects the decomposition of an aborted fetus containing brucella. Decomposition begins at some minimum daily temperature at 27 to 30 degrees C and peaks at 39 to 40C. It is useful to view the summation of temperature as a threshold for other bacteria growth, so that accumulated temperature greater than some value causes decomposition through competition with other bacteria and brucella die from the accumulated effects of UV-B, temperature and organism competition. Results of a study (Cook 1998) to determine survivability of brucellosis in the environment through exposure of aborted bovine fetuses show no one cause can be attributed to death of the disease agent. The combination of daily increase in temperature and accumulated UV-B radiation reveal an inverse correlation to survivability data and can be modeled as an indicator of brucella survivability in the environment in arid regions.

  5. Learning to Apply Models of Materials While Explaining Their Properties

    Science.gov (United States)

    Karpin, Tiia; Juuti, Kalle; Lavonen, Jari

    2014-01-01

    Background: Applying structural models is important to chemistry education at the upper secondary level, but it is considered one of the most difficult topics to learn. Purpose: This study analyses to what extent in designed lessons students learned to apply structural models in explaining the properties and behaviours of various materials.…

  6. Exact results for survival probability in the multistate Landau-Zener model

    International Nuclear Information System (INIS)

    Volkov, M V; Ostrovsky, V N

    2004-01-01

    An exact formula is derived for survival probability in the multistate Landau-Zener model in the special case where the initially populated state corresponds to the extremal (maximum or minimum) slope of a linear diabatic potential curve. The formula was originally guessed by S Brundobler and V Elzer (1993 J. Phys. A: Math. Gen. 26 1211) based on numerical calculations. It is a simple generalization of the expression for the probability of diabatic passage in the famous two-state Landau-Zener model. Our result is obtained via analysis and summation of the entire perturbation theory series

  7. International Conference on Applied Mathematics, Modeling and Computational Science & Annual meeting of the Canadian Applied and Industrial Mathematics

    CERN Document Server

    Bélair, Jacques; Kunze, Herb; Makarov, Roman; Melnik, Roderick; Spiteri, Raymond J

    2016-01-01

    Focusing on five main groups of interdisciplinary problems, this book covers a wide range of topics in mathematical modeling, computational science and applied mathematics. It presents a wealth of new results in the development of modeling theories and methods, advancing diverse areas of applications and promoting interdisciplinary interactions between mathematicians, scientists, engineers and representatives from other disciplines. The book offers a valuable source of methods, ideas, and tools developed for a variety of disciplines, including the natural and social sciences, medicine, engineering, and technology. Original results are presented on both the fundamental and applied level, accompanied by an ample number of real-world problems and examples emphasizing the interdisciplinary nature and universality of mathematical modeling, and providing an excellent outline of today’s challenges. Mathematical modeling, with applied and computational methods and tools, plays a fundamental role in modern science a...

  8. Methods for model selection in applied science and engineering.

    Energy Technology Data Exchange (ETDEWEB)

    Field, Richard V., Jr.

    2004-10-01

    Mathematical models are developed and used to study the properties of complex systems and/or modify these systems to satisfy some performance requirements in just about every area of applied science and engineering. A particular reason for developing a model, e.g., performance assessment or design, is referred to as the model use. Our objective is the development of a methodology for selecting a model that is sufficiently accurate for an intended use. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. Methods are developed for selecting the optimal member from a class of candidate models for the system. The optimal model depends on the available information, the selected class of candidate models, and the model use. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, as well as a method employing a decision-theoretic approach, are formulated to select the optimal model for numerous applications. There is no requirement that the candidate models be random. Classical methods for model selection ignore model use and require data to be available. Examples are used to show that these methods can be unreliable when data is limited. The decision-theoretic approach to model selection does not have these limitations, and model use is included through an appropriate utility function. This is especially important when modeling high risk systems, where the consequences of using an inappropriate model for the system can be disastrous. The decision-theoretic method for model selection is developed and applied for a series of complex and diverse applications. These include the selection of the: (1) optimal order of the polynomial chaos approximation for non-Gaussian random variables and stationary stochastic processes, (2) optimal pressure load model to be

  9. Updated estimates of survival and cost effectiveness for imatinib versus interferon-alpha plus low-dose cytarabine for newly diagnosed chronic-phase chronic myeloid leukaemia.

    Science.gov (United States)

    Reed, Shelby D; Anstrom, Kevin J; Li, Yanhong; Schulman, Kevin A

    2008-01-01

    For trials in which participants are followed beyond the main study period to assess long-term outcomes, economic evaluations conducted using short-term data should be systematically updated to reflect new information. We used 60-month survival data from the IRIS (International Randomized study of Interferon vs STI571) trial to update previously published cost-effectiveness estimates, based on 19 months of follow-up, of imatinib versus interferon (IFN)-alpha plus low-dose cytarabine in patients with chronic-phase chronic myeloid leukaemia. For patients treated with imatinib, we used the 60-month data to calibrate the survival curves generated from the original cost-effectiveness model. We used historical data to model survival for patients randomized to IFNalpha. We updated costs for medical resources using 2006 Medicare reimbursement rates and applied average wholesale prices (AWPs) and wholesale acquisition costs (WACs) to study medications. Five-year survival for patients randomized to imatinib was better than predicted in the original model (89.4% vs 83.2%). We estimated remaining life expectancy with first-line imatinib to be 19.1 life-years (3.8 life-years over the original model) and 15.2 QALYs (3.1 QALYs over the original estimate). Estimates for IFNalpha remained at 9.1 life-years and 6.3 QALYs. When we applied AWPs to study medications, incremental cost-effectiveness ratios (ICERs) were $US 51,800-57,500 per QALY. When we applied WACs, ICERs were $US 42,000-46,200 per QALY. Although the analysis revealed that the original survival estimates were conservative, the updated cost-effectiveness ratios were consistent with, or slightly higher than, the original estimates, depending on the method for assigning costs to study medications.

  10. An approach to the drone fleet survivability assessment based on a stochastic continues-time model

    Science.gov (United States)

    Kharchenko, Vyacheslav; Fesenko, Herman; Doukas, Nikos

    2017-09-01

    An approach and the algorithm to the drone fleet survivability assessment based on a stochastic continues-time model are proposed. The input data are the number of the drones, the drone fleet redundancy coefficient, the drone stability and restoration rate, the limit deviation from the norms of the drone fleet recovery, the drone fleet operational availability coefficient, the probability of the drone failure-free operation, time needed for performing the required tasks by the drone fleet. The ways for improving the recoverable drone fleet survivability taking into account amazing factors of system accident are suggested. Dependencies of the drone fleet survivability rate both on the drone stability and the number of the drones are analysed.

  11. Standard model group: survival of the fittest

    Energy Technology Data Exchange (ETDEWEB)

    Nielsen, H.B. (Niels Bohr Inst., Copenhagen (Denmark); Nordisk Inst. for Teoretisk Atomfysik, Copenhagen (Denmark)); Brene, N. (Niels Bohr Inst., Copenhagen (Denmark))

    1983-09-19

    The essential content of this paper is related to random dynamics. We speculate that the world seen through a sub-Planck-scale microscope has a lattice structure and that the dynamics on this lattice is almost completely random, except for the requirement that the random (plaquette) action is invariant under some ''world (gauge) group''. We see that the randomness may lead to spontaneous symmetry breakdown in the vacuum (spontaneous collapse) without explicit appeal to any scalar field associated with the usual Higgs mechanism. We further argue that the subgroup which survives as the end product of a possible chain of collapse is likely to have certain properties; the most important is that it has a topologically connected center. The standard group, i.e. the group of the gauge theory which combines the Salam-Weinberg model with QCD, has this property.

  12. Standard model group survival of the fittest

    International Nuclear Information System (INIS)

    Nielsen, H.B.; Brene, N.

    1983-02-01

    The essential content of this note is related to random dynamics. The authors speculate that the world seen through a sub Planck scale microscope has a lattice structure and that the dynamics on this lattice is almost completely random, except for the requirement that the random (plaquette) action is invariant under some ''world (gauge) group''. It is seen that the randomness may lead to spontaneous symmetry breakdown in the vacuum (spontaneous collapse) without explicit appeal to any scalar field associated with the usual Higgs mechanism. It is further argued that the subgroup which survives as the end product of a possible chain of collapses is likely to have certain properties; the most important is that it has a topologically connected center. The standard group, i.e. the group of the gauge theory which combines the Salam-Weinberg model with QCD, has this property. (Auth.)

  13. Modelling lecturer performance index of private university in Tulungagung by using survival analysis with multivariate adaptive regression spline

    Science.gov (United States)

    Hasyim, M.; Prastyo, D. D.

    2018-03-01

    Survival analysis performs relationship between independent variables and survival time as dependent variable. In fact, not all survival data can be recorded completely by any reasons. In such situation, the data is called censored data. Moreover, several model for survival analysis requires assumptions. One of the approaches in survival analysis is nonparametric that gives more relax assumption. In this research, the nonparametric approach that is employed is Multivariate Regression Adaptive Spline (MARS). This study is aimed to measure the performance of private university’s lecturer. The survival time in this study is duration needed by lecturer to obtain their professional certificate. The results show that research activities is a significant factor along with developing courses material, good publication in international or national journal, and activities in research collaboration.

  14. Nonlinear Eddy Viscosity Models applied to Wind Turbine Wakes

    DEFF Research Database (Denmark)

    Laan, van der, Paul Maarten; Sørensen, Niels N.; Réthoré, Pierre-Elouan

    2013-01-01

    The linear k−ε eddy viscosity model and modified versions of two existing nonlinear eddy viscosity models are applied to single wind turbine wake simulations using a Reynolds Averaged Navier-Stokes code. Results are compared with field wake measurements. The nonlinear models give better results...

  15. Spatial Random Effects Survival Models to Assess Geographical Inequalities in Dengue Fever Using Bayesian Approach: a Case Study

    Science.gov (United States)

    Astuti Thamrin, Sri; Taufik, Irfan

    2018-03-01

    Dengue haemorrhagic fever (DHF) is an infectious disease caused by dengue virus. The increasing number of people with DHF disease correlates with the neighbourhood, for example sub-districts, and the characteristics of the sub-districts are formed from individuals who are domiciled in the sub-districts. Data containing individuals and sub-districts is a hierarchical data structure, called multilevel analysis. Frequently encountered response variable of the data is the time until an event occurs. Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in DHF survival. Using a case study approach, we report on the implications of using multilevel with spatial survival models to study geographical inequalities in all cause survival.

  16. Compensatory effects of recruitment and survival when amphibian populations are perturbed by disease

    Science.gov (United States)

    Muths, E.; Scherer, R. D.; Pilliod, D.S.

    2011-01-01

    The need to increase our understanding of factors that regulate animal population dynamics has been catalysed by recent, observed declines in wildlife populations worldwide. Reliable estimates of demographic parameters are critical for addressing basic and applied ecological questions and understanding the response of parameters to perturbations (e.g. disease, habitat loss, climate change). However, to fully assess the impact of perturbation on population dynamics, all parameters contributing to the response of the target population must be estimated. We applied the reverse-time model of Pradel in Program mark to 6years of capture-recapture data from two populations of Anaxyrus boreas (boreal toad) populations, one with disease and one without. We then assessed a priori hypotheses about differences in survival and recruitment relative to local environmental conditions and the presence of disease. We further explored the relative contribution of survival probability and recruitment rate to population growth and investigated how shifts in these parameters can alter population dynamics when a population is perturbed. High recruitment rates (0??41) are probably compensating for low survival probability (range 0??51-0??54) in the population challenged by an emerging pathogen, resulting in a relatively slow rate of decline. In contrast, the population with no evidence of disease had high survival probability (range 0??75-0??78) but lower recruitment rates (0??25). Synthesis and applications.We suggest that the relationship between survival and recruitment may be compensatory, providing evidence that populations challenged with disease are not necessarily doomed to extinction. A better understanding of these interactions may help to explain, and be used to predict, population regulation and persistence for wildlife threatened with disease. Further, reliable estimates of population parameters such as recruitment and survival can guide the formulation and implementation of

  17. The non-linear, interactive effects of population density and climate drive the geographical patterns of waterfowl survival

    Science.gov (United States)

    Zhao, Qing; Boomer, G. Scott; Kendall, William L.

    2018-01-01

    On-going climate change has major impacts on ecological processes and patterns. Understanding the impacts of climate on the geographical patterns of survival can provide insights to how population dynamics respond to climate change and provide important information for the development of appropriate conservation strategies at regional scales. It is challenging to understand the impacts of climate on survival, however, due to the fact that the non-linear relationship between survival and climate can be modified by density-dependent processes. In this study we extended the Brownie model to partition hunting and non-hunting mortalities and linked non-hunting survival to covariates. We applied this model to four decades (1972–2014) of waterfowl band-recovery, breeding population survey, and precipitation and temperature data covering multiple ecological regions to examine the non-linear, interactive effects of population density and climate on waterfowl non-hunting survival at a regional scale. Our results showed that the non-linear effect of temperature on waterfowl non-hunting survival was modified by breeding population density. The concave relationship between non-hunting survival and temperature suggested that the effects of warming on waterfowl survival might be multifaceted. Furthermore, the relationship between non-hunting survival and temperature was stronger when population density was higher, suggesting that high-density populations may be less buffered against warming than low-density populations. Our study revealed distinct relationships between waterfowl non-hunting survival and climate across and within ecological regions, highlighting the importance of considering different conservation strategies according to region-specific population and climate conditions. Our findings and associated novel modelling approach have wide implications in conservation practice.

  18. Model for breast cancer survival: relative prognostic roles of axillary nodal status, TNM stage, estrogen receptor concentration, and tumor necrosis.

    Science.gov (United States)

    Shek, L L; Godolphin, W

    1988-10-01

    The independent prognostic effects of certain clinical and pathological variables measured at the time of primary diagnosis were assessed with Cox multivariate regression analysis. The 859 patients with primary breast cancer, on which the proportional hazards model was based, had a median follow-up of 60 months. Axillary nodal status (categorized as N0, N1-3 or N4+) was the most significant and independent factor in overall survival, but inclusion of TNM stage, estrogen receptor (ER) concentration and tumor necrosis significantly improved survival predictions. Predictions made with the model showed striking subset survival differences within stage: 5-year survival from 36% (N4+, loge[ER] = 0, marked necrosis) to 96% (N0, loge[ER] = 6, no necrosis) in TNM I, and from 0 to 70% for the same categories in TNM IV. Results of the model were used to classify patients into four distinct risk groups according to a derived hazard index. An 8-fold variation in survival was seen with the highest (greater than 3) to lowest index values (less than 1). Each hazard index level included patients with varied combinations of the above factors, but could be considered to denote the same degree of risk of breast cancer mortality. A model with ER concentration, nodal status, and tumor necrosis was found to best predict survival after disease recurrence in 369 patients, thus confirming the enduring biological significance of these factors.

  19. Risk stratification in middle-aged patients with congestive heart failure: prospective comparison of the Heart Failure Survival Score (HFSS) and a simplified two-variable model.

    Science.gov (United States)

    Zugck, C; Krüger, C; Kell, R; Körber, S; Schellberg, D; Kübler, W; Haass, M

    2001-10-01

    The performance of a US-American scoring system (Heart Failure Survival Score, HFSS) was prospectively evaluated in a sample of ambulatory patients with congestive heart failure (CHF). Additionally, it was investigated whether the HFSS might be simplified by assessment of the distance ambulated during a 6-min walk test (6'WT) instead of determination of peak oxygen uptake (peak VO(2)). In 208 middle-aged CHF patients (age 54+/-10 years, 82% male, NYHA class 2.3+/-0.7; follow-up 28+/-14 months) the seven variables of the HFSS: CHF aetiology; heart rate; mean arterial pressure; serum sodium concentration; intraventricular conduction time; left ventricular ejection fraction (LVEF); and peak VO(2), were determined. Additionally, a 6'WT was performed. The HFSS allowed discrimination between patients at low, medium and high risk, with mortality rates of 16, 39 and 50%, respectively. However, the prognostic power of the HFSS was not superior to a two-variable model consisting only of LVEF and peak VO(2). The areas under the receiver operating curves (AUC) for prediction of 1-year survival were even higher for the two-variable model (0.84 vs. 0.74, P<0.05). Replacing peak VO(2) with 6'WT resulted in a similar AUC (0.83). The HFSS continued to predict survival when applied to this patient sample. However, the HFSS was inferior to a two-variable model containing only LVEF and either peak VO(2) or 6'WT. As the 6'WT requires no sophisticated equipment, a simplified two-variable model containing only LVEF and 6'WT may be more widely applicable, and is therefore recommended.

  20. Factors influencing survival and mark retention in postmetamorphic boreal chorus frogs

    Science.gov (United States)

    Swanson, Jennifer E; Bailey, Larissa L.; Muths, Erin L.; Funk, W. Chris

    2013-01-01

    The ability to track individual animals is crucial in many field studies and often requires applying marks to captured individuals. Toe clipping has historically been a standard marking method for wild amphibian populations, but more recent marking methods include visual implant elastomer and photo identification. Unfortunately, few studies have investigated the influence and effectiveness of marking methods for recently metamorphosed individuals and as a result little is known about this life-history phase for most amphibians. Our focus was to explore survival probabilities, mark retention, and mark migration in postmetamorphic Boreal Chorus Frogs (Psuedacris maculata) in a laboratory setting. One hundred forty-seven individuals were assigned randomly to two treatment groups or a control group. Frogs in the first treatment group were marked with visual implant elastomer, while frogs in the second treatment group were toe clipped. Growth and mortality were recorded for one year and resulting data were analyzed using known-fate models in Program MARK. Model selection results suggested that survival probabilities of frogs varied with time and showed some variation among marking treatments. We found that frogs with multiple toes clipped on the same foot had lower survival probabilities than individuals in other treatments, but individuals can be marked by clipping a single toe on two different feet without any mark loss or negative survival effects. Individuals treated with visual implant elastomer had a mark migration rate of 4% and mark loss rate of 6%, and also showed very little negative survival impacts relative to control individuals.

  1. Survival and transport of faecal bacteria in agricultural soils

    DEFF Research Database (Denmark)

    Bech, Tina Bundgaard

    Today, there is yearly applied 34 million tonnes of animal waste to arable land in Denmark. This waste may contain pathogenic zoonotic bacteria and/or antibiotic resistant bacteria, and when applied to arable land there is a risk of contaminating groundwater, surface water, feeding animals or fresh...... produce. Prediction of faecal bacterial survival and transport in the soil environment will help minimize the risk of contamination, as best management practices can be adapted to this knowledge. The aim of this Ph.D. is to study factors influencing faecal bacteria survival and transport in soil...... – it is based on both field scale and lab scale experiments. The influence of application method and slurry properties has been tested on both survival and transport....

  2. Comparison of two multiaxial fatigue models applied to dental implants

    Directory of Open Access Journals (Sweden)

    JM. Ayllon

    2015-07-01

    Full Text Available This paper presents two multiaxial fatigue life prediction models applied to a commercial dental implant. One model is called Variable Initiation Length Model and takes into account both the crack initiation and propagation phases. The second model combines the Theory of Critical Distance with a critical plane damage model to characterise the initiation and initial propagation of micro/meso cracks in the material. This paper discusses which material properties are necessary for the implementation of these models and how to obtain them in the laboratory from simple test specimens. It also describes the FE models developed for the stress/strain and stress intensity factor characterisation in the implant. The results of applying both life prediction models are compared with experimental results arising from the application of ISO-14801 standard to a commercial dental implant.

  3. Structural Modeling and Analysis of a Wave Energy Converter Applying Dynamical Substructuring Method

    DEFF Research Database (Denmark)

    Zurkinden, Andrew Stephen; Damkilde, Lars; Gao, Zhen

    2013-01-01

    to the relative stiff behavior of the arm the calculation can be reduced to a quasi-static analysis. The hydrodynamic and the structural analyses are thus performed separately. In order to reduce the computational time of the finite element calculation the main structure is modeled as a superelement......This paper deals with structural modeling and analysis of a wave energy converter. The device, called Wavestar, is a bottom fixed structure, located in a shallow water environment at the Danish Northwest coast. The analysis is concentrated on a single float and its structural arm which connects...... the WEC to a jackup structure. The wave energy converter is characterized by having an operational and survival mode. The survival mode drastically reduces the exposure to waves and therfore to the wave loads. Structural response analysis of the Wavestar arm is carried out in this study. Due...

  4. Development of a predictive model for 6 month survival in patients with venous thromboembolism and solid malignancy requiring IVC filter placement.

    Science.gov (United States)

    Huang, Steven Y; Odisio, Bruno C; Sabir, Sharjeel H; Ensor, Joe E; Niekamp, Andrew S; Huynh, Tam T; Kroll, Michael; Gupta, Sanjay

    2017-07-01

    Our purpose was to develop a predictive model for short-term survival (i.e. filter placement in patients with venous thromboembolism (VTE) and solid malignancy. Clinical and laboratory parameters were retrospectively reviewed for patients with solid malignancy who received a filter between January 2009 and December 2011 at a tertiary care cancer center. Multivariate Cox proportional hazards modeling was used to assess variables associated with 6 month survival following filter placement in patients with VTE and solid malignancy. Significant variables were used to generate a predictive model. 397 patients with solid malignancy received a filter during the study period. Three variables were associated with 6 month survival: (1) serum albumin [hazard ratio (HR) 0.496, P filter placement can be predicted from three patient variables. Our predictive model could be used to help physicians decide whether a permanent or retrievable filter may be more appropriate as well as to assess the risks and benefits for filter retrieval within the context of survival longevity in patients with cancer.

  5. Applying MDA to SDR for Space to Model Real-time Issues

    Science.gov (United States)

    Blaser, Tammy M.

    2007-01-01

    NASA space communications systems have the challenge of designing SDRs with highly-constrained Size, Weight and Power (SWaP) resources. A study is being conducted to assess the effectiveness of applying the MDA Platform-Independent Model (PIM) and one or more Platform-Specific Models (PSM) specifically to address NASA space domain real-time issues. This paper will summarize our experiences with applying MDA to SDR for Space to model real-time issues. Real-time issues to be examined, measured, and analyzed are: meeting waveform timing requirements and efficiently applying Real-time Operating System (RTOS) scheduling algorithms, applying safety control measures, and SWaP verification. Real-time waveform algorithms benchmarked with the worst case environment conditions under the heaviest workload will drive the SDR for Space real-time PSM design.

  6. Sodium nitroprusside enhanced cardiopulmonary resuscitation improves short term survival in a porcine model of ischemic refractory ventricular fibrillation.

    Science.gov (United States)

    Yannopoulos, Demetris; Bartos, Jason A; George, Stephen A; Sideris, George; Voicu, Sebastian; Oestreich, Brett; Matsuura, Timothy; Shekar, Kadambari; Rees, Jennifer; Aufderheide, Tom P

    2017-01-01

    Sodium nitroprusside (SNP) enhanced CPR (SNPeCPR) demonstrates increased vital organ blood flow and survival in multiple porcine models. We developed a new, coronary occlusion/ischemia model of prolonged resuscitation, mimicking the majority of out-of-hospital cardiac arrests presenting with shockable rhythms. SNPeCPR will increase short term (4-h) survival compared to standard 2015 Advanced Cardiac Life Support (ACLS) guidelines in an ischemic refractory ventricular fibrillation (VF), prolonged CPR model. Sixteen anesthetized pigs had the ostial left anterior descending artery occluded leading to ischemic VF arrest. VF was untreated for 5min. Basic life support was performed for 10min. At minute 10 (EMS arrival), animals received either SNPeCPR (n=8) or standard ACLS (n=8). Defibrillation (200J) occurred every 3min. CPR continued for a total of 45min, then the balloon was deflated simulating revascularization. CPR continued until return of spontaneous circulation (ROSC) or a total of 60min, if unsuccessful. SNPeCPR animals received 2mg of SNP at minute 10 followed by 1mg every 5min until ROSC. Standard ACLS animals received 0.5mg epinephrine every 5min until ROSC. Primary endpoints were ROSC and 4-h survival. All SNPeCPR animals (8/8) achieved sustained ROSC versus 2/8 standard ACLS animals within one hour of resuscitation (p=0.04). The 4-h survival was significantly improved with SNPeCPR compared to standard ACLS, 7/8 versus 1/8 respectively, p=0.0019. SNPeCPR significantly improved ROSC and 4-h survival compared with standard ACLS CPR in a porcine model of prolonged ischemic, refractory VF cardiac arrest. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  7. Development and External Validation of Prognostic Model for 2-Year Survival of Non-Small-Cell Lung Cancer Patients Treated With Chemoradiotherapy

    International Nuclear Information System (INIS)

    Dehing-Oberije, Cary; Yu Shipeng; De Ruysscher, Dirk; Meersschout, Sabine; Van Beek, Karen; Lievens, Yolande; Van Meerbeeck, Jan; De Neve, Wilfried; Rao, Bharat Ph.D.; Weide, Hiska van der; Lambin, Philippe

    2009-01-01

    Purpose: Radiotherapy, combined with chemotherapy, is the treatment of choice for a large group of non-small-cell lung cancer (NSCLC) patients. Recent developments in the treatment of these patients have led to improved survival. However, the clinical TNM stage is highly inaccurate for the prediction of survival, and alternatives are lacking. The objective of this study was to develop and validate a prediction model for survival of NSCLC patients, treated with chemoradiotherapy. Patients and Methods: The clinical data from 377 consecutive inoperable NSCLC patients, Stage I-IIIB, treated radically with chemoradiotherapy were collected. A prognostic model for 2-year survival was developed, using 2-norm support vector machines. The performance of the model was expressed as the area under the curve of the receiver operating characteristic and assessed using leave-one-out cross-validation, as well as two external data sets. Results: The final multivariate model consisted of gender, World Health Organization performance status, forced expiratory volume in 1 s, number of positive lymph node stations, and gross tumor volume. The area under the curve, assessed by leave-one-out cross-validation, was 0.74, and application of the model to the external data sets yielded an area under the curve of 0.75 and 0.76. A high- and low-risk group could be clearly identified using a risk score based on the model. Conclusion: The multivariate model performed very well and was able to accurately predict the 2-year survival of NSCLC patients treated with chemoradiotherapy. The model could support clinicians in the treatment decision-making process.

  8. Challenges in economic modeling of anticancer therapies: an example of modeling the survival benefit of olaparib maintenance therapy for patients with BRCA-mutated platinum-sensitive relapsed ovarian cancer.

    Science.gov (United States)

    Hettle, Robert; Posnett, John; Borrill, John

    2015-01-01

    The aim of this paper is to describe a four health-state, semi-Markov model structure with health states defined by initiation of subsequent treatment, designed to make best possible use of the data available from a phase 2 clinical trial. The approach is illustrated using data from a sub-group of patients enrolled in a phase 2 clinical trial of olaparib maintenance therapy in patients with platinum-sensitive relapsed ovarian cancer and a BRCA mutation (NCT00753545). A semi-Markov model was developed with four health states: progression-free survival (PFS), first subsequent treatment (FST), second subsequent treatment (SST), and death. Transition probabilities were estimated by fitting survival curves to trial data for time from randomization to FST, time from FST to SST, and time from SST to death. Survival projections generated by the model are broadly consistent with the outcomes observed in the clinical trial. However, limitations of the trial data (small sample size, immaturity of the PFS and overall survival [OS] end-points, and treatment switching) create uncertainty in estimates of survival. The model framework offers a promising approach to evaluating cost-effectiveness of a maintenance therapy for patients with cancer, which may be generalizable to other chronic diseases.

  9. Human immune cells' behavior and survival under bioenergetically restricted conditions in an in vitro fracture hematoma model

    Science.gov (United States)

    Hoff, Paula; Maschmeyer, Patrick; Gaber, Timo; Schütze, Tabea; Raue, Tobias; Schmidt-Bleek, Katharina; Dziurla, René; Schellmann, Saskia; Lohanatha, Ferenz Leonard; Röhner, Eric; Ode, Andrea; Burmester, Gerd-Rüdiger; Duda, Georg N; Perka, Carsten; Buttgereit, Frank

    2013-01-01

    The initial inflammatory phase of bone fracture healing represents a critical step for the outcome of the healing process. However, both the mechanisms initiating this inflammatory phase and the function of immune cells present at the fracture site are poorly understood. In order to study the early events within a fracture hematoma, we established an in vitro fracture hematoma model: we cultured hematomas forming during an osteotomy (artificial bone fracture) of the femur during total hip arthroplasty (THA) in vitro under bioenergetically controlled conditions. This model allowed us to monitor immune cell populations, cell survival and cytokine expression during the early phase following a fracture. Moreover, this model enabled us to change the bioenergetical conditions in order to mimic the in vivo situation, which is assumed to be characterized by hypoxia and restricted amounts of nutrients. Using this model, we found that immune cells adapt to hypoxia via the expression of angiogenic factors, chemoattractants and pro-inflammatory molecules. In addition, combined restriction of oxygen and nutrient supply enhanced the selective survival of lymphocytes in comparison with that of myeloid derived cells (i.e., neutrophils). Of note, non-restricted bioenergetical conditions did not show any similar effects regarding cytokine expression and/or different survival rates of immune cell subsets. In conclusion, we found that the bioenergetical conditions are among the crucial factors inducing the initial inflammatory phase of fracture healing and are thus a critical step for influencing survival and function of immune cells in the early fracture hematoma. PMID:23396474

  10. Biostatistics series module 9: Survival analysis

    Directory of Open Access Journals (Sweden)

    Avijit Hazra

    2017-01-01

    Full Text Available Survival analysis is concerned with “time to event“ data. Conventionally, it dealt with cancer death as the event in question, but it can handle any event occurring over a time frame, and this need not be always adverse in nature. When the outcome of a study is the time to an event, it is often not possible to wait until the event in question has happened to all the subjects, for example, until all are dead. In addition, subjects may leave the study prematurely. Such situations lead to what is called censored observations as complete information is not available for these subjects. The data set is thus an assemblage of times to the event in question and times after which no more information on the individual is available. Survival analysis methods are the only techniques capable of handling censored observations without treating them as missing data. They also make no assumption regarding normal distribution of time to event data. Descriptive methods for exploring survival times in a sample include life table and Kaplan–Meier techniques as well as various kinds of distribution fitting as advanced modeling techniques. The Kaplan–Meier cumulative survival probability over time plot has become the signature plot for biomedical survival analysis. Several techniques are available for comparing the survival experience in two or more groups – the log-rank test is popularly used. This test can also be used to produce an odds ratio as an estimate of risk of the event in the test group; this is called hazard ratio (HR. Limitations of the traditional log-rank test have led to various modifications and enhancements. Finally, survival analysis offers different regression models for estimating the impact of multiple predictors on survival. Cox's proportional hazard model is the most general of the regression methods that allows the hazard function to be modeled on a set of explanatory variables without making restrictive assumptions concerning the

  11. Additive survival least square support vector machines: A simulation study and its application to cervical cancer prediction

    Science.gov (United States)

    Khotimah, Chusnul; Purnami, Santi Wulan; Prastyo, Dedy Dwi; Chosuvivatwong, Virasakdi; Sriplung, Hutcha

    2017-11-01

    Support Vector Machines (SVMs) has been widely applied for prediction in many fields. Recently, SVM is also developed for survival analysis. In this study, Additive Survival Least Square SVM (A-SURLSSVM) approach is used to analyze cervical cancer dataset and its performance is compared with the Cox model as a benchmark. The comparison is evaluated based on the prognostic index produced: concordance index (c-index), log rank, and hazard ratio. The higher prognostic index represents the better performance of the corresponding methods. This work also applied feature selection to choose important features using backward elimination technique based on the c-index criterion. The cervical cancer dataset consists of 172 patients. The empirical results show that nine out of the twelve features: age at marriage, age of first getting menstruation, age, parity, type of treatment, history of family planning, stadium, long-time of menstruation, and anemia status are selected as relevant features that affect the survival time of cervical cancer patients. In addition, the performance of the proposed method is evaluated through a simulation study with the different number of features and censoring percentages. Two out of three performance measures (c-index and hazard ratio) obtained from A-SURLSSVM consistently yield better results than the ones obtained from Cox model when it is applied on both simulated and cervical cancer data. Moreover, the simulation study showed that A-SURLSSVM performs better when the percentage of censoring data is small.

  12. Model Proposition for the Fiscal Policies Analysis Applied in Economic Field

    Directory of Open Access Journals (Sweden)

    Larisa Preda

    2007-05-01

    Full Text Available This paper presents a study about fiscal policy applied in economic development. Correlations between macroeconomics and fiscal indicators signify the first steep in our analysis. Next step is a new model proposal for the fiscal and budgetary choices. This model is applied on the date of the Romanian case.

  13. Neuregulin-1/erbB-activation improves cardiac function and survival in models of ischemic, dilated, and viral cardiomyopathy.

    Science.gov (United States)

    Liu, Xifu; Gu, Xinhua; Li, Zhaoming; Li, Xinyan; Li, Hui; Chang, Jianjie; Chen, Ping; Jin, Jing; Xi, Bing; Chen, Denghong; Lai, Donna; Graham, Robert M; Zhou, Mingdong

    2006-10-03

    We evaluated the therapeutic potential of a recombinant 61-residue neuregulin-1 (beta2a isoform) receptor-active peptide (rhNRG-1) in multiple animal models of heart disease. Activation of the erbB family of receptor tyrosine kinases by rhNRG-1 could provide a treatment option for heart failure, because neuregulin-stimulated erbB2/erbB4 heterodimerization is not only critical for myocardium formation in early heart development but prevents severe dysfunction of the adult heart and premature death. Disabled erbB-signaling is also implicated in the transition from compensatory hypertrophy to failure, whereas erbB receptor-activation promotes myocardial cell growth and survival and protects against anthracycline-induced cardiomyopathy. rhNRG-1 was administered IV to animal models of ischemic, dilated, and viral cardiomyopathy, and cardiac function and survival were evaluated. Short-term intravenous administration of rhNRG-1 to normal dogs and rats did not alter hemodynamics or cardiac contractility. In contrast, rhNRG-1 improved cardiac performance, attenuated pathological changes, and prolonged survival in rodent models of ischemic, dilated, and viral cardiomyopathy, with the survival benefits in the ischemic model being additive to those of angiotensin-converting enzyme inhibitor therapy. In addition, despite continued pacing, rhNRG-1 produced global improvements in cardiac function in a canine model of pacing-induced heart failure. These beneficial effects make rhNRG-1 promising as a broad-spectrum therapeutic for the treatment of heart failure due to a variety of common cardiac diseases.

  14. Factors associated with supermarket and convenience store closure: a discrete time spatial survival modelling approach.

    Science.gov (United States)

    Warren, Joshua L; Gordon-Larsen, Penny

    2018-06-01

    While there is a literature on the distribution of food stores across geographic and social space, much of this research uses cross-sectional data. Analyses attempting to understand whether the availability of stores across neighborhoods is associated with diet and/or health outcomes are limited by a lack of understanding of factors that shape the emergence of new stores and the closure of others. We used quarterly data on supermarket and convenience store locations spanning seven years (2006-2012) and tract-level census data in four US cities: Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; San Francisco, California. A spatial discrete-time survival model was used to identify factors associated with an earlier and/or later closure time of a store. Sales volume was typically the strongest indicator of store survival. We identified heterogeneity in the association between tract-level poverty and racial composition with respect to store survival. Stores in high poverty, non-White tracts were often at a disadvantage in terms of survival length. The observed patterns of store survival varied by some of the same neighborhood sociodemographic factors associated with lifestyle and health outcomes, which could lead to confusion in interpretation in studies of the estimated effects of introduction of food stores into neighborhoods on health.

  15. Modeling in applied sciences a kinetic theory approach

    CERN Document Server

    Pulvirenti, Mario

    2000-01-01

    Modeling complex biological, chemical, and physical systems, in the context of spatially heterogeneous mediums, is a challenging task for scientists and engineers using traditional methods of analysis Modeling in Applied Sciences is a comprehensive survey of modeling large systems using kinetic equations, and in particular the Boltzmann equation and its generalizations An interdisciplinary group of leading authorities carefully develop the foundations of kinetic models and discuss the connections and interactions between model theories, qualitative and computational analysis and real-world applications This book provides a thoroughly accessible and lucid overview of the different aspects, models, computations, and methodology for the kinetic-theory modeling process Topics and Features * Integrated modeling perspective utilized in all chapters * Fluid dynamics of reacting gases * Self-contained introduction to kinetic models * Becker–Doring equations * Nonlinear kinetic models with chemical reactions * Kinet...

  16. The Limitations of Applying Rational Decision-Making Models

    African Journals Online (AJOL)

    decision-making models as applied to child spacing and more. specificaDy to the use .... also assumes that the individual operates as a rational decision- making organism in ..... work involves: Motivation; Counselling; Distribution ofIEC mate-.

  17. SURVIVAL OF MICROORGANISMS FROM MODERN PROBIOTICS IN MODEL CONDITIONS OF THE INTESTINE

    Directory of Open Access Journals (Sweden)

    Kabluchko TV

    2017-03-01

    Full Text Available Introduction. The staye of intestinal microflora affects the work of the whole organism. When composition of normal ibtestine microflora changes, its restoration is required. In our days a wide variety of probiotic drugs are available on the market which can be used to solve this problem. Most bacteria having probiotic properties represent the families Lactobacillus and Bifidobacterium, which have poor resistance to acidic content of the stomach and toxic effects of bile salts. Various studies have clearly shown that in a person with normal acidic and bile secretion, the lactobacilli and bifidobacteria are not detected after the passage through the duodenum, i.e., they perish before reaching the small intestines. In this study we compared the survival of different microorganisms which are contained in 9 probiotic drugs in a model of gastric and intestinal environments. Material and methods. In the laboratory of SI: “Mechnikov Institute Microbiology and Immunology, National Ukrainian Academy Medical Sciences" the in vitro experiments have been evaluated to test the ability of different probiotic bacteria which were contained in 9 probiotic drugs to survive the impact of the model environment of the stomach and duodenum. Bacillus coagulans persistence was evaluated under impact of simulated environment of the stomach and duodenum, it also was assessed by the quantity of CFU by incubation on culture medium. The following were studied: Lactobacillus acidophilus, Lactobacillus rhamnosus, Lactobacillus reuteri, Lactobacillus casei, Lactobacillus plantarum, Lactobacillus bulgaricus, Bifidobacterium bifidum, Bifidobacterium longum , Bifidobacterium breve, Bifidobacterium infantis, Bifidobacterium animalis subsp. Lactis BB-12, Saccharomyces boulardii, Bacillus coagulans, Bacillus clausii, Enterococcus faecium. Microorganisms were incubated for 3 hours in a model environment of the stomach (pepsin 3 g / l, hydrochloric acid of 160 mmol / l, pH 2

  18. Linear mixing model applied to coarse resolution satellite data

    Science.gov (United States)

    Holben, Brent N.; Shimabukuro, Yosio E.

    1992-01-01

    A linear mixing model typically applied to high resolution data such as Airborne Visible/Infrared Imaging Spectrometer, Thematic Mapper, and Multispectral Scanner System is applied to the NOAA Advanced Very High Resolution Radiometer coarse resolution satellite data. The reflective portion extracted from the middle IR channel 3 (3.55 - 3.93 microns) is used with channels 1 (0.58 - 0.68 microns) and 2 (0.725 - 1.1 microns) to run the Constrained Least Squares model to generate fraction images for an area in the west central region of Brazil. The derived fraction images are compared with an unsupervised classification and the fraction images derived from Landsat TM data acquired in the same day. In addition, the relationship betweeen these fraction images and the well known NDVI images are presented. The results show the great potential of the unmixing techniques for applying to coarse resolution data for global studies.

  19. Development and internal validation of a prognostic model to predict recurrence free survival in patients with adult granulosa cell tumors of the ovary

    NARCIS (Netherlands)

    van Meurs, Hannah S.; Schuit, Ewoud; Horlings, Hugo M.; van der Velden, Jacobus; van Driel, Willemien J.; Mol, Ben Willem J.; Kenter, Gemma G.; Buist, Marrije R.

    2014-01-01

    Models to predict the probability of recurrence free survival exist for various types of malignancies, but a model for recurrence free survival in individuals with an adult granulosa cell tumor (GCT) of the ovary is lacking. We aimed to develop and internally validate such a prognostic model. We

  20. Model description and evaluation of the mark-recapture survival model used to parameterize the 2012 status and threats analysis for the Florida manatee (Trichechus manatus latirostris)

    Science.gov (United States)

    Langtimm, Catherine A.; Kendall, William L.; Beck, Cathy A.; Kochman, Howard I.; Teague, Amy L.; Meigs-Friend, Gaia; Peñaloza, Claudia L.

    2016-11-30

    This report provides supporting details and evidence for the rationale, validity and efficacy of a new mark-recapture model, the Barker Robust Design, to estimate regional manatee survival rates used to parameterize several components of the 2012 version of the Manatee Core Biological Model (CBM) and Threats Analysis (TA).  The CBM and TA provide scientific analyses on population viability of the Florida manatee subspecies (Trichechus manatus latirostris) for U.S. Fish and Wildlife Service’s 5-year reviews of the status of the species as listed under the Endangered Species Act.  The model evaluation is presented in a standardized reporting framework, modified from the TRACE (TRAnsparent and Comprehensive model Evaluation) protocol first introduced for environmental threat analyses.  We identify this new protocol as TRACE-MANATEE SURVIVAL and this model evaluation specifically as TRACE-MANATEE SURVIVAL, Barker RD version 1. The longer-term objectives of the manatee standard reporting format are to (1) communicate to resource managers consistent evaluation information over sequential modeling efforts; (2) build understanding and expertise on the structure and function of the models; (3) document changes in model structures and applications in response to evolving management objectives, new biological and ecological knowledge, and new statistical advances; and (4) provide greater transparency for management and research review.

  1. The mass effect model of the survival rate's dose effect of organism irradiated with low energy ion beam

    International Nuclear Information System (INIS)

    Shao Chunlin; Gui Qifu; Yu Zengliang

    1995-01-01

    The main characteristic of the low energy ions mutation is its mass deposition effect. Basing on the theory of 'double strand breaking' and the 'mass deposition effect', the authors suggests that the mass deposition products can repair or further damage the double strand breaking of DNA. According to this consideration the dose effect model of the survival rate of organism irradiated by low energy of N + ion beam is deduced as: S exp{-p[αφ + βφ 2 -Rφ 2 exp(-kφ)-Lφ 3 exp(-kφ)]}, which can be called 'mass effect model'. In the low energy ion beam mutation, the dose effects of many survival rates that can not be imitated by previous models are successfully imitated by this model. The suitable application fields of the model are also discussed

  2. Unified Modeling of Discrete Event and Control Systems Applied in Manufacturing

    Directory of Open Access Journals (Sweden)

    Amanda Arêas de Souza

    2015-05-01

    Full Text Available For the development of both a simulation modeland a control system, it is necessary to build, inadvance, a conceptual model. This is what isusually suggested by the methodologies applied inprojects of this nature. Some conceptual modelingtechniques allow for a better understanding ofthe simulation model, and a clear descriptionof the logic of control systems. Therefore, thispaper aims to present and evaluate conceptuallanguages for unified modeling of models ofdiscrete event simulation and control systemsapplied in manufacturing. The results show thatthe IDEF-SIM language can be applied both insimulation systems and in process control.

  3. Intracranial AAV-sTRAIL combined with lanatoside C prolongs survival in an orthotopic xenograft mouse model of invasive glioblastoma.

    Science.gov (United States)

    Crommentuijn, Matheus H W; Maguire, Casey A; Niers, Johanna M; Vandertop, W Peter; Badr, Christian E; Würdinger, Thomas; Tannous, Bakhos A

    2016-04-01

    Glioblastoma (GBM) is the most common malignant brain tumor in adults. We designed an adeno-associated virus (AAV) vector for intracranial delivery of secreted, soluble tumor necrosis factor-related apoptosis-inducing ligand (sTRAIL) to GBM tumors in mice and combined it with the TRAIL-sensitizing cardiac glycoside, lanatoside C (lan C). We applied this combined therapy to two different GBM models using human U87 glioma cells and primary patient-derived GBM neural spheres in culture and in orthotopic GBM xenograft models in mice. In U87 cells, conditioned medium from AAV2-sTRAIL expressing cells combined with lan C induced 80% cell death. Similarly, lan C sensitized primary GBM spheres to sTRAIL causing over 90% cell death. In mice bearing intracranial U87 tumors treated with AAVrh.8-sTRAIL, administration of lan C caused a decrease in tumor-associated Fluc signal, while tumor size increased within days of stopping the treatment. Another round of lan C treatment re-sensitized GBM tumor to sTRAIL-induced cell death. AAVrh.8-sTRAIL treatment alone and combined with lanatoside C resulted in a significant decrease in tumor growth and longer survival of mice bearing orthotopic invasive GBM brain tumors. In summary, AAV-sTRAIL combined with lanatoside C induced cell death in U87 glioma cells and patient-derived GBM neural spheres in culture and in vivo leading to an increased in overall mice survival. Copyright © 2015 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

  4. Practical Findings from Applying the PSD Model for Evaluating Software Design Specifications

    Science.gov (United States)

    Räisänen, Teppo; Lehto, Tuomas; Oinas-Kukkonen, Harri

    This paper presents practical findings from applying the PSD model to evaluating the support for persuasive features in software design specifications for a mobile Internet device. On the one hand, our experiences suggest that the PSD model fits relatively well for evaluating design specifications. On the other hand, the model would benefit from more specific heuristics for evaluating each technique to avoid unnecessary subjectivity. Better distinction between the design principles in the social support category would also make the model easier to use. Practitioners who have no theoretical background can apply the PSD model to increase the persuasiveness of the systems they design. The greatest benefit of the PSD model for researchers designing new systems may be achieved when it is applied together with a sound theory, such as the Elaboration Likelihood Model. Using the ELM together with the PSD model, one may increase the chances for attitude change.

  5. Improving breast cancer survival analysis through competition-based multidimensional modeling.

    Directory of Open Access Journals (Sweden)

    Erhan Bilal

    Full Text Available Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.

  6. Applied research in uncertainty modeling and analysis

    CERN Document Server

    Ayyub, Bilal

    2005-01-01

    Uncertainty has been a concern to engineers, managers, and scientists for many years. For a long time uncertainty has been considered synonymous with random, stochastic, statistic, or probabilistic. Since the early sixties views on uncertainty have become more heterogeneous. In the past forty years numerous tools that model uncertainty, above and beyond statistics, have been proposed by several engineers and scientists. The tool/method to model uncertainty in a specific context should really be chosen by considering the features of the phenomenon under consideration, not independent of what is known about the system and what causes uncertainty. In this fascinating overview of the field, the authors provide broad coverage of uncertainty analysis/modeling and its application. Applied Research in Uncertainty Modeling and Analysis presents the perspectives of various researchers and practitioners on uncertainty analysis and modeling outside their own fields and domain expertise. Rather than focusing explicitly on...

  7. Assessing the effect of quantitative and qualitative predictors on gastric cancer individuals survival using hierarchical artificial neural network models.

    Science.gov (United States)

    Amiri, Zohreh; Mohammad, Kazem; Mahmoudi, Mahmood; Parsaeian, Mahbubeh; Zeraati, Hojjat

    2013-01-01

    There are numerous unanswered questions in the application of artificial neural network models for analysis of survival data. In most studies, independent variables have been studied as qualitative dichotomous variables, and results of using discrete and continuous quantitative, ordinal, or multinomial categorical predictive variables in these models are not well understood in comparison to conventional models. This study was designed and conducted to examine the application of these models in order to determine the survival of gastric cancer patients, in comparison to the Cox proportional hazards model. We studied the postoperative survival of 330 gastric cancer patients who suffered surgery at a surgical unit of the Iran Cancer Institute over a five-year period. Covariates of age, gender, history of substance abuse, cancer site, type of pathology, presence of metastasis, stage, and number of complementary treatments were entered in the models, and survival probabilities were calculated at 6, 12, 18, 24, 36, 48, and 60 months using the Cox proportional hazards and neural network models. We estimated coefficients of the Cox model and the weights in the neural network (with 3, 5, and 7 nodes in the hidden layer) in the training group, and used them to derive predictions in the study group. Predictions with these two methods were compared with those of the Kaplan-Meier product limit estimator as the gold standard. Comparisons were performed with the Friedman and Kruskal-Wallis tests. Survival probabilities at different times were determined using the Cox proportional hazards and a neural network with three nodes in the hidden layer; the ratios of standard errors with these two methods to the Kaplan-Meier method were 1.1593 and 1.0071, respectively, revealed a significant difference between Cox and Kaplan-Meier (P neural network, and the neural network and the standard (Kaplan-Meier), as well as better accuracy for the neural network (with 3 nodes in the hidden layer

  8. Model structure of the stream salmonid simulator (S3)—A dynamic model for simulating growth, movement, and survival of juvenile salmonids

    Science.gov (United States)

    Perry, Russell W.; Plumb, John M.; Jones, Edward C.; Som, Nicholas A.; Hetrick, Nicholas J.; Hardy, Thomas B.

    2018-04-06

    Fisheries and water managers often use population models to aid in understanding the effect of alternative water management or restoration actions on anadromous fish populations. We developed the Stream Salmonid Simulator (S3) to help resource managers evaluate the effect of management alternatives on juvenile salmonid populations. S3 is a deterministic stage-structured population model that tracks daily growth, movement, and survival of juvenile salmon. A key theme of the model is that river flow affects habitat availability and capacity, which in turn drives density dependent population dynamics. To explicitly link population dynamics to habitat quality and quantity, the river environment is constructed as a one-dimensional series of linked habitat units, each of which has an associated daily time series of discharge, water temperature, and usable habitat area or carrying capacity. The physical characteristics of each habitat unit and the number of fish occupying each unit, in turn, drive survival and growth within each habitat unit and movement of fish among habitat units.The purpose of this report is to outline the underlying general structure of the S3 model that is common among different applications of the model. We have developed applications of the S3 model for juvenile fall Chinook salmon (Oncorhynchus tshawytscha) in the lower Klamath River. Thus, this report is a companion to current application of the S3 model to the Trinity River (in review). The general S3 model structure provides a biological and physical framework for the salmonid freshwater life cycle. This framework captures important demographics of juvenile salmonids aimed at translating management alternatives into simulated population responses. Although the S3 model is built on this common framework, the model has been constructed to allow much flexibility in application of the model to specific river systems. The ability for practitioners to include system-specific information for the

  9. Genetic introgression and the survival of Florida panther kittens

    Science.gov (United States)

    Hostetler, Jeffrey A.; Onorato, David P.; Nichols, James D.; Johnson, Warren E.; Roelke, Melody E.; O'Brien, Stephen J.; Jansen, Deborah; Oli, Madan K.

    2010-01-01

    Estimates of survival for the young of a species are critical for population models. These models can often be improved by determining the effects of management actions and population abundance on this demographic parameter. We used multiple sources of data collected during 1982–2008 and a live-recapture dead-recovery modeling framework to estimate and model survival of Florida panther (Puma concolor coryi) kittens (age 0–1 year). Overall, annual survival of Florida panther kittens was 0.323 ± 0.071 (SE), which was lower than estimates used in previous population models. In 1995, female pumas from Texas (P. c. stanleyana) were released into occupied panther range as part of an intentional introgression program to restore genetic variability. We found that kitten survival generally increased with degree of admixture: F1 admixed and backcrossed to Texas kittens survived better than canonical Florida panther and backcrossed to canonical kittens. Average heterozygosity positively influenced kitten and older panther survival, whereas index of panther abundance negatively influenced kitten survival. Our results provide strong evidence for the positive population-level impact of genetic introgression on Florida panthers. Our approach to integrate data from multiple sources was effective at improving robustness as well as precision of estimates of Florida panther kitten survival, and can be useful in estimating vital rates for other elusive species with sparse data.

  10. Predicting functional decline and survival in amyotrophic lateral sclerosis.

    Science.gov (United States)

    Ong, Mei-Lyn; Tan, Pei Fang; Holbrook, Joanna D

    2017-01-01

    Better predictors of amyotrophic lateral sclerosis disease course could enable smaller and more targeted clinical trials. Partially to address this aim, the Prize for Life foundation collected de-identified records from amyotrophic lateral sclerosis sufferers who participated in clinical trials of investigational drugs and made them available to researchers in the PRO-ACT database. In this study, time series data from PRO-ACT subjects were fitted to exponential models. Binary classes for decline in the total score of amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) (fast/slow progression) and survival (high/low death risk) were derived. Data was segregated into training and test sets via cross validation. Learning algorithms were applied to the demographic, clinical and laboratory parameters in the training set to predict ALSFRS-R decline and the derived fast/slow progression and high/low death risk categories. The performance of predictive models was assessed by cross-validation in the test set using Receiver Operator Curves and root mean squared errors. A model created using a boosting algorithm containing the decline in four parameters (weight, alkaline phosphatase, albumin and creatine kinase) post baseline, was able to predict functional decline class (fast or slow) with fair accuracy (AUC = 0.82). However similar approaches to build a predictive model for decline class by baseline subject characteristics were not successful. In contrast, baseline values of total bilirubin, gamma glutamyltransferase, urine specific gravity and ALSFRS-R item score-climbing stairs were sufficient to predict survival class. Using combinations of small numbers of variables it was possible to predict classes of functional decline and survival across the 1-2 year timeframe available in PRO-ACT. These findings may have utility for design of future ALS clinical trials.

  11. A new formalism for modelling parameters α and β of the linear-quadratic model of cell survival for hadron therapy

    Science.gov (United States)

    Vassiliev, Oleg N.; Grosshans, David R.; Mohan, Radhe

    2017-10-01

    We propose a new formalism for calculating parameters α and β of the linear-quadratic model of cell survival. This formalism, primarily intended for calculating relative biological effectiveness (RBE) for treatment planning in hadron therapy, is based on a recently proposed microdosimetric revision of the single-target multi-hit model. The main advantage of our formalism is that it reliably produces α and β that have correct general properties with respect to their dependence on physical properties of the beam, including the asymptotic behavior for very low and high linear energy transfer (LET) beams. For example, in the case of monoenergetic beams, our formalism predicts that, as a function of LET, (a) α has a maximum and (b) the α/β ratio increases monotonically with increasing LET. No prior models reviewed in this study predict both properties (a) and (b) correctly, and therefore, these prior models are valid only within a limited LET range. We first present our formalism in a general form, for polyenergetic beams. A significant new result in this general case is that parameter β is represented as an average over the joint distribution of energies E 1 and E 2 of two particles in the beam. This result is consistent with the role of the quadratic term in the linear-quadratic model. It accounts for the two-track mechanism of cell kill, in which two particles, one after another, damage the same site in the cell nucleus. We then present simplified versions of the formalism, and discuss predicted properties of α and β. Finally, to demonstrate consistency of our formalism with experimental data, we apply it to fit two sets of experimental data: (1) α for heavy ions, covering a broad range of LETs, and (2) β for protons. In both cases, good agreement is achieved.

  12. Working Conditions and Factory Survival: Evidence from Better Factories Cambodia

    OpenAIRE

    Robertson, Raymond; Brown, Drusilla; Dehejia, Rajeev

    2016-01-01

    A large and growing literature has identified several conditions, including exporting, that contribute to plant survival. A prevailing sentiment suggests that anti-sweatshop activity against plants in developing countries adds the risk of making survival more difficult by imposing external constraints that may interfere with optimizing behavior. Using a relatively new plant-level panel dataset from Cambodia, this paper applies survival analysis to estimate the relationship between changes in ...

  13. Living donor risk model for predicting kidney allograft and patient survival in an emerging economy.

    Science.gov (United States)

    Zafar, Mirza Naqi; Wong, Germaine; Aziz, Tahir; Abbas, Khawar; Adibul Hasan Rizvi, S

    2018-03-01

    Living donor kidney is the main source of donor organs in low to middle income countries. We aimed to develop a living donor risk model that predicts graft and patient survival in an emerging economy. We used data from the Sindh Institute of Urology and Transplantation (SIUT) database (n = 2283 recipients and n = 2283 living kidney donors, transplanted between 1993 and 2009) and conducted Cox proportional hazard analyses to develop a composite score that predicts graft and patient survivals. Donor factors age, creatinine clearance, nephron dose (estimated by donor/recipient body weight ratio) and human leukocyte antigen (HLA) match were included in the living donor risk model. The adjusted hazard ratios (HRs) for graft failures among those who received a kidney with living donor scores (reference to donor score of zero) of 1, 2, 3 and 4 were 1.14 (95%CI: 0.94-1.39), 1.24 (95%CI:1.03-1.49), 1.25 (95%CI:1.03-1.51) and 1.36 (95%CI:1.08-1.72) (P-value for trend =0.05). Similar findings were observed for patient survival. Similar to findings in high income countries, our study suggests that donor characteristics such as age, nephron dose, creatinine clearance and HLA match are important factors that determine the long-term patient and graft survival in low income countries. However, other crucial but undefined factors may play a role in determining the overall risk of graft failure and mortality in living kidney donor transplant recipients. © 2016 Asian Pacific Society of Nephrology.

  14. Demisability and survivability sensitivity to design-for-demise techniques

    Science.gov (United States)

    Trisolini, Mirko; Lewis, Hugh G.; Colombo, Camilla

    2018-04-01

    The paper is concerned with examining the effects that design-for-demise solutions can have not only on the demisability of components, but also on their survivability that is their capability to withstand impacts from space debris. First two models are introduced. A demisability model to predict the behaviour of spacecraft components during the atmospheric re-entry and a survivability model to assess the vulnerability of spacecraft structures against space debris impacts. Two indices that evaluate the level of demisability and survivability are also proposed. The two models are then used to study the sensitivity of the demisability and of the survivability indices as a function of typical design-for-demise options. The demisability and the survivability can in fact be influenced by the same design parameters in a competing fashion that is while the demisability is improved, the survivability is worsened and vice versa. The analysis shows how the design-for-demise solutions influence the demisability and the survivability independently. In addition, the effect that a solution has simultaneously on the two criteria is assessed. Results shows which, among the design-for-demise parameters mostly influence the demisability and the survivability. For such design parameters maps are presented, describing their influence on the demisability and survivability indices. These maps represent a useful tool to quickly assess the level of demisability and survivability that can be expected from a component, when specific design parameters are changed.

  15. Intrastriatal Grafting of Chromospheres: Survival and Functional Effects in the 6-OHDA Rat Model of Parkinson's Disease.

    Directory of Open Access Journals (Sweden)

    Alejandra Boronat-García

    Full Text Available Cell replacement therapy in Parkinson's disease (PD aims at re-establishing dopamine neurotransmission in the striatum by grafting dopamine-releasing cells. Chromaffin cell (CC grafts produce some transitory improvements of functional motor deficits in PD animal models, and have the advantage of allowing autologous transplantation. However, CC grafts have exhibited low survival, poor functional effects and dopamine release compared to other cell types. Recently, chromaffin progenitor-like cells were isolated from bovine and human adult adrenal medulla. Under low-attachment conditions, these cells aggregate and grow as spheres, named chromospheres. Here, we found that bovine-derived chromosphere-cell cultures exhibit a greater fraction of cells with a dopaminergic phenotype and higher dopamine release than CC. Chromospheres grafted in a rat model of PD survived in 57% of the total grafted animals. Behavioral tests showed that surviving chromosphere cells induce a reduction in motor alterations for at least 3 months after grafting. Finally, we found that compared with CC, chromosphere grafts survive more and produce more robust and consistent motor improvements. However, further experiments would be necessary to determine whether the functional benefits induced by chromosphere grafts can be improved, and also to elucidate the mechanisms underlying the functional effects of the grafts.

  16. Survival modeling for the estimation of transition probabilities in model-based economic evaluations in the absence of individual patient data: a tutorial.

    Science.gov (United States)

    Diaby, Vakaramoko; Adunlin, Georges; Montero, Alberto J

    2014-02-01

    Survival modeling techniques are increasingly being used as part of decision modeling for health economic evaluations. As many models are available, it is imperative for interested readers to know about the steps in selecting and using the most suitable ones. The objective of this paper is to propose a tutorial for the application of appropriate survival modeling techniques to estimate transition probabilities, for use in model-based economic evaluations, in the absence of individual patient data (IPD). An illustration of the use of the tutorial is provided based on the final progression-free survival (PFS) analysis of the BOLERO-2 trial in metastatic breast cancer (mBC). An algorithm was adopted from Guyot and colleagues, and was then run in the statistical package R to reconstruct IPD, based on the final PFS analysis of the BOLERO-2 trial. It should be emphasized that the reconstructed IPD represent an approximation of the original data. Afterwards, we fitted parametric models to the reconstructed IPD in the statistical package Stata. Both statistical and graphical tests were conducted to verify the relative and absolute validity of the findings. Finally, the equations for transition probabilities were derived using the general equation for transition probabilities used in model-based economic evaluations, and the parameters were estimated from fitted distributions. The results of the application of the tutorial suggest that the log-logistic model best fits the reconstructed data from the latest published Kaplan-Meier (KM) curves of the BOLERO-2 trial. Results from the regression analyses were confirmed graphically. An equation for transition probabilities was obtained for each arm of the BOLERO-2 trial. In this paper, a tutorial was proposed and used to estimate the transition probabilities for model-based economic evaluation, based on the results of the final PFS analysis of the BOLERO-2 trial in mBC. The results of our study can serve as a basis for any model

  17. Genetic Determinants Associated With in Vivo Survival of Burkholderia cenocepacia in the Caenorhabditis elegans Model

    KAUST Repository

    Wong, Yee-Chin

    2018-05-29

    A Burkholderia cenocepacia infection usually leads to reduced survival and fatal cepacia syndrome in cystic fibrosis patients. The identification of B. cenocepacia essential genes for in vivo survival is key to designing new anti-infectives therapies. We used the Transposon-Directed Insertion Sequencing (TraDIS) approach to identify genes required for B. cenocepacia survival in the model infection host, Caenorhabditis elegans. A B. cenocepacia J2315 transposon pool of ∼500,000 mutants was used to infect C. elegans. We identified 178 genes as crucial for B. cenocepacia survival in the infected nematode. The majority of these genes code for proteins of unknown function, many of which are encoded by the genomic island BcenGI13, while other gene products are involved in nutrient acquisition, general stress responses and LPS O-antigen biosynthesis. Deletion of the glycosyltransferase gene wbxB and a histone-like nucleoid structuring (H-NS) protein-encoding gene (BCAL0154) reduced bacterial accumulation and attenuated virulence in C. elegans. Further analysis using quantitative RT-PCR indicated that BCAL0154 modulates B. cenocepacia pathogenesis via transcriptional regulation of motility-associated genes including fliC, fliG, flhD, and cheB1. This screen has successfully identified genes required for B. cenocepacia survival within the host-associated environment, many of which are potential targets for developing new antimicrobials.

  18. Genetic Determinants Associated With in Vivo Survival of Burkholderia cenocepacia in the Caenorhabditis elegans Model

    KAUST Repository

    Wong, Yee-Chin; Abd El Ghany, Moataz; Ghazzali, Raeece N. M.; Yap, Soon-Joo; Hoh, Chee-Choong; Pain, Arnab; Nathan, Sheila

    2018-01-01

    A Burkholderia cenocepacia infection usually leads to reduced survival and fatal cepacia syndrome in cystic fibrosis patients. The identification of B. cenocepacia essential genes for in vivo survival is key to designing new anti-infectives therapies. We used the Transposon-Directed Insertion Sequencing (TraDIS) approach to identify genes required for B. cenocepacia survival in the model infection host, Caenorhabditis elegans. A B. cenocepacia J2315 transposon pool of ∼500,000 mutants was used to infect C. elegans. We identified 178 genes as crucial for B. cenocepacia survival in the infected nematode. The majority of these genes code for proteins of unknown function, many of which are encoded by the genomic island BcenGI13, while other gene products are involved in nutrient acquisition, general stress responses and LPS O-antigen biosynthesis. Deletion of the glycosyltransferase gene wbxB and a histone-like nucleoid structuring (H-NS) protein-encoding gene (BCAL0154) reduced bacterial accumulation and attenuated virulence in C. elegans. Further analysis using quantitative RT-PCR indicated that BCAL0154 modulates B. cenocepacia pathogenesis via transcriptional regulation of motility-associated genes including fliC, fliG, flhD, and cheB1. This screen has successfully identified genes required for B. cenocepacia survival within the host-associated environment, many of which are potential targets for developing new antimicrobials.

  19. Cross-Validation of Survival Bump Hunting by Recursive Peeling Methods.

    Science.gov (United States)

    Dazard, Jean-Eudes; Choe, Michael; LeBlanc, Michael; Rao, J Sunil

    2014-08-01

    We introduce a survival/risk bump hunting framework to build a bump hunting model with a possibly censored time-to-event type of response and to validate model estimates. First, we describe the use of adequate survival peeling criteria to build a survival/risk bump hunting model based on recursive peeling methods. Our method called "Patient Recursive Survival Peeling" is a rule-induction method that makes use of specific peeling criteria such as hazard ratio or log-rank statistics. Second, to validate our model estimates and improve survival prediction accuracy, we describe a resampling-based validation technique specifically designed for the joint task of decision rule making by recursive peeling (i.e. decision-box) and survival estimation. This alternative technique, called "combined" cross-validation is done by combining test samples over the cross-validation loops, a design allowing for bump hunting by recursive peeling in a survival setting. We provide empirical results showing the importance of cross-validation and replication.

  20. Impact of censoring on learning Bayesian networks in survival modelling.

    Science.gov (United States)

    Stajduhar, Ivan; Dalbelo-Basić, Bojana; Bogunović, Nikola

    2009-11-01

    Bayesian networks are commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to represent causal relationships, they are an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. Although good procedures for learning Bayesian networks from data have been defined, their performance in learning from censored survival data has not been widely studied. In this paper, we explore how to use these procedures to learn about possible interactions between prognostic factors and their influence on the variate of interest. We study how censoring affects the probability of learning correct Bayesian network structures. Additionally, we analyse the potential usefulness of the learnt models for predicting the time-independent probability of an event of interest. We analysed the influence of censoring with a simulation on synthetic data sampled from randomly generated Bayesian networks. We used two well-known methods for learning Bayesian networks from data: a constraint-based method and a score-based method. We compared the performance of each method under different levels of censoring to those of the naive Bayes classifier and the proportional hazards model. We did additional experiments on several datasets from real-world medical domains. The machine-learning methods treated censored cases in the data as event-free. We report and compare results for several commonly used model evaluation metrics. On average, the proportional hazards method outperformed other methods in most censoring setups. As part of the simulation study, we also analysed structural similarities of the learnt networks. Heavy censoring, as opposed to no censoring, produces up to a 5% surplus and up to 10% missing total arcs. It also produces up to 50% missing arcs that should originally be connected to the variate of interest. Presented methods for learning Bayesian networks from

  1. Generation of a convalescent model of virulent Francisella tularensis infection for assessment of host requirements for survival of tularemia.

    Directory of Open Access Journals (Sweden)

    Deborah D Crane

    Full Text Available Francisella tularensis is a facultative intracellular bacterium and the causative agent of tularemia. Development of novel vaccines and therapeutics for tularemia has been hampered by the lack of understanding of which immune components are required to survive infection. Defining these requirements for protection against virulent F. tularensis, such as strain SchuS4, has been difficult since experimentally infected animals typically die within 5 days after exposure to as few as 10 bacteria. Such a short mean time to death typically precludes development, and therefore assessment, of immune responses directed against virulent F. tularensis. To enable identification of the components of the immune system that are required for survival of virulent F. tularensis, we developed a convalescent model of tularemia in C57Bl/6 mice using low dose antibiotic therapy in which the host immune response is ultimately responsible for clearance of the bacterium. Using this model we demonstrate αβTCR(+ cells, γδTCR(+ cells, and B cells are necessary to survive primary SchuS4 infection. Analysis of mice deficient in specific soluble mediators shows that IL-12p40 and IL-12p35 are essential for survival of SchuS4 infection. We also show that IFN-γ is required for survival of SchuS4 infection since mice lacking IFN-γR succumb to disease during the course of antibiotic therapy. Finally, we found that both CD4(+ and CD8(+ cells are the primary producers of IFN-γand that γδTCR(+ cells and NK cells make a minimal contribution toward production of this cytokine throughout infection. Together these data provide a novel model that identifies key cells and cytokines required for survival or exacerbation of infection with virulent F. tularensis and provides evidence that this model will be a useful tool for better understanding the dynamics of tularemia infection.

  2. Impact of sentinel lymphadenectomy on survival in a murine model of melanoma.

    Science.gov (United States)

    Rebhun, Robert B; Lazar, Alexander J F; Fidler, Isaiah J; Gershenwald, Jeffrey E

    2008-01-01

    Lymphatic mapping and sentinel lymph node biopsy-also termed sentinel lymphadenectomy (SL)-has become a standard of care for patients with primary invasive cutaneous melanoma. This technique has been shown to provide accurate information about the disease status of the regional lymph node basins at risk for metastasis, provide prognostic information, and provide durable regional lymph node control. The potential survival benefit afforded to patients undergoing SL is controversial. Central to this controversy is whether metastasis to regional lymph nodes occurs independent of or prior to widespread hematogenous dissemination. A related area of uncertainty is whether tumor cells residing within regional lymph nodes have increased metastatic potential. We have used a murine model of primary invasive cutaneous melanoma based on injection of B16-BL6 melanoma cells into the pinna to address two questions: (1) does SL plus wide excision of the primary tumor result in a survival advantage over wide excision alone; and (2) do melanoma cells growing within lymph nodes produce a higher incidence of hematogenous metastases than do cells growing at the primary tumor site? We found that SL significantly improved the survival of mice with small primary tumors. We found no difference in the incidence of lung metastases produced by B16-BL6 melanoma cells growing exclusively within regional lymph nodes and cells growing within the pinna.

  3. Application of accelerated failure time models for breast cancer patients' survival in Kurdistan Province of Iran.

    Science.gov (United States)

    Karimi, Asrin; Delpisheh, Ali; Sayehmiri, Kourosh

    2016-01-01

    Breast cancer is the most common cancer and the second common cause of cancer-induced mortalities in Iranian women. There has been a rapid development in hazard models and survival analysis in the last decade. The aim of this study was to evaluate the prognostic factors of overall survival (OS) in breast cancer patients using accelerated failure time models (AFT). This was a retrospective-analytic cohort study. About 313 women with a pathologically proven diagnosis of breast cancer who had been treated during a 7-year period (since January 2006 until March 2014) in Sanandaj City, Kurdistan Province of Iran were recruited. Performance among AFT was assessed using the goodness of fit methods. Discrimination among the exponential, Weibull, generalized gamma, log-logistic, and log-normal distributions was done using Akaik information criteria and maximum likelihood. The 5 years OS was 75% (95% CI = 74.57-75.43). The main results in terms of survival were found for the different categories of the clinical stage covariate, tumor metastasis, and relapse of cancer. Survival time in breast cancer patients without tumor metastasis and relapse were 4, 2-fold longer than other patients with metastasis and relapse, respectively. One of the most important undermining prognostic factors in breast cancer is metastasis; hence, knowledge of the mechanisms of metastasis is necessary to prevent it so occurrence and treatment of metastatic breast cancer and ultimately extend the lifetime of patients.

  4. Methodology to predict long-term cancer survival from short-term data using Tobacco Cancer Risk and Absolute Cancer Cure models

    International Nuclear Information System (INIS)

    Mould, R F; Lederman, M; Tai, P; Wong, J K M

    2002-01-01

    Three parametric statistical models have been fully validated for cancer of the larynx for the prediction of long-term 15, 20 and 25 year cancer-specific survival fractions when short-term follow-up data was available for just 1-2 years after the end of treatment of the last patient. In all groups of cases the treatment period was only 5 years. Three disease stage groups were studied, T1N0, T2N0 and T3N0. The models are the Standard Lognormal (SLN) first proposed by Boag (1949 J. R. Stat. Soc. Series B 11 15-53) but only ever fully validated for cancer of the cervix, Mould and Boag (1975 Br. J. Cancer 32 529-50), and two new models which have been termed Tobacco Cancer Risk (TCR) and Absolute Cancer Cure (ACC). In each, the frequency distribution of survival times of defined groups of cancer deaths is lognormally distributed: larynx only (SLN), larynx and lung (TCR) and all cancers (ACC). All models each have three unknown parameters but it was possible to estimate a value for the lognormal parameter S a priori. By reduction to two unknown parameters the model stability has been improved. The material used to validate the methodology consisted of case histories of 965 patients, all treated during the period 1944-1968 by Dr Manuel Lederman of the Royal Marsden Hospital, London, with follow-up to 1988. This provided a follow-up range of 20- 44 years and enabled predicted long-term survival fractions to be compared with the actual survival fractions, calculated by the Kaplan and Meier (1958 J. Am. Stat. Assoc. 53 457-82) method. The TCR and ACC models are better than the SLN model and for a maximum short-term follow-up of 6 years, the 20 and 25 year survival fractions could be predicted. Therefore the numbers of follow-up years saved are respectively 14 years and 19 years. Clinical trial results using the TCR and ACC models can thus be analysed much earlier than currently possible. Absolute cure from cancer was also studied, using not only the prediction models which

  5. Cost-effectiveness Analysis in R Using a Multi-state Modeling Survival Analysis Framework: A Tutorial.

    Science.gov (United States)

    Williams, Claire; Lewsey, James D; Briggs, Andrew H; Mackay, Daniel F

    2017-05-01

    This tutorial provides a step-by-step guide to performing cost-effectiveness analysis using a multi-state modeling approach. Alongside the tutorial, we provide easy-to-use functions in the statistics package R. We argue that this multi-state modeling approach using a package such as R has advantages over approaches where models are built in a spreadsheet package. In particular, using a syntax-based approach means there is a written record of what was done and the calculations are transparent. Reproducing the analysis is straightforward as the syntax just needs to be run again. The approach can be thought of as an alternative way to build a Markov decision-analytic model, which also has the option to use a state-arrival extended approach. In the state-arrival extended multi-state model, a covariate that represents patients' history is included, allowing the Markov property to be tested. We illustrate the building of multi-state survival models, making predictions from the models and assessing fits. We then proceed to perform a cost-effectiveness analysis, including deterministic and probabilistic sensitivity analyses. Finally, we show how to create 2 common methods of visualizing the results-namely, cost-effectiveness planes and cost-effectiveness acceptability curves. The analysis is implemented entirely within R. It is based on adaptions to functions in the existing R package mstate to accommodate parametric multi-state modeling that facilitates extrapolation of survival curves.

  6. Applied Regression Modeling A Business Approach

    CERN Document Server

    Pardoe, Iain

    2012-01-01

    An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a

  7. Agent-Based Modelling applied to 5D model of the HIV infection

    Directory of Open Access Journals (Sweden)

    Toufik Laroum

    2016-12-01

    The simplest model was the 3D mathematical model. But the complexity of this phenomenon and the diversity of cells and actors which affect its evolution requires the use of new approaches such as multi-agents approach that we have applied in this paper. The results of our simulator on the 5D model are promising because they are consistent with biological knowledge’s. Therefore, the proposed approach is well appropriate to the study of population dynamics in general and could help to understand and predict the dynamics of HIV infection.

  8. Kidney transplant survival in pediatric and young adults

    Directory of Open Access Journals (Sweden)

    Acott Phil

    2011-10-01

    Full Text Available Abstract Background There is a perception that kidney transplant recipients transferred from pediatric centers to adult care have an increased risk of graft loss. It is not clear whether young adults transplanted in adult centers also suffer from high graft loss rates. Methods We examined death censored graft survival in 3 cohorts of young patients transplanted at a single center. Pediatric (PED patients transplanted at the pediatric center were compared to a cohort of young adults (YAD; age 18- Results In a multivariate Cox model for death-censored graft survival, PED survival was statistically similar to the YAD (HR 0.86, 95% CI 0.44, 1.7, p = 0.66, however the ADL cohort (HR 0.45, 95% CI 0.25, 0.82, p = 0.009 demonstrated better survival. Admitted non-adherence rates were not different among cohorts. Patients were transferred within a narrow age window (18.6 ± 1.0 age in years but at a wide range of times from the date of transplantation (5.1 ± 3.5 years and with a wide range of graft function (serum creatinine 182 ± 81 μmol/L. Conclusions The perception that pediatric transfers do poorly reflects advanced graft dysfunction in some at the time of transfer. The evidence also suggests that it is not the transfer of care that is the critical issue but rather recipients, somewhere between the ages of 11-14 and 25, are a unique and vulnerable cohort. Effective strategies to improve outcomes across this age group need to be identified and applied consistently.

  9. Estimation of survival of adult Florida manatees in the Crystal River, at Blue Spring, and on the Atlantic Coast

    Science.gov (United States)

    O'Shea, Thomas J.; Langtimm, Catherine A.; O'Shea, Thomas J.; Ackerman, B.B.; Percival, H. Franklin

    1995-01-01

    We applied Cormack-Jolly-Seber open population models to manatee (Trichechus manatus latirostris) photo-identification databases to estimate adult survival probabilities. The computer programs JOLLY and RECAPCO were used to estimate survival of 677 individuals in three study areas: Crystal River (winters 1977-78 to 1990-91), Blue Spring (winters 1977-78 to 1990-91), and the Atlantic Coast (winters 1984-85 to 1990-91). We also estimated annual survival from observations of 111 manatees tagged for studies with radiotelemetry. Survival estimated from observations with telemetry had broader confidence intervals than survival estimated with the Cormack-Jolly-Seber models. Annual probabilities of capture based on photo-identification records were generally high. The mean annual adult survival estimated from sighting-resighting records was 0.959-0.962 in the Crystal River and 0.936-0.948 at Blue Spring and may be high enough to permit population growth, given the values of other life-history parameters. On the Atlantic Coast, the estimated annual adult survival (range of means = 0.877-0.885) may signify a declining population. However, for several reasons, interpretation of data from the latter study group should be tempered with caution. Adult survivorship seems to be constant with age in all three study groups. No strong differences were apparent between adult survival ofmales and females in the Crystal River or at Blue Spring; the basis of significant differences between sexes on the Atlantic Coast is unclear. Future research into estimating survival with photo-identification and the Cormack-Jolly-Seber models should be vigorously pursued. Estimates of annual survival can provide an additional indication of Florida manatee population status with a stronger statistical basis than aerial counts and carcass totals.

  10. Impact of experimental habitat manipulation on northern bobwhite survival

    Science.gov (United States)

    Peters, David C.; Brooke, Jarred M.; Tanner, Evan P.; Unger, Ashley M.; Keyser, Patrick D.; Harper, Craig A.; Clark, Joseph D.; Morgan, John J.

    2015-01-01

    Habitat management for northern bobwhite (Colinus virginianus) should affect vital rates, but direct linkages with survival are not well documented; therefore, we implemented an experiment to evaluate those responses. We conducted our experiment on a reclaimed surface mine, a novel landscape where conditions were considered sub-optimal because of the dominance of non-native vegetation, such as sericea lespedeza (Lespedeza cuneata), which has been reported to provide marginal habitat for northern bobwhite and may negatively affect survival. Nonetheless, these areas have great potential for contributing to bobwhite conservation because of the amount of early successional cover they provide. Our study site, a 3,330-ha reclaimed surface mine in western Kentucky, consisted of 2 tracts (Sinclair and Ken, 1,471 ha and 1,853 ha, respectively) that served as replicates with each randomly divided into a treatment (i.e., habitat manipulation through a combination of disking, burning, and herbicide application) and an undisturbed control (n = 4 experimental units). Habitat treatments were applied October 2009 to September 2013. We used radio telemetry to monitor northern bobwhite (n = 1,198) during summer (1 Apr–30 Sep) and winter (1 Oct–31 Mar), 2009–2013. We used the known-fate model in Program MARK to evaluate treatment effects on seasonal survival rates. We included biological, home-range, landscape, and microhabitat metrics as covariates to help improve model sensitivity and further elucidate experimental impacts. Survival varied annually, ranging from 0.139 (SE = 0.031) to 0.301 (SE = 0.032), and seasonally (summer, 0.148 [SE = 0.015]; winter, 0.281 [SE = 0.022]). We found a treatment effect (β = 0.256, 95% CI = 0.057–0.456) with a seasonal interaction (β  = −0.598, 95% CI = −0.898 to −0.298) with survival being higher in summer (0.179 [SE = 0.022] vs. 0.109 [SE = 0.019]) and lower in winter (0.233 [SE

  11. Survival rates of birds of tropical and temperate forests: will the dogma survive?

    Science.gov (United States)

    Karr, J.R.; Nichols, J.D.; Klimkiewicz, M.K.; Brawn, J.D.

    1990-01-01

    Survival rates of tropical forest birds are widely assumed to be high relative to the survival rates of temperate forest birds. Much life-history theory is based on this assumption despite the lack of empirical data to support it. We provide the first detailed comparison of survival rates of tropical and temperate forest birds based on extensive data bases and modern capture-recapture models. We find no support for the conventional wisdom. Because clutch size is only one component of reproductive rate, the frequently assumed, simple association between clutch size and adult survival rates should not necessarily be expected. Our results emphasize the need to consider components of fecundity in addition to clutch size when comparing the life histories of tropical and temperate birds and suggest similar considerations in the development of vertebrate life-history theory.

  12. Quantifying Fire Cycle from Dendroecological Records Using Survival Analyses

    Directory of Open Access Journals (Sweden)

    Dominic Cyr

    2016-06-01

    Full Text Available Quantifying fire regimes in the boreal forest ecosystem is crucial for understanding the past and present dynamics, as well as for predicting its future dynamics. Survival analyses have often been used to estimate the fire cycle in eastern Canada because they make it possible to take into account the censored information that is made prevalent by the typically long fire return intervals and the limited scope of the dendroecological methods that are used to quantify them. Here, we assess how the true length of the fire cycle, the short-term temporal variations in fire activity, and the sampling effort affect the accuracy and precision of estimates obtained from two types of parametric survival models, the Weibull and the exponential models, and one non-parametric model obtained with the Cox regression. Then, we apply those results in a case area located in eastern Canada. Our simulation experiment confirms some documented concerns regarding the detrimental effects of temporal variations in fire activity on parametric estimation of the fire cycle. Cox regressions appear to provide the most accurate and robust estimator, being by far the least affected by temporal variations in fire activity. The Cox-based estimate of the fire cycle for the last 300 years in the case study area is 229 years (CI95: 162–407, compared with the likely overestimated 319 years obtained with the commonly used exponential model.

  13. Survival of Five Strains of Shiga Toxigenic Escherichia coli in a Sausage Fermentation Model and Subsequent Sensitivity to Stress from Gastric Acid and Intestinal Fluid

    Directory of Open Access Journals (Sweden)

    Tone Mari Rode

    2017-01-01

    Full Text Available The ability of foodborne pathogens to exhibit adaptive responses to stressful conditions in foods may enhance their survival when passing through the gastrointestinal system. We aimed to determine whether Escherichia coli surviving stresses encountered during a model dry-fermented sausage (DFS production process exhibit enhanced tolerance and survival in an in vitro gastrointestinal model. Salami sausage batters spiked with five E. coli isolates, including enterohaemorrhagic E. coli strains isolated from different DFS outbreaks, were fermented in a model DFS process (20°C, 21 days. Control batters spiked with the same strains were stored at 4°C for the same period. Samples from matured model sausages and controls were thereafter exposed to an in vitro digestion challenge. Gastric exposure (pH 3 resulted in considerably reduced survival of the E. coli strains that had undergone the model DFS process. This reduction continued after entering intestinal challenge (pH 8, but growth resumed after 120 min. When subjected to gastric challenge for 120 min, E. coli that had undergone the DFS process showed about 2.3 log10⁡​ lower survival compared with those kept in sausage batter at 4°C. Our results indicated that E. coli strains surviving a model DFS process exhibited reduced tolerance to subsequent gastric challenge at low pH.

  14. Possibilities and limitations of applying software reliability growth models to safety-critical software

    International Nuclear Information System (INIS)

    Kim, Man Cheol; Jang, Seung Cheol; Ha, Jae Joo

    2007-01-01

    It is generally known that software reliability growth models such as the Jelinski-Moranda model and the Goel-Okumoto's Non-Homogeneous Poisson Process (NHPP) model cannot be applied to safety-critical software due to a lack of software failure data. In this paper, by applying two of the most widely known software reliability growth models to sample software failure data, we demonstrate the possibility of using the software reliability growth models to prove the high reliability of safety-critical software. The high sensitivity of a piece of software's reliability to software failure data, as well as a lack of sufficient software failure data, is also identified as a possible limitation when applying the software reliability growth models to safety-critical software

  15. [Survival time of HIV/AIDS cases and related factors in Beijing, 1995-2015].

    Science.gov (United States)

    Li, Y; Wang, J; He, S F; Chen, J; Lu, H Y

    2017-11-10

    Objective: To analyze the survival time of HIV/AIDS cases and related factors in Beijing from 1995 to 2015. Methods: A retrospective cohort study was conducted to analyze the data of 12 874 HIV/AIDS cases. The data were collected from Chinese HIV/AIDS Comprehensive Information Management System. Life table method was applied to calculate the survival proportion, and Cox proportion hazard regression model were used to identify the factors related with survival time. Results: Among 12 874 HIV/AIDS cases, 303 (2.4%) died of AIDS related diseases; 9 346 (72.6%) received antiretroviral therapy. The average survival time was 226.5 months (95 %CI : 223.0-230.1), and the survival rates of 1, 5, 10, and 15 years were 98.2%, 96.4%, 93.2%, and 91.9% respectively. Multivariate Cox proportion hazard regression model showed that AIDS phase ( HR =1.439, 95 %CI : 1.041-1.989), heterosexual transmission ( HR =1.646, 95 %CI : 1.184-2.289), being married ( HR =2.186, 95 %CI : 1.510-3.164); older age (≥60 years) at diagnosis ( HR =6.608, 95 %CI : 3.546-12.316); lower CD(4)(+)T cell counts at diagnosis (<350 cells/μl) ( HR =8.711, 95 %CI : 5.757-13.181); receiving no antiretroviral therapy (ART) ( HR =18.223, 95 %CI : 13.317-24.937) were the high risk factors influencing the survival of AIDS patients compared with HIV phase, homosexual transmission, being unmarried, younger age (≤30 years), higher CD(4)(+)T cell count (≥350 cell/μl) and receiving ART. Conclusion: The average survival time of HIV/AIDS cases was 226.5 months after diagnoses. Receiving ART, higher CD(4)(+)T cell counts at the first test, HIV phase, younger age, being unmarried and the homosexual transmission were related to the longer survival time of HIV/AIDS cases. Receiving no ART, the lower CD(4)(+)T cell counts at the first test, AIDS phase, older age, being married and heterosexual transmission indicated higher risk of death due to AIDS.

  16. Survival of probiotic lactobacilli in the upper gastrointestinal tract using an in vitro gastric model of digestion.

    Science.gov (United States)

    Lo Curto, Alberto; Pitino, Iole; Mandalari, Giuseppina; Dainty, Jack Richard; Faulks, Richard Martin; John Wickham, Martin Sean

    2011-10-01

    The aim of this study was to investigate survival of three commercial probiotic strains (Lactobacillus casei subsp. shirota, L. casei subsp. immunitas, Lactobacillus acidophilus subsp. johnsonii) in the human upper gastrointestinal (GI) tract using a dynamic gastric model (DGM) of digestion followed by incubation under duodenal conditions. Water and milk were used as food matrices and survival was evaluated in both logarithmic and stationary phase. The % of recovery in logarithmic phase ranged from 1.0% to 43.8% in water for all tested strains, and from 80.5% to 197% in milk. Higher survival was observed in stationary phase for all strains. L. acidophilus subsp. johnsonii showed the highest survival rate in both water (93.9%) and milk (202.4%). Lactic acid production was higher in stationary phase, L. casei subsp. shirota producing the highest concentration (98.2 mM) after in vitro gastric plus duodenal digestion. Copyright © 2011 Elsevier Ltd. All rights reserved.

  17. Nuclear, biological and chemical contamination survivability of Army material

    International Nuclear Information System (INIS)

    Feeney, J.J.

    1987-01-01

    Army Regulation (AR) 70-71, Nuclear, Biological and Chemical (NBC) Contamination Survivability of Army Material, published during 1984, establishes Army policy and procedures for the development and acquisition of material to ensure its survivablility and sustainability on the NBC-contaminated battlefield. This regulation defines NBC contamination as a term that includes both the individual and collective effects of residual radiological, biological, and chemical contamination. AR 70-71 applies to all mission-essential equipment within the Army. NBC contamination survivability is the capability of a system and its crew to withstand an NBC-contaminated environment, including decontamination, without losing the ability to accomplish the assigned mission. Characteristics of NBC contamination survivability are decontaminability, hardness, and compatability. These characteristics are engineering design criteria which are intended for use only in a developmental setting. To comply with AR 70-71, each mission-essential item must address all three criteria. The Department of Defense (DOD) has published a draft instruction addressing acquisition of NBC contamination survivable systems. This instruction will apply throughout DOD to those programs, systems and subsystems designated by the Secretary of Defense as major systems acquisition programs and to those non-major systems that have potential impact on critical functions

  18. Online traffic flow model applying dynamic flow-density relation

    International Nuclear Information System (INIS)

    Kim, Y.

    2002-01-01

    This dissertation describes a new approach of the online traffic flow modelling based on the hydrodynamic traffic flow model and an online process to adapt the flow-density relation dynamically. The new modelling approach was tested based on the real traffic situations in various homogeneous motorway sections and a motorway section with ramps and gave encouraging simulation results. This work is composed of two parts: first the analysis of traffic flow characteristics and second the development of a new online traffic flow model applying these characteristics. For homogeneous motorway sections traffic flow is classified into six different traffic states with different characteristics. Delimitation criteria were developed to separate these states. The hysteresis phenomena were analysed during the transitions between these traffic states. The traffic states and the transitions are represented on a states diagram with the flow axis and the density axis. For motorway sections with ramps the complicated traffic flow is simplified and classified into three traffic states depending on the propagation of congestion. The traffic states are represented on a phase diagram with the upstream demand axis and the interaction strength axis which was defined in this research. The states diagram and the phase diagram provide a basis for the development of the dynamic flow-density relation. The first-order hydrodynamic traffic flow model was programmed according to the cell-transmission scheme extended by the modification of flow dependent sending/receiving functions, the classification of cells and the determination strategy for the flow-density relation in the cells. The unreasonable results of macroscopic traffic flow models, which may occur in the first and last cells in certain conditions are alleviated by applying buffer cells between the traffic data and the model. The sending/receiving functions of the cells are determined dynamically based on the classification of the

  19. The Female Stroke Survival Advantage: Relation to Age

    DEFF Research Database (Denmark)

    Olsen, Tom Skyhøj; Dehlendorff, Christian; Andersen, Klaus Kaae

    2009-01-01

    Background: Age-related hormonal factors are thought to be related to the gender gap in longevity. Testing the hypothesis that survival is best in young premenopausal women we studied the effect of age on 1-week mortality in stroke patients. Methods: A registry was started in 2001 with the aim...... in women. While mortality increased almost linearly in women over the entire age range, it increased steeply in men from the age of 50 and at the age of 80 years survival was 80% better in women. Conclusion: The female stroke survival advantage applies to all ages. It increases with age due to a steeply...

  20. Recent progress and modern challenges in applied mathematics, modeling and computational science

    CERN Document Server

    Makarov, Roman; Belair, Jacques

    2017-01-01

    This volume is an excellent resource for professionals in various areas of applications of mathematics, modeling, and computational science. It focuses on recent progress and modern challenges in these areas. The volume provides a balance between fundamental theoretical and applied developments, emphasizing the interdisciplinary nature of modern trends and detailing state-of-the-art achievements in Applied Mathematics, Modeling, and Computational Science.  The chapters have been authored by international experts in their respective fields, making this book ideal for researchers in academia, practitioners, and graduate students. It can also serve as a reference in the diverse selected areas of applied mathematics, modelling, and computational sciences, and is ideal for interdisciplinary collaborations.

  1. Rapidity gap survival in enhanced Pomeron scheme

    Energy Technology Data Exchange (ETDEWEB)

    Ostapchenko, Sergey [Frankfurt Institute for Advanced Studies, Frankfurt am Main (Germany); Moscow State University, D.V. Skobeltsyn Institute of Nuclear Physics, Moscow (Russian Federation); Bleicher, Marcus [Frankfurt Institute for Advanced Studies, Frankfurt am Main (Germany); Goethe-Universitat, Institute for Theoretical Physics, Frankfurt am Main (Germany)

    2018-01-15

    We apply the phenomenological Reggeon field theory framework to investigate rapidity gap survival (RGS) probability for diffractive dijet production in proton-proton collisions. In particular, we study in some detail rapidity gap suppression due to elastic rescatterings of intermediate partons in the underlying parton cascades, described by enhanced (Pomeron-Pomeron interaction) diagrams. We demonstrate that such contributions play a subdominant role, compared to the usual, so-called ''eikonal'', rapidity gap suppression due to elastic rescatterings of constituent partons of the colliding protons. On the other hand, the overall RGS factor proves to be sensitive to color fluctuations in the proton. Hence, experimental data on diffractive dijet production can be used to constrain the respective model approaches. (orig.)

  2. Incorporating movement patterns to improve survival estimates for juvenile bull trout

    Science.gov (United States)

    Bowerman, Tracy; Budy, Phaedra

    2012-01-01

    Populations of many fish species are sensitive to changes in vital rates during early life stages, but our understanding of the factors affecting growth, survival, and movement patterns is often extremely limited for juvenile fish. These critical information gaps are particularly evident for bull trout Salvelinus confluentus, a threatened Pacific Northwest char. We combined several active and passive mark–recapture and resight techniques to assess migration rates and estimate survival for juvenile bull trout (70–170 mm total length). We evaluated the relative performance of multiple survival estimation techniques by comparing results from a common Cormack–Jolly–Seber (CJS) model, the less widely used Barker model, and a simple return rate (an index of survival). Juvenile bull trout of all sizes emigrated from their natal habitat throughout the year, and thereafter migrated up to 50 km downstream. With the CJS model, high emigration rates led to an extreme underestimate of apparent survival, a combined estimate of site fidelity and survival. In contrast, the Barker model, which allows survival and emigration to be modeled as separate parameters, produced estimates of survival that were much less biased than the return rate. Estimates of age-class-specific annual survival from the Barker model based on all available data were 0.218±0.028 (estimate±SE) for age-1 bull trout and 0.231±0.065 for age-2 bull trout. This research demonstrates the importance of incorporating movement patterns into survival analyses, and we provide one of the first field-based estimates of juvenile bull trout annual survival in relatively pristine rearing conditions. These estimates can provide a baseline for comparison with future studies in more impacted systems and will help managers develop reliable stage-structured population models to evaluate future recovery strategies.

  3. Inverse geothermal modelling applied to Danish sedimentary basins

    Science.gov (United States)

    Poulsen, Søren E.; Balling, Niels; Bording, Thue S.; Mathiesen, Anders; Nielsen, Søren B.

    2017-10-01

    This paper presents a numerical procedure for predicting subsurface temperatures and heat-flow distribution in 3-D using inverse calibration methodology. The procedure is based on a modified version of the groundwater code MODFLOW by taking advantage of the mathematical similarity between confined groundwater flow (Darcy's law) and heat conduction (Fourier's law). Thermal conductivity, heat production and exponential porosity-depth relations are specified separately for the individual geological units of the model domain. The steady-state temperature model includes a model-based transient correction for the long-term palaeoclimatic thermal disturbance of the subsurface temperature regime. Variable model parameters are estimated by inversion of measured borehole temperatures with uncertainties reflecting their quality. The procedure facilitates uncertainty estimation for temperature predictions. The modelling procedure is applied to Danish onshore areas containing deep sedimentary basins. A 3-D voxel-based model, with 14 lithological units from surface to 5000 m depth, was built from digital geological maps derived from combined analyses of reflection seismic lines and borehole information. Matrix thermal conductivity of model lithologies was estimated by inversion of all available deep borehole temperature data and applied together with prescribed background heat flow to derive the 3-D subsurface temperature distribution. Modelled temperatures are found to agree very well with observations. The numerical model was utilized for predicting and contouring temperatures at 2000 and 3000 m depths and for two main geothermal reservoir units, the Gassum (Lower Jurassic-Upper Triassic) and Bunter/Skagerrak (Triassic) reservoirs, both currently utilized for geothermal energy production. Temperature gradients to depths of 2000-3000 m are generally around 25-30 °C km-1, locally up to about 35 °C km-1. Large regions have geothermal reservoirs with characteristic temperatures

  4. Making Faces - State-Space Models Applied to Multi-Modal Signal Processing

    DEFF Research Database (Denmark)

    Lehn-Schiøler, Tue

    2005-01-01

    The two main focus areas of this thesis are State-Space Models and multi modal signal processing. The general State-Space Model is investigated and an addition to the class of sequential sampling methods is proposed. This new algorithm is denoted as the Parzen Particle Filter. Furthermore...... optimizer can be applied to speed up convergence. The linear version of the State-Space Model, the Kalman Filter, is applied to multi modal signal processing. It is demonstrated how a State-Space Model can be used to map from speech to lip movements. Besides the State-Space Model and the multi modal...... application an information theoretic vector quantizer is also proposed. Based on interactions between particles, it is shown how a quantizing scheme based on an analytic cost function can be derived....

  5. An extended gravity model with substitution applied to international trade

    NARCIS (Netherlands)

    Bikker, J.A.|info:eu-repo/dai/nl/06912261X

    The traditional gravity model has been applied many times to international trade flows, especially in order to analyze trade creation and trade diversion. However, there are two fundamental objections to the model: it cannot describe substitutions between flows and it lacks a cogent theoretical

  6. A clinical-molecular prognostic model to predict survival in patients with post polycythemia vera and post essential thrombocythemia myelofibrosis.

    Science.gov (United States)

    Passamonti, F; Giorgino, T; Mora, B; Guglielmelli, P; Rumi, E; Maffioli, M; Rambaldi, A; Caramella, M; Komrokji, R; Gotlib, J; Kiladjian, J J; Cervantes, F; Devos, T; Palandri, F; De Stefano, V; Ruggeri, M; Silver, R T; Benevolo, G; Albano, F; Caramazza, D; Merli, M; Pietra, D; Casalone, R; Rotunno, G; Barbui, T; Cazzola, M; Vannucchi, A M

    2017-12-01

    Polycythemia vera (PV) and essential thrombocythemia (ET) are myeloproliferative neoplasms with variable risk of evolution into post-PV and post-ET myelofibrosis, from now on referred to as secondary myelofibrosis (SMF). No specific tools have been defined for risk stratification in SMF. To develop a prognostic model for predicting survival, we studied 685 JAK2, CALR, and MPL annotated patients with SMF. Median survival of the whole cohort was 9.3 years (95% CI: 8-not reached-NR-). Through penalized Cox regressions we identified negative predictors of survival and according to beta risk coefficients we assigned 2 points to hemoglobin level <11 g/dl, to circulating blasts ⩾3%, and to CALR-unmutated genotype, 1 point to platelet count <150 × 10 9 /l and to constitutional symptoms, and 0.15 points to any year of age. Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM) allocated SMF patients into four risk categories with different survival (P<0.0001): low (median survival NR; 133 patients), intermediate-1 (9.3 years, 95% CI: 8.1-NR; 245 patients), intermediate-2 (4.4 years, 95% CI: 3.2-7.9; 126 patients), and high risk (2 years, 95% CI: 1.7-3.9; 75 patients). Finally, we found that the MYSEC-PM represents the most appropriate tool for SMF decision-making to be used in clinical and trial settings.

  7. Remarks on orthotropic elastic models applied to wood

    Directory of Open Access Journals (Sweden)

    Nilson Tadeu Mascia

    2006-09-01

    Full Text Available Wood is generally considered an anisotropic material. In terms of engineering elastic models, wood is usually treated as an orthotropic material. This paper presents an analysis of two principal anisotropic elastic models that are usually applied to wood. The first one, the linear orthotropic model, where the material axes L (Longitudinal, R( radial and T(tangential are coincident with the Cartesian axes (x, y, z, is more accepted as wood elastic model. The other one, the cylindrical orthotropic model is more adequate of the growth caracteristics of wood but more mathematically complex to be adopted in practical terms. Specifically due to its importance in wood elastic parameters, this paper deals with the fiber orientation influence in these models through adequate transformation of coordinates. As a final result, some examples of the linear model, which show the variation of elastic moduli, i.e., Young´s modulus and shear modulus, with fiber orientation are presented.

  8. Applied systems ecology: models, data, and statistical methods

    Energy Technology Data Exchange (ETDEWEB)

    Eberhardt, L L

    1976-01-01

    In this report, systems ecology is largely equated to mathematical or computer simulation modelling. The need for models in ecology stems from the necessity to have an integrative device for the diversity of ecological data, much of which is observational, rather than experimental, as well as from the present lack of a theoretical structure for ecology. Different objectives in applied studies require specialized methods. The best predictive devices may be regression equations, often non-linear in form, extracted from much more detailed models. A variety of statistical aspects of modelling, including sampling, are discussed. Several aspects of population dynamics and food-chain kinetics are described, and it is suggested that the two presently separated approaches should be combined into a single theoretical framework. It is concluded that future efforts in systems ecology should emphasize actual data and statistical methods, as well as modelling.

  9. Survival of Patients with Oral Cavity Cancer in Germany

    Science.gov (United States)

    Listl, Stefan; Jansen, Lina; Stenzinger, Albrecht; Freier, Kolja; Emrich, Katharina; Holleczek, Bernd; Katalinic, Alexander; Gondos, Adam; Brenner, Hermann

    2013-01-01

    The purpose of the present study was to describe the survival of patients diagnosed with oral cavity cancer in Germany. The analyses relied on data from eleven population-based cancer registries in Germany covering a population of 33 million inhabitants. Patients with a diagnosis of oral cavity cancer (ICD-10: C00-06) between 1997 and 2006 are included. Period analysis for 2002–2006 was applied to estimate five-year age-standardized relative survival, taking into account patients' sex as well as grade and tumor stage. Overall five-year relative survival for oral cavity cancer patients was 54.6%. According to tumor localization, five-year survival was 86.5% for lip cancer, 48.1% for tongue cancer and 51.7% for other regions of the oral cavity. Differences in survival were identified with respect to age, sex, tumor grade and stage. The present study is the first to provide a comprehensive overview on survival of oral cavity cancer patients in Germany. PMID:23349710

  10. A linear-quadratic model of cell survival considering both sublethal and potentially lethal radiation damage

    International Nuclear Information System (INIS)

    Rutz, H.P.; Coucke, P.A.; Mirimanoff, R.O.

    1991-01-01

    The authors assessed the dose-dependence of repair of potentially lethal damage in Chinese hamster ovary cells x-irradiated in vitro. The recovery ratio (RR) by which survival (SF) of the irradiated cells was enhanced increased exponentially with a linear and a quadratic component namely ζ and ψ: RR=exp(ζD+ψD 2 ). Survival of irradiated cells can thus be expressed by a combined linear-quadratic model considering 4 variables, namely α and β for the capacity of the cells to accumulate sublethal damage, and ζ and ψ for their capacity to repair potentially lethal damage: SF=exp((ζ-α)D+ (ψ-β)D 2 ). author. 26 refs.; 1 fig.; 1 tab

  11. Exponential models applied to automated processing of radioimmunoassay standard curves

    International Nuclear Information System (INIS)

    Morin, J.F.; Savina, A.; Caroff, J.; Miossec, J.; Legendre, J.M.; Jacolot, G.; Morin, P.P.

    1979-01-01

    An improved computer processing is described for fitting of radio-immunological standard curves by means of an exponential model on a desk-top calculator. This method has been applied to a variety of radioassays and the results are in accordance with those obtained by more sophisticated models [fr

  12. Effect of external volume expansion on the survival of fat grafts

    Directory of Open Access Journals (Sweden)

    Raghuveer Reddy

    2016-01-01

    Full Text Available Introduction: External volume expansion (EVE is one method, which has been utilised for increasing the survival of adipose tissue grafts. EVE releases positive pressure from the graft and also induces intense levels of edema that decreases diffusion of metabolites essential for graft survival initially. The ideal timing of external volume expansion in relation to the injection of the fat to facilitate survival is not yet clear. Aims and Objectives: This study was undertaken to evaluate and compare the efficacy of external volume expansion applied at variable time points in relation to the injection of the fat. Materials and Methods: Athymic mouse was the animal model and human lipo-aspirate mixed with PRP was used as graft. An indigenous dome shaped silicone device was fabricated to deliver a negative pressure of -30 mm of Hg. The EVE was applied at variable time intervals. At the end of 4 weeks visual, histological and radiological features of the injected fat were compared. The adipose tissue was stained with human vimentin to ascertain the origin of the retained fat. Results: All the grafts, which had EVE, had significantly better volume retention and vascularity. The groups which underwent a delayed EVE or prior expansion followed by concomitant graft injection and expansion showed the most optimal vascularity and graft retention. Conclusions: A delayed EVE or prior expansion followed by concomitant graft injection and expansion may be the most ideal combinations to optimize graft take. However, on account of the relatively small sample size, there was a limitation in drawing statistically significant conclusions for certain variables.

  13. Liquid-drop model applied to heavy ions irradiation

    International Nuclear Information System (INIS)

    De Cicco, Hernan; Alurralde, Martin A.; Saint-Martin, Maria L. G.; Bernaola, Omar A.

    1999-01-01

    Liquid-drop model is used, previously applied in the study of radiation damage in metals, in an energy range not covered by molecular dynamics, in order to understand experimental data of particle tracks in an organic material (Makrofol E), which cannot be accurately described by the existing theoretical methods. The nuclear and electronic energy depositions are considered for each ion considered and the evolution of the thermal explosion is evaluated. The experimental observation of particle tracks in a region previously considered as 'prohibited' are justified. Although the model used has free parameters and some discrepancies with the experimental diametrical values exist, the agreement obtained is highly superior than that of other existing models. (author)

  14. Surface-bounded growth modeling applied to human mandibles

    DEFF Research Database (Denmark)

    Andresen, Per Rønsholt

    1999-01-01

    This thesis presents mathematical and computational techniques for three dimensional growth modeling applied to human mandibles. The longitudinal shape changes make the mandible a complex bone. The teeth erupt and the condylar processes change direction, from pointing predominantly backward...... of the common features. 3.model the process that moves the matched points (growth modeling). A local shape feature called crest line has shown itself to be structurally stable on mandibles. Registration of crest lines (from different mandibles) results in a sparse deformation field, which must be interpolated...... old mandible based on the 3 month old scan. When using successively more recent scans as basis for the model the error drops to 2.0 mm for the 11 years old scan. Thus, it seems reasonable to assume that the mandibular growth is linear....

  15. Survival of Patients with Stomach Cancer and its Determinants in Kurdistan.

    Science.gov (United States)

    Moradi, Ghobad; Karimi, Kohsar; Esmailnasab, Nader; Roshani, Daem

    2016-01-01

    Stomach cancer is the fourth most common cancer and the second leading cause of death from cancer in the world. In Iran, this type of cancer has high rates of incidence and mortality. This study aimed to assess the survival rate of patients with stomach cancer and its determinants in Kurdistan, a province with one of the highest incidence rates of stomach cancer in the country. We studied a total of 202 patients with stomach cancer who were admitted to Tohid Hospital in Sanandaj from 2009 to 2013. Using KaplanMeier nonparametric methods the survival rate of patients was calculated in terms of different levels of age at diagnosis, gender, education, residential area, occupation, underweight, and clinical variables including tumor histology, site of tumor, disease stage, and type of treatment. In addition, we compared the survival rates using the logrank test. Finally, Cox proportional hazards regression was applied using Stata 12 and R 3.1.0 software. The significance level was set at 0.05. The mean age at diagnosis was 64.7 ± 12.0 years. The survival rate of patients with stomach cancer was 43.9% and 7% at the first and the fifth year after diagnosis, respectively. The results of logrank test showed significant relationships between survival and age at diagnosis, education, disease stage, type of treatment, and degree of being underweight (P<0.05). Moreover, according to the results of Cox proportional hazards regression model, the variables of education, disease stage, and type of treatment were associated with patient survival (P<0.05). The survival rate of patients with stomach cancer is low and the prognosis is very poor. Given the poor prognosis of the patients, it is critical to find ways for early diagnosis and facilitating timely access to effective treatment methods.

  16. The sdg interacting-boson model applied to 168Er

    Science.gov (United States)

    Yoshinaga, N.; Akiyama, Y.; Arima, A.

    1986-03-01

    The sdg interacting-boson model is applied to 168Er. Energy levels and E2 transitions are calculated. This model is shown to solve the problem of anharmonicity regarding the excitation energy of the first Kπ=4+ band relative to that of the first Kπ=2+ one. The level scheme including the Kπ=3+ band is well reproduced and the calculated B(E2)'s are consistent with the experimental data.

  17. Predicting long-term graft survival in adult kidney transplant recipients

    Directory of Open Access Journals (Sweden)

    Brett W Pinsky

    2012-01-01

    Full Text Available The ability to accurately predict a population′s long-term survival has important implications for quantifying the benefits of transplantation. To identify a model that can accurately predict a kidney transplant population′s long-term graft survival, we retrospectively studied the United Network of Organ Sharing data from 13,111 kidney-only transplants completed in 1988- 1989. Nineteen-year death-censored graft survival (DCGS projections were calculated and com-pared with the population′s actual graft survival. The projection curves were created using a two-part estimation model that (1 fits a Kaplan-Meier survival curve immediately after transplant (Part A and (2 uses truncated observational data to model a survival function for long-term projection (Part B. Projection curves were examined using varying amounts of time to fit both parts of the model. The accuracy of the projection curve was determined by examining whether predicted sur-vival fell within the 95% confidence interval for the 19-year Kaplan-Meier survival, and the sample size needed to detect the difference in projected versus observed survival in a clinical trial. The 19-year DCGS was 40.7% (39.8-41.6%. Excellent predictability (41.3% can be achieved when Part A is fit for three years and Part B is projected using two additional years of data. Using less than five total years of data tended to overestimate the population′s long-term survival, accurate prediction of long-term DCGS is possible, but requires attention to the quantity data used in the projection method.

  18. Do American dippers obtain a survival benefit from altitudinal migration?

    Science.gov (United States)

    Green, David J; Whitehorne, Ivy B J; Middleton, Holly A; Morrissey, Christy A

    2015-01-01

    Studies of partial migrants provide an opportunity to assess the cost and benefits of migration. Previous work has demonstrated that sedentary American dippers (residents) have higher annual productivity than altitudinal migrants that move to higher elevations to breed. Here we use a ten-year (30 period) mark-recapture dataset to evaluate whether migrants offset their lower productivity with higher survival during the migration-breeding period when they occupy different habitat, or early and late-winter periods when they coexist with residents. Mark-recapture models provide no evidence that apparent monthly survival of migrants is higher than that of residents at any time of the year. The best-supported model suggests that monthly survival is higher in the migration-breeding period than winter periods. Another well-supported model suggested that residency conferred a survival benefit, and annual apparent survival (calculated from model weighted monthly apparent survival estimates using the Delta method) of residents (0.511 ± 0.038SE) was slightly higher than that of migrants (0.487 ± 0.032). Winter survival of American dippers was influenced by environmental conditions; monthly apparent survival increased as maximum daily flow rates increased and declined as winter temperatures became colder. However, we found no evidence that environmental conditions altered differences in winter survival of residents and migrants. Since migratory American dippers have lower productivity and slightly lower survival than residents our data suggests that partial migration is likely an outcome of competition for limited nest sites at low elevations, with less competitive individuals being forced to migrate to higher elevations in order to breed.

  19. A comparison of economic evaluation models as applied to geothermal energy technology

    Science.gov (United States)

    Ziman, G. M.; Rosenberg, L. S.

    1983-01-01

    Several cost estimation and financial cash flow models have been applied to a series of geothermal case studies. In order to draw conclusions about relative performance and applicability of these models to geothermal projects, the consistency of results was assessed. The model outputs of principal interest in this study were net present value, internal rate of return, or levelized breakeven price. The models used were VENVAL, a venture analysis model; the Geothermal Probabilistic Cost Model (GPC Model); the Alternative Power Systems Economic Analysis Model (APSEAM); the Geothermal Loan Guarantee Cash Flow Model (GCFM); and the GEOCOST and GEOCITY geothermal models. The case studies to which the models were applied include a geothermal reservoir at Heber, CA; a geothermal eletric power plant to be located at the Heber site; an alcohol fuels production facility to be built at Raft River, ID; and a direct-use, district heating system in Susanville, CA.

  20. Identification of subgroups by risk of graft failure after paediatric renal transplantation: application of survival tree models on the ESPN/ERA-EDTA Registry.

    Science.gov (United States)

    Lofaro, Danilo; Jager, Kitty J; Abu-Hanna, Ameen; Groothoff, Jaap W; Arikoski, Pekka; Hoecker, Britta; Roussey-Kesler, Gwenaelle; Spasojević, Brankica; Verrina, Enrico; Schaefer, Franz; van Stralen, Karlijn J

    2016-02-01

    Identification of patient groups by risk of renal graft loss might be helpful for accurate patient counselling and clinical decision-making. Survival tree models are an alternative statistical approach to identify subgroups, offering cut-off points for covariates and an easy-to-interpret representation. Within the European Society of Pediatric Nephrology/European Renal Association-European Dialysis and Transplant Association (ESPN/ERA-EDTA) Registry data we identified paediatric patient groups with specific profiles for 5-year renal graft survival. Two analyses were performed, including (i) parameters known at time of transplantation and (ii) additional clinical measurements obtained early after transplantation. The identified subgroups were added as covariates in two survival models. The prognostic performance of the models was tested and compared with conventional Cox regression analyses. The first analysis included 5275 paediatric renal transplants. The best 5-year graft survival (90.4%) was found among patients who received a renal graft as a pre-emptive transplantation or after short-term dialysis (2.2 years). The Cox model including both pre-transplant factors and tree subgroups had a significantly better predictive performance than conventional Cox regression (P 30 mL/min/1.73 m(2) and dialysis 20 months). Also in this case combining tree findings and clinical factors improved the predictive performance as compared with conventional Cox model models (P tree model to be an accurate and attractive tool to predict graft failure for patients with specific characteristics. This may aid the evaluation of individual graft prognosis and thereby the design of measures to improve graft survival in the poor prognosis groups. © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

  1. Survival chance in papillary thyroid cancer in Hungary: individual survival probability estimation using the Markov method

    International Nuclear Information System (INIS)

    Esik, Olga; Tusnady, Gabor; Daubner, Kornel; Nemeth, Gyoergy; Fuezy, Marton; Szentirmay, Zoltan

    1997-01-01

    Purpose: The typically benign, but occasionally rapidly fatal clinical course of papillary thyroid cancer has raised the need for individual survival probability estimation, to tailor the treatment strategy exclusively to a given patient. Materials and methods: A retrospective study was performed on 400 papillary thyroid cancer patients with a median follow-up time of 7.1 years to establish a clinical database for uni- and multivariate analysis of the prognostic factors related to survival (Kaplan-Meier product limit method and Cox regression). For a more precise prognosis estimation, the effect of the most important clinical events were then investigated on the basis of a Markov renewal model. The basic concept of this approach is that each patient has an individual disease course which (besides the initial clinical categories) is affected by special events, e.g. internal covariates (local/regional/distant relapses). On the supposition that these events and the cause-specific death are influenced by the same biological processes, the parameters of transient survival probability characterizing the speed of the course of the disease for each clinical event and their sequence were determined. The individual survival curves for each patient were calculated by using these parameters and the independent significant clinical variables selected from multivariate studies, summation of which resulted in a mean cause-specific survival function valid for the entire group. On the basis of this Markov model, prediction of the cause-specific survival probability is possible for extrastudy cases, if it is supposed that the clinical events occur within new patients in the same manner and with the similar probability as within the study population. Results: The patient's age, a distant metastasis at presentation, the extent of the surgical intervention, the primary tumor size and extent (pT), the external irradiation dosage and the degree of TSH suppression proved to be

  2. Investigating Rates of Hunting and Survival in Declining European Lapwing Populations.

    Directory of Open Access Journals (Sweden)

    Guillaume Souchay

    Full Text Available Understanding effects of harvest on population dynamics is of major interest, especially for declining species. European lapwing Vanellus vanellus populations increased from the 1960s until the 1980s and declined strongly thereafter. About 400,000 lapwings are harvested annually and it is thus of high conservation relevance to assess whether hunting was a main cause for the observed changes in lapwing population trends. We developed a multi-event cause-specific mortality model which we applied to a long-term ring-recovery data set (1960-2010 of > 360,000 records to estimate survival and cause-specific mortalities. We found no temporal change in survival over the last 50 years for first-year (FY and older birds (after first-year; AFY originating from different ringing areas. Mean survival was high, around 0.60 and 0.80 for FY and AFY individuals, respectively. The proportion of total mortality due to hunting was <0.10 over the study period and the estimated proportion of harvested individuals (kill rate was <0.05 in each year. Our result of constant survival indicates that demographic processes other than survival were responsible for the pronounced change in lapwing population trends in the 1980s. Our findings lend support to the hypothesis that hunting was not a significant contributor to the large-scale decline of lapwing populations. To halt the ongoing decline of European lapwing populations management should focus on life history stages other than survival (e.g. productivity. Further analyses are required to investigate the contribution of other demographic rates to the decline of lapwings and to identify the most efficient conservation actions.

  3. Survivability of systems under multiple factor impact

    International Nuclear Information System (INIS)

    Korczak, Edward; Levitin, Gregory

    2007-01-01

    The paper considers vulnerable multi-state series-parallel systems operating under influence of external impacts. Both the external impacts and internal failures affect system survivability, which is determined as the probability of meeting a given demand. The external impacts are characterized by several destructive factors affecting the system or its parts simultaneously. In order to increase the system's survivability a multilevel protection against the destructive factors can be applied to its subsystems. In such systems, the protected subsystems can be destroyed only if all of the levels of their protection are destroyed. The paper presents an algorithm for evaluating the survivability of series-parallel systems with arbitrary configuration of multilevel protection against multiple destructive factor impacts. The algorithm is based on a composition of Boolean and the Universal Generating Function techniques. Illustrative examples are presented

  4. Increasing maternal healthcare use in Rwanda: implications for child nutrition and survival.

    Science.gov (United States)

    Pierce, Hayley; Heaton, Tim B; Hoffmann, John

    2014-04-01

    Rwanda has made great progress in improving maternal utilization of health care through coordination of external aid and more efficient health policy. Using data from the 2005 and 2010 Rwandan Demographic and Health Surveys, we examine three related questions regarding the impact of expansion of health care in Rwanda. First, did the increased use of health center deliveries apply to women across varying levels of education, economic status, and area of residency? Second, did the benefits associated with being delivered at a health center diminish as utilization became more widespread? Finally, did inequality in child outcomes decline as a result of increased health care utilization? Propensity score matching was used to address the selectivity that arises when choosing to deliver at a hospital. In addition, the regression models include a linear model to predict child nutritional status and Cox regression to predict child survival. The analysis shows that the largest increases in delivery at a health center occur among less educated, less wealthy, and rural Rwandan women. In addition, delivery at a health center is associated with better nutritional status and survival and the benefit is not diminished following the dramatic increase in use of health centers. Finally, educational, economic and residential inequality in child survival and nutrition did not decline. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Multivariate survival analysis and competing risks

    CERN Document Server

    Crowder, Martin J

    2012-01-01

    Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.

  6. Systemic administration of bevacizumab prolongs survival in an in vivo model of platinum pre-treated ovarian cancer

    Science.gov (United States)

    REIN, DANIEL T.; VOLKMER, ANNE KATHRIN; VOLKMER, JENS; BEYER, INES M.; JANNI, WOLFGANG; FLEISCH, MARKUS C.; WELTER, ANNE KATHRIN; BAUERSCHLAG, DIRK; SCHÖNDORF, THOMAS; BREIDENBACH, MARTINA

    2012-01-01

    Ovarian cancer patients often suffer from malignant ascites and pleural effusion. Apart from worsening the outcome, this condition frequently impairs the quality of life in patients who are already distressed by ovarian cancer. This study investigated whether single intraperitoneal administration of the anti-VEGF antibody bevacizumab is capable of reducing the ascites-related body surface and prolonging survival. The study was performed in an orthotopic murine model of peritoneal disseminated platin-resistant ovarian cancer. Mice were treated with bevacizumab and/or paclitaxel or buffer (control). Reduction of body surface and increased survival rates were assessed as therapeutic success. Survival of mice in all treatment groups was significantly enhanced when compared to the non-treatment control group. The combination of paclitaxel plus bevacizumab significantly improved body surface as well as overall survival in comparison to a treatment with only one of the drugs. Treatment of malignant effusion with a single dose of bevacizumab as an intraperitoneal application, with or without cytostatic co-medication, may be a powerful alternative to systemic treatment. PMID:22740945

  7. Survival of viral pathogens in animal feed ingredients under transboundary shipping models

    Science.gov (United States)

    Bauermann, Fernando V.; Niederwerder, Megan C.; Singrey, Aaron; Clement, Travis; de Lima, Marcelo; Long, Craig; Patterson, Gilbert; Sheahan, Maureen A.; Stoian, Ana M. M.; Petrovan, Vlad; Jones, Cassandra K.; De Jong, Jon; Ji, Ju; Spronk, Gordon D.; Minion, Luke; Christopher-Hennings, Jane; Zimmerman, Jeff J.; Rowland, Raymond R. R.; Nelson, Eric; Sundberg, Paul; Diel, Diego G.

    2018-01-01

    The goal of this study was to evaluate survival of important viral pathogens of livestock in animal feed ingredients imported daily into the United States under simulated transboundary conditions. Eleven viruses were selected based on global significance and impact to the livestock industry, including Foot and Mouth Disease Virus (FMDV), Classical Swine Fever Virus (CSFV), African Swine Fever Virus (ASFV), Influenza A Virus of Swine (IAV-S), Pseudorabies virus (PRV), Nipah Virus (NiV), Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), Swine Vesicular Disease Virus (SVDV), Vesicular Stomatitis Virus (VSV), Porcine Circovirus Type 2 (PCV2) and Vesicular Exanthema of Swine Virus (VESV). Surrogate viruses with similar genetic and physical properties were used for 6 viruses. Surrogates belonged to the same virus families as target pathogens, and included Senecavirus A (SVA) for FMDV, Bovine Viral Diarrhea Virus (BVDV) for CSFV, Bovine Herpesvirus Type 1 (BHV-1) for PRV, Canine Distemper Virus (CDV) for NiV, Porcine Sapelovirus (PSV) for SVDV and Feline Calicivirus (FCV) for VESV. For the remaining target viruses, actual pathogens were used. Virus survival was evaluated using Trans-Pacific or Trans-Atlantic transboundary models involving representative feed ingredients, transport times and environmental conditions, with samples tested by PCR, VI and/or swine bioassay. SVA (representing FMDV), FCV (representing VESV), BHV-1 (representing PRV), PRRSV, PSV (representing SVDV), ASFV and PCV2 maintained infectivity during transport, while BVDV (representing CSFV), VSV, CDV (representing NiV) and IAV-S did not. Notably, more viruses survived in conventional soybean meal, lysine hydrochloride, choline chloride, vitamin D and pork sausage casings. These results support published data on transboundary risk of PEDV in feed, demonstrate survival of certain viruses in specific feed ingredients (“high-risk combinations”) under conditions simulating transport between

  8. A spatial scan statistic for survival data based on Weibull distribution.

    Science.gov (United States)

    Bhatt, Vijaya; Tiwari, Neeraj

    2014-05-20

    The spatial scan statistic has been developed as a geographical cluster detection analysis tool for different types of data sets such as Bernoulli, Poisson, ordinal, normal and exponential. We propose a scan statistic for survival data based on Weibull distribution. It may also be used for other survival distributions, such as exponential, gamma, and log normal. The proposed method is applied on the survival data of tuberculosis patients for the years 2004-2005 in Nainital district of Uttarakhand, India. Simulation studies reveal that the proposed method performs well for different survival distribution functions. Copyright © 2013 John Wiley & Sons, Ltd.

  9. Applying incentive sensitization models to behavioral addiction

    DEFF Research Database (Denmark)

    Rømer Thomsen, Kristine; Fjorback, Lone; Møller, Arne

    2014-01-01

    The incentive sensitization theory is a promising model for understanding the mechanisms underlying drug addiction, and has received support in animal and human studies. So far the theory has not been applied to the case of behavioral addictions like Gambling Disorder, despite sharing clinical...... symptoms and underlying neurobiology. We examine the relevance of this theory for Gambling Disorder and point to predictions for future studies. The theory promises a significant contribution to the understanding of behavioral addiction and opens new avenues for treatment....

  10. Applying a Particle-only Model to the HL Tau Disk

    OpenAIRE

    Tabeshian, Maryam; Wiegert, Paul A.

    2018-01-01

    Observations have revealed rich structures in protoplanetary disks, offering clues about their embedded planets. Due to the complexities introduced by the abundance of gas in these disks, modeling their structure in detail is computationally intensive, requiring complex hydrodynamic codes and substantial computing power. It would be advantageous if computationally simpler models could provide some preliminary information on these disks. Here we apply a particle-only model (that we developed f...

  11. Model predictive control based on reduced order models applied to belt conveyor system.

    Science.gov (United States)

    Chen, Wei; Li, Xin

    2016-11-01

    In the paper, a model predictive controller based on reduced order model is proposed to control belt conveyor system, which is an electro-mechanics complex system with long visco-elastic body. Firstly, in order to design low-degree controller, the balanced truncation method is used for belt conveyor model reduction. Secondly, MPC algorithm based on reduced order model for belt conveyor system is presented. Because of the error bound between the full-order model and reduced order model, two Kalman state estimators are applied in the control scheme to achieve better system performance. Finally, the simulation experiments are shown that balanced truncation method can significantly reduce the model order with high-accuracy and model predictive control based on reduced-model performs well in controlling the belt conveyor system. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  12. Using simulation to interpret a discrete time survival model in a complex biological system: fertility and lameness in dairy cows.

    Directory of Open Access Journals (Sweden)

    Christopher D Hudson

    Full Text Available The ever-growing volume of data routinely collected and stored in everyday life presents researchers with a number of opportunities to gain insight and make predictions. This study aimed to demonstrate the usefulness in a specific clinical context of a simulation-based technique called probabilistic sensitivity analysis (PSA in interpreting the results of a discrete time survival model based on a large dataset of routinely collected dairy herd management data. Data from 12,515 dairy cows (from 39 herds were used to construct a multilevel discrete time survival model in which the outcome was the probability of a cow becoming pregnant during a given two day period of risk, and presence or absence of a recorded lameness event during various time frames relative to the risk period amongst the potential explanatory variables. A separate simulation model was then constructed to evaluate the wider clinical implications of the model results (i.e. the potential for a herd's incidence rate of lameness to influence its overall reproductive performance using PSA. Although the discrete time survival analysis revealed some relatively large associations between lameness events and risk of pregnancy (for example, occurrence of a lameness case within 14 days of a risk period was associated with a 25% reduction in the risk of the cow becoming pregnant during that risk period, PSA revealed that, when viewed in the context of a realistic clinical situation, a herd's lameness incidence rate is highly unlikely to influence its overall reproductive performance to a meaningful extent in the vast majority of situations. Construction of a simulation model within a PSA framework proved to be a very useful additional step to aid contextualisation of the results from a discrete time survival model, especially where the research is designed to guide on-farm management decisions at population (i.e. herd rather than individual level.

  13. Sodium caseinate induces increased survival in leukaemic mouse J774 model.

    Science.gov (United States)

    Córdova-Galaviz, Yolanda; Ledesma-Martínez, Edgar; Aguíñiga-Sánchez, Itzen; Soldevila-Melgarejo, Gloria; Soto-Cruz, Isabel; Weiss-Steider, Benny; Santiago-Osorio, Edelmiro

    2014-01-01

    Acute myeloid leukaemia is a neoplastic disease of haematopoietic stem cells. Although there have been recent advances regarding its treatment, mortality remains high. Consequently, therapeutic alternatives continue to be explored. In the present report, we present evidence that sodium caseinate (CasNa), a salt of the principal protein in milk, may possess important anti-leukaemic properties. J774 leukaemia macrophage-like cells were cultured with CasNa and proliferation, viability and differentiation were evaluated. These cells were also inoculated into BALB/c mice as a model of leukemia. We demonstrated that CasNa inhibits the in vitro proliferation and reduces viability of J774 cells, and leads to increased survival in vivo in a leukaemic mouse model. These data indicate that CasNa may be useful in leukaemia therapy. Copyright © 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

  14. Long-Term Survival Prediction for Coronary Artery Bypass Grafting: Validation of the ASCERT Model Compared With The Society of Thoracic Surgeons Predicted Risk of Mortality.

    Science.gov (United States)

    Lancaster, Timothy S; Schill, Matthew R; Greenberg, Jason W; Ruaengsri, Chawannuch; Schuessler, Richard B; Lawton, Jennifer S; Maniar, Hersh S; Pasque, Michael K; Moon, Marc R; Damiano, Ralph J; Melby, Spencer J

    2018-05-01

    The recently developed American College of Cardiology Foundation-Society of Thoracic Surgeons (STS) Collaboration on the Comparative Effectiveness of Revascularization Strategy (ASCERT) Long-Term Survival Probability Calculator is a valuable addition to existing short-term risk-prediction tools for cardiac surgical procedures but has yet to be externally validated. Institutional data of 654 patients aged 65 years or older undergoing isolated coronary artery bypass grafting between 2005 and 2010 were reviewed. Predicted survival probabilities were calculated using the ASCERT model. Survival data were collected using the Social Security Death Index and institutional medical records. Model calibration and discrimination were assessed for the overall sample and for risk-stratified subgroups based on (1) ASCERT 7-year survival probability and (2) the predicted risk of mortality (PROM) from the STS Short-Term Risk Calculator. Logistic regression analysis was performed to evaluate additional perioperative variables contributing to death. Overall survival was 92.1% (569 of 597) at 1 year and 50.5% (164 of 325) at 7 years. Calibration assessment found no significant differences between predicted and actual survival curves for the overall sample or for the risk-stratified subgroups, whether stratified by predicted 7-year survival or by PROM. Discriminative performance was comparable between the ASCERT and PROM models for 7-year survival prediction (p validated for prediction of long-term survival after coronary artery bypass grafting in all risk groups. The widely used STS PROM performed comparably as a predictor of long-term survival. Both tools provide important information for preoperative decision making and patient counseling about potential outcomes after coronary artery bypass grafting. Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

  15. Applying Model Based Systems Engineering to NASA's Space Communications Networks

    Science.gov (United States)

    Bhasin, Kul; Barnes, Patrick; Reinert, Jessica; Golden, Bert

    2013-01-01

    System engineering practices for complex systems and networks now require that requirement, architecture, and concept of operations product development teams, simultaneously harmonize their activities to provide timely, useful and cost-effective products. When dealing with complex systems of systems, traditional systems engineering methodology quickly falls short of achieving project objectives. This approach is encumbered by the use of a number of disparate hardware and software tools, spreadsheets and documents to grasp the concept of the network design and operation. In case of NASA's space communication networks, since the networks are geographically distributed, and so are its subject matter experts, the team is challenged to create a common language and tools to produce its products. Using Model Based Systems Engineering methods and tools allows for a unified representation of the system in a model that enables a highly related level of detail. To date, Program System Engineering (PSE) team has been able to model each network from their top-level operational activities and system functions down to the atomic level through relational modeling decomposition. These models allow for a better understanding of the relationships between NASA's stakeholders, internal organizations, and impacts to all related entities due to integration and sustainment of existing systems. Understanding the existing systems is essential to accurate and detailed study of integration options being considered. In this paper, we identify the challenges the PSE team faced in its quest to unify complex legacy space communications networks and their operational processes. We describe the initial approaches undertaken and the evolution toward model based system engineering applied to produce Space Communication and Navigation (SCaN) PSE products. We will demonstrate the practice of Model Based System Engineering applied to integrating space communication networks and the summary of its

  16. Coyote removal, understory cover, and survival of white-tailed deer neonates: Coyote Control and Fawn Survival

    Energy Technology Data Exchange (ETDEWEB)

    Kilgo, John C. [USDA Forest Service; Southern Research Station, New Ellenton, SC (United States); Vukovich, Mark [USDA Forest Service; Southern Research Station, New Ellenton, SC (United States); Ray, H. Scott [USDA Forest Service, Savannah River; New Ellenton, SC (United States); Shaw, Christopher E. [USDA Forest Service; Southern Research Station, New Ellenton, SC (United States); Ruth, Charles [South Carolina Dept. of Natural Resources, Columbia, SC (United States)

    2014-09-01

    Predation by coyotes (Canis latrans) on white-tailed deer (Odocoileus virginianus) neonates has led to reduced recruitment in many deer populations in southeastern North America. This low recruitment combined with liberal antlerless deer harvest has resulted in declines in some deer populations, and consequently, increased interest in coyote population control. We investigated whether neonate survival increased after coyote removal, whether coyote predation on neonates was additive to other mortality sources, and whether understory vegetation density affected neonate survival. We monitored neonate survival for 4 years prior to (2006–2009) and 3 years during (2010–2012) intensive coyote removal on 3 32-km2 units on the United States Department of Energy’s Savannah River Site, South Carolina. We removed 474 coyotes (1.63 coyotes/km2 per unit per year), reducing coyote abundance by 78% from pre-removal levels. The best model (wi = 0.927) describing survival probability among 216 radio-collared neonates included a within-year quadratic time trend variable, date of birth, removal treatment, and a varying removal year effect. Under this model, survival differed between pre-treatment and removal periods and it differed among years during the removal period, being >100% greater than pre-treatment survival (0.228) during the first removal year (0.513), similar to pre-treatment survival during the second removal year (0.202), and intermediate during the third removal year (0.431). Despite an initial increase, the overall effect of coyote removal on neonate survival was modest. Mortality rate attributable to coyote predation was lowest during the first removal year (0.357) when survival was greatest, but the mortality rate from all other causes did not differ between the pretreatment period and any year during removals, indicating that coyote predation acted as an additive source of mortality. Survival probability was not related to

  17. Combined treatment with atorvastatin and imipenem improves survival and vascular functions in mouse model of sepsis.

    Science.gov (United States)

    Choudhury, Soumen; Kannan, Kandasamy; Pule Addison, M; Darzi, Sazad A; Singh, Vishakha; Singh, Thakur Uttam; Thangamalai, Ramasamy; Dash, Jeevan Ranjan; Parida, Subhashree; Debroy, Biplab; Paul, Avishek; Mishra, Santosh Kumar

    2015-08-01

    We have recently reported that pre-treatment, but not the post-treatment with atorvastatin showed survival benefit and improved hemodynamic functions in cecal ligation and puncture (CLP) model of sepsis in mice. Here we examined whether combined treatment with atorvastatin and imipenem after onset of sepsis can prolong survival and improve vascular functions. At 6 and 18h after sepsis induction, treatment with atorvastatin plus imipenem, atorvastatin or imipenem alone or placebo was initiated. Ex vivo experiments were done on mouse aorta to examine the vascular reactivity to nor-adrenaline and acetylcholine and mRNA expressions of α1D AR, GRK2 and eNOS. Atorvastatin plus imipenem extended the survival time to 56.00±4.62h from 20.00±1.66h observed in CLP mice. The survival time with atorvastatin or imipenem alone was 20.50±1.89h and 27.00±4.09h, respectively. The combined treatment reversed the hyporeactivity to nor-adrenaline through preservation of α1D AR mRNA/protein expression and reversal of α1D AR desensitization mediated by GRK2/Gβγ pathway. The treatment also restored endothelium-dependent relaxation to ACh through restoration of aortic eNOS mRNA expression and NO availability. In conclusion, combined treatment with atorvastatin and imipenem exhibited survival benefit and improved vascular functions in septic mice. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Do American dippers obtain a survival benefit from altitudinal migration?

    Directory of Open Access Journals (Sweden)

    David J Green

    Full Text Available Studies of partial migrants provide an opportunity to assess the cost and benefits of migration. Previous work has demonstrated that sedentary American dippers (residents have higher annual productivity than altitudinal migrants that move to higher elevations to breed. Here we use a ten-year (30 period mark-recapture dataset to evaluate whether migrants offset their lower productivity with higher survival during the migration-breeding period when they occupy different habitat, or early and late-winter periods when they coexist with residents. Mark-recapture models provide no evidence that apparent monthly survival of migrants is higher than that of residents at any time of the year. The best-supported model suggests that monthly survival is higher in the migration-breeding period than winter periods. Another well-supported model suggested that residency conferred a survival benefit, and annual apparent survival (calculated from model weighted monthly apparent survival estimates using the Delta method of residents (0.511 ± 0.038SE was slightly higher than that of migrants (0.487 ± 0.032. Winter survival of American dippers was influenced by environmental conditions; monthly apparent survival increased as maximum daily flow rates increased and declined as winter temperatures became colder. However, we found no evidence that environmental conditions altered differences in winter survival of residents and migrants. Since migratory American dippers have lower productivity and slightly lower survival than residents our data suggests that partial migration is likely an outcome of competition for limited nest sites at low elevations, with less competitive individuals being forced to migrate to higher elevations in order to breed.

  19. Performance of an easy-to-use prediction model for renal patient survival: an external validation study using data from the ERA-EDTA Registry.

    Science.gov (United States)

    Hemke, Aline C; Heemskerk, Martin B A; van Diepen, Merel; Kramer, Anneke; de Meester, Johan; Heaf, James G; Abad Diez, José Maria; Torres Guinea, Marta; Finne, Patrik; Brunet, Philippe; Vikse, Bjørn E; Caskey, Fergus J; Traynor, Jamie P; Massy, Ziad A; Couchoud, Cécile; Groothoff, Jaap W; Nordio, Maurizio; Jager, Kitty J; Dekker, Friedo W; Hoitsma, Andries J

    2018-01-16

    An easy-to-use prediction model for long-term renal patient survival based on only four predictors [age, primary renal disease, sex and therapy at 90 days after the start of renal replacement therapy (RRT)] has been developed in The Netherlands. To assess the usability of this model for use in Europe, we externally validated the model in 10 European countries. Data from the European Renal Association-European Dialysis and Transplant Association (ERA-EDTA) Registry were used. Ten countries that reported individual patient data to the registry on patients starting RRT in the period 1995-2005 were included. Patients prediction model was evaluated for the 10- (primary endpoint), 5- and 3-year survival predictions by assessing the calibration and discrimination outcomes. We used a data set of 136 304 patients from 10 countries. The calibration in the large and calibration plots for 10 deciles of predicted survival probabilities showed average differences of 1.5, 3.2 and 3.4% in observed versus predicted 10-, 5- and 3-year survival, with some small variation on the country level. The concordance index, indicating the discriminatory power of the model, was 0.71 in the complete ERA-EDTA Registry cohort and varied according to country level between 0.70 and 0.75. A prediction model for long-term renal patient survival developed in a single country, based on only four easily available variables, has a comparably adequate performance in a wide range of other European countries. © The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  20. Free Base Lysine Increases Survival and Reduces Metastasis in Prostate Cancer Model.

    Science.gov (United States)

    Ibrahim-Hashim, Arig; Wojtkowiak, Jonathan W; de Lourdes Coelho Ribeiro, Maria; Estrella, Veronica; Bailey, Kate M; Cornnell, Heather H; Gatenby, Robert A; Gillies, Robert J

    2011-11-19

    Malignant tumor cells typically metabolize glucose anaerobically to lactic acid even under normal oxygen tension, a phenomenon called aerobic glycolysis or the Warburg effect. This results in increased acid production and the acidification of the extracellular microenvironment in solid tumors. H + ions tend to flow along concentration gradients into peritumoral normal tissue causing extracellular matrix degradation and increased tumor cell motility thus promoting invasion and metastasis. We have shown that reducing this acidity with sodium bicarbonate buffer decreases the metastatic fitness of circulating tumor cells in prostate cancer and other cancer models. Mathematical models of the tumor-host dynamics predicted that buffers with a pka around 7 will be more effective in reducing intra- and peri-tumoral acidosis and, thus, and possibly more effective in inhibiting tumor metastasis than sodium bicarbonate which has a pKa around 6. Here we test this prediction the efficacy of free base lysine; a non-bicarbonate/non-volatile buffer with a higher pKa (~10), on prostate tumor metastases model. Oxygen consumption and acid production rate of PC3M prostate cancer cells and normal prostate cells were determined using the Seahorse Extracellular Flux (XF-96) analyzer. In vivo effect of 200 mM lysine started four days prior to inoculation on inhibition of metastasis was examined in PC3M-LUC-C6 prostate cancer model using SCID mice. Metastases were followed by bioluminescence imaging. PC3M prostate cancer cells are highly acidic in comparison to a normal prostate cell line indicating that reduction of intra- and perit-tumoral acidosis should inhibit metastases formation. In vivo administration of 200 mM free base lysine increased survival and reduced metastasis. PC3M prostate cancer cells are highly glycolytic and produce large amounts of acid when compared to normal prostate cells. Administration of non-volatile buffer decreased growth of metastases and improved survival

  1. Integrated survival analysis using an event-time approach in a Bayesian framework.

    Science.gov (United States)

    Walsh, Daniel P; Dreitz, Victoria J; Heisey, Dennis M

    2015-02-01

    Event-time or continuous-time statistical approaches have been applied throughout the biostatistical literature and have led to numerous scientific advances. However, these techniques have traditionally relied on knowing failure times. This has limited application of these analyses, particularly, within the ecological field where fates of marked animals may be unknown. To address these limitations, we developed an integrated approach within a Bayesian framework to estimate hazard rates in the face of unknown fates. We combine failure/survival times from individuals whose fates are known and times of which are interval-censored with information from those whose fates are unknown, and model the process of detecting animals with unknown fates. This provides the foundation for our integrated model and permits necessary parameter estimation. We provide the Bayesian model, its derivation, and use simulation techniques to investigate the properties and performance of our approach under several scenarios. Lastly, we apply our estimation technique using a piece-wise constant hazard function to investigate the effects of year, age, chick size and sex, sex of the tending adult, and nesting habitat on mortality hazard rates of the endangered mountain plover (Charadrius montanus) chicks. Traditional models were inappropriate for this analysis because fates of some individual chicks were unknown due to failed radio transmitters. Simulations revealed biases of posterior mean estimates were minimal (≤ 4.95%), and posterior distributions behaved as expected with RMSE of the estimates decreasing as sample sizes, detection probability, and survival increased. We determined mortality hazard rates for plover chicks were highest at birth weights and/or whose nest was within agricultural habitats. Based on its performance, our approach greatly expands the range of problems for which event-time analyses can be used by eliminating the need for having completely known fate data.

  2. Integrated survival analysis using an event-time approach in a Bayesian framework

    Science.gov (United States)

    Walsh, Daniel P.; Dreitz, VJ; Heisey, Dennis M.

    2015-01-01

    Event-time or continuous-time statistical approaches have been applied throughout the biostatistical literature and have led to numerous scientific advances. However, these techniques have traditionally relied on knowing failure times. This has limited application of these analyses, particularly, within the ecological field where fates of marked animals may be unknown. To address these limitations, we developed an integrated approach within a Bayesian framework to estimate hazard rates in the face of unknown fates. We combine failure/survival times from individuals whose fates are known and times of which are interval-censored with information from those whose fates are unknown, and model the process of detecting animals with unknown fates. This provides the foundation for our integrated model and permits necessary parameter estimation. We provide the Bayesian model, its derivation, and use simulation techniques to investigate the properties and performance of our approach under several scenarios. Lastly, we apply our estimation technique using a piece-wise constant hazard function to investigate the effects of year, age, chick size and sex, sex of the tending adult, and nesting habitat on mortality hazard rates of the endangered mountain plover (Charadrius montanus) chicks. Traditional models were inappropriate for this analysis because fates of some individual chicks were unknown due to failed radio transmitters. Simulations revealed biases of posterior mean estimates were minimal (≤ 4.95%), and posterior distributions behaved as expected with RMSE of the estimates decreasing as sample sizes, detection probability, and survival increased. We determined mortality hazard rates for plover chicks were highest at birth weights and/or whose nest was within agricultural habitats. Based on its performance, our approach greatly expands the range of problems for which event-time analyses can be used by eliminating the need for having completely known fate data.

  3. Eliciting expert opinion for economic models: an applied example.

    Science.gov (United States)

    Leal, José; Wordsworth, Sarah; Legood, Rosa; Blair, Edward

    2007-01-01

    Expert opinion is considered as a legitimate source of information for decision-analytic modeling where required data are unavailable. Our objective was to develop a practical computer-based tool for eliciting expert opinion about the shape of the uncertainty distribution around individual model parameters. We first developed a prepilot survey with departmental colleagues to test a number of alternative approaches to eliciting opinions on the shape of the uncertainty distribution around individual parameters. This information was used to develop a survey instrument for an applied clinical example. This involved eliciting opinions from experts to inform a number of parameters involving Bernoulli processes in an economic model evaluating DNA testing for families with a genetic disease, hypertrophic cardiomyopathy. The experts were cardiologists, clinical geneticists, and laboratory scientists working with cardiomyopathy patient populations and DNA testing. Our initial prepilot work suggested that the more complex elicitation techniques advocated in the literature were difficult to use in practice. In contrast, our approach achieved a reasonable response rate (50%), provided logical answers, and was generally rated as easy to use by respondents. The computer software user interface permitted graphical feedback throughout the elicitation process. The distributions obtained were incorporated into the model, enabling the use of probabilistic sensitivity analysis. There is clearly a gap in the literature between theoretical elicitation techniques and tools that can be used in applied decision-analytic models. The results of this methodological study are potentially valuable for other decision analysts deriving expert opinion.

  4. Gemtuzumab Ozogamicin (GO Inclusion to Induction Chemotherapy Eliminates Leukemic Initiating Cells and Significantly Improves Survival in Mouse Models of Acute Myeloid Leukemia

    Directory of Open Access Journals (Sweden)

    Cathy C Zhang

    2018-01-01

    Full Text Available Gemtuzumab ozogamicin (GO is an anti-CD33 antibody-drug conjugate for the treatment of acute myeloid leukemia (AML. Although GO shows a narrow therapeutic window in early clinical studies, recent reports detailing a modified dosing regimen of GO can be safely combined with induction chemotherapy, and the combination provides significant survival benefits in AML patients. Here we tested whether the survival benefits seen with the combination arise from the enhanced reduction of chemoresidual disease and leukemic initiating cells (LICs. Herein, we use cell line and patient-derived xenograft (PDX AML models to evaluate the combination of GO with daunorubicin and cytarabine (DA induction chemotherapy on AML blast growth and animal survival. DA chemotherapy and GO as separate treatments reduced AML burden but left significant chemoresidual disease in multiple AML models. The combination of GO and DA chemotherapy eliminated nearly all AML burden and extended overall survival. In two small subsets of AML models, chemoresidual disease following DA chemotherapy displayed hallmark markers of leukemic LICs (CLL1 and CD34. In vivo, the two chemoresistant subpopulations (CLL1+/CD117− and CD34+/CD38+ showed higher ability to self-renewal than their counterpart subpopulations, respectively. CD33 was coexpressed in these functional LIC subpopulations. We demonstrate that the GO and DA induction chemotherapy combination more effectively eliminates LICs in AML PDX models than either single agent alone. These data suggest that the survival benefit seen by the combination of GO and induction chemotherapy, nonclinically and clinically, may be attributed to the enhanced reduction of LICs.

  5. Accounting for Uncertainty in Decision Analytic Models Using Rank Preserving Structural Failure Time Modeling: Application to Parametric Survival Models.

    Science.gov (United States)

    Bennett, Iain; Paracha, Noman; Abrams, Keith; Ray, Joshua

    2018-01-01

    Rank Preserving Structural Failure Time models are one of the most commonly used statistical methods to adjust for treatment switching in oncology clinical trials. The method is often applied in a decision analytic model without appropriately accounting for additional uncertainty when determining the allocation of health care resources. The aim of the study is to describe novel approaches to adequately account for uncertainty when using a Rank Preserving Structural Failure Time model in a decision analytic model. Using two examples, we tested and compared the performance of the novel Test-based method with the resampling bootstrap method and with the conventional approach of no adjustment. In the first example, we simulated life expectancy using a simple decision analytic model based on a hypothetical oncology trial with treatment switching. In the second example, we applied the adjustment method on published data when no individual patient data were available. Mean estimates of overall and incremental life expectancy were similar across methods. However, the bootstrapped and test-based estimates consistently produced greater estimates of uncertainty compared with the estimate without any adjustment applied. Similar results were observed when using the test based approach on a published data showing that failing to adjust for uncertainty led to smaller confidence intervals. Both the bootstrapping and test-based approaches provide a solution to appropriately incorporate uncertainty, with the benefit that the latter can implemented by researchers in the absence of individual patient data. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

  6. Experiences in applying Bayesian integrative models in interdisciplinary modeling: the computational and human challenges

    DEFF Research Database (Denmark)

    Kuikka, Sakari; Haapasaari, Päivi Elisabet; Helle, Inari

    2011-01-01

    We review the experience obtained in using integrative Bayesian models in interdisciplinary analysis focusing on sustainable use of marine resources and environmental management tasks. We have applied Bayesian models to both fisheries and environmental risk analysis problems. Bayesian belief...... be time consuming and research projects can be difficult to manage due to unpredictable technical problems related to parameter estimation. Biology, sociology and environmental economics have their own scientific traditions. Bayesian models are becoming traditional tools in fisheries biology, where...

  7. Applied data analysis and modeling for energy engineers and scientists

    CERN Document Server

    Reddy, T Agami

    2011-01-01

    ""Applied Data Analysis and Modeling for Energy Engineers and Scientists"" discusses mathematical models, data analysis, and decision analysis in modeling. The approach taken in this volume focuses on the modeling and analysis of thermal systems in an engineering environment, while also covering a number of other critical areas. Other material covered includes the tools that researchers and engineering professionals will need in order to explore different analysis methods, use critical assessment skills and reach sound engineering conclusions. The book also covers process and system design and

  8. Molecular Mechanisms of Survival Strategies in Extreme Conditions

    Directory of Open Access Journals (Sweden)

    Federica Migliardo

    2012-12-01

    Full Text Available Today, one of the major challenges in biophysics is to disclose the molecular mechanisms underlying biological processes. In such a frame, the understanding of the survival strategies in extreme conditions received a lot of attention both from the scientific and applicative points of view. Since nature provides precious suggestions to be applied for improving the quality of life, extremophiles are considered as useful model-systems. The main goal of this review is to present an overview of some systems, with a particular emphasis on trehalose playing a key role in several extremophile organisms. The attention is focused on the relation among the structural and dynamic properties of biomolecules and bioprotective mechanisms, as investigated by complementary spectroscopic techniques at low- and high-temperature values.

  9. Addressing dependability by applying an approach for model-based risk assessment

    International Nuclear Information System (INIS)

    Gran, Bjorn Axel; Fredriksen, Rune; Thunem, Atoosa P.-J.

    2007-01-01

    This paper describes how an approach for model-based risk assessment (MBRA) can be applied for addressing different dependability factors in a critical application. Dependability factors, such as availability, reliability, safety and security, are important when assessing the dependability degree of total systems involving digital instrumentation and control (I and C) sub-systems. In order to identify risk sources their roles with regard to intentional system aspects such as system functions, component behaviours and intercommunications must be clarified. Traditional risk assessment is based on fault or risk models of the system. In contrast to this, MBRA utilizes success-oriented models describing all intended system aspects, including functional, operational and organizational aspects of the target. The EU-funded CORAS project developed a tool-supported methodology for the application of MBRA in security-critical systems. The methodology has been tried out within the telemedicine and e-commerce areas, and provided through a series of seven trials a sound basis for risk assessments. In this paper the results from the CORAS project are presented, and it is discussed how the approach for applying MBRA meets the needs of a risk-informed Man-Technology-Organization (MTO) model, and how methodology can be applied as a part of a trust case development

  10. Addressing dependability by applying an approach for model-based risk assessment

    Energy Technology Data Exchange (ETDEWEB)

    Gran, Bjorn Axel [Institutt for energiteknikk, OECD Halden Reactor Project, NO-1751 Halden (Norway)]. E-mail: bjorn.axel.gran@hrp.no; Fredriksen, Rune [Institutt for energiteknikk, OECD Halden Reactor Project, NO-1751 Halden (Norway)]. E-mail: rune.fredriksen@hrp.no; Thunem, Atoosa P.-J. [Institutt for energiteknikk, OECD Halden Reactor Project, NO-1751 Halden (Norway)]. E-mail: atoosa.p-j.thunem@hrp.no

    2007-11-15

    This paper describes how an approach for model-based risk assessment (MBRA) can be applied for addressing different dependability factors in a critical application. Dependability factors, such as availability, reliability, safety and security, are important when assessing the dependability degree of total systems involving digital instrumentation and control (I and C) sub-systems. In order to identify risk sources their roles with regard to intentional system aspects such as system functions, component behaviours and intercommunications must be clarified. Traditional risk assessment is based on fault or risk models of the system. In contrast to this, MBRA utilizes success-oriented models describing all intended system aspects, including functional, operational and organizational aspects of the target. The EU-funded CORAS project developed a tool-supported methodology for the application of MBRA in security-critical systems. The methodology has been tried out within the telemedicine and e-commerce areas, and provided through a series of seven trials a sound basis for risk assessments. In this paper the results from the CORAS project are presented, and it is discussed how the approach for applying MBRA meets the needs of a risk-informed Man-Technology-Organization (MTO) model, and how methodology can be applied as a part of a trust case development.

  11. Effects of temperature on development, survival and reproduction of insects: Experimental design, data analysis and modeling

    Science.gov (United States)

    Jacques Regniere; James Powell; Barbara Bentz; Vincent Nealis

    2012-01-01

    The developmental response of insects to temperature is important in understanding the ecology of insect life histories. Temperature-dependent phenology models permit examination of the impacts of temperature on the geographical distributions, population dynamics and management of insects. The measurement of insect developmental, survival and reproductive responses to...

  12. Terahertz spectroscopy applied to food model systems

    DEFF Research Database (Denmark)

    Møller, Uffe

    Water plays a crucial role in the quality of food. Apart from the natural water content of a food product, the state of that water is very important. Water can be found integrated into the biological material or it can be added during production of the product. Currently it is difficult...... to differentiate between these types of water in subsequent quality controls. This thesis describes terahertz time-domain spectroscopy applied on aqueous food model systems, with particular focus on ethanol-water mixtures and confined water pools in inverse micelles....

  13. In-season retail sales forecasting using survival models

    African Journals Online (AJOL)

    Retail sales forecasting, survival analysis, time series analysis, Holt's smoothing .... where fx(t) is the probability density function of the future lifetime, Tx, of a .... Adjustments were made to the shape of the smoothed mortality rates in light of new.

  14. A chip-level modeling approach for rail span collapse and survivability analyses

    International Nuclear Information System (INIS)

    Marvis, D.G.; Alexander, D.R.; Dinger, G.L.

    1989-01-01

    A general semiautomated analysis technique has been developed for analyzing rail span collapse and survivability of VLSI microcircuits in high ionizing dose rate radiation environments. Hierarchical macrocell modeling permits analyses at the chip level and interactive graphical postprocessing provides a rapid visualization of voltage, current and power distributions over an entire VLSIC. The technique is demonstrated for a 16k C MOS/SOI SRAM and a CMOS/SOS 8-bit multiplier. The authors also present an efficient method to treat memory arrays as well as a three-dimensional integration technique to compute sapphire photoconduction from the design layout

  15. Habitat suitability and nest survival of white-headed woodpeckers in unburned forests of Oregon

    Science.gov (United States)

    Hollenbeck, Jeff P.; Saab, Victoria A.; Frenzel, Richard W.

    2011-01-01

    We evaluated habitat suitability and nest survival of breeding white-headed woodpeckers (Picoides albolarvatus) in unburned forests of central Oregon, USA. Daily nest-survival rate was positively related to maximum daily temperature during the nest interval and to density of large-diameter trees surrounding the nest tree. We developed a niche-based habitat suitability model (partitioned Mahalanobis distance) for nesting white-headed woodpeckers using remotely sensed data. Along with low elevation, high density of large trees, and low slope, our habitat suitability model suggested that interspersion–juxtaposition of low- and high-canopy cover ponderosa pine (Pinus ponderosa) patches was important for nest-site suitability. Cross-validation suggested the model performed adequately for management planning at a scale >1 ha. Evaluation of mapped habitat suitability index (HSI) suggested that the maximum predictive gain (HSI = 0.36), where the number of nest locations are maximized in the smallest proportion of the modeled landscape, provided an objective initial threshold for identification of suitable habitat. However, managers can choose the threshold HSI most appropriate for their purposes (e.g., locating regions of low–moderate suitability that have potential for habitat restoration). Consequently, our habitat suitability model may be useful for managing dry coniferous forests for white-headed woodpeckers in central Oregon; however, model validation is necessary before our model could be applied to other locations.

  16. Cisplatin Resistant Spheroids Model Clinically Relevant Survival Mechanisms in Ovarian Tumors.

    Directory of Open Access Journals (Sweden)

    Winyoo Chowanadisai

    Full Text Available The majority of ovarian tumors eventually recur in a drug resistant form. Using cisplatin sensitive and resistant cell lines assembled into 3D spheroids we profiled gene expression and identified candidate mechanisms and biological pathways associated with cisplatin resistance. OVCAR-8 human ovarian carcinoma cells were exposed to sub-lethal concentrations of cisplatin to create a matched cisplatin-resistant cell line, OVCAR-8R. Genome-wide gene expression profiling of sensitive and resistant ovarian cancer spheroids identified 3,331 significantly differentially expressed probesets coding for 3,139 distinct protein-coding genes (Fc >2, FDR < 0.05 (S2 Table. Despite significant expression changes in some transporters including MDR1, cisplatin resistance was not associated with differences in intracellular cisplatin concentration. Cisplatin resistant cells were significantly enriched for a mesenchymal gene expression signature. OVCAR-8R resistance derived gene sets were significantly more biased to patients with shorter survival. From the most differentially expressed genes, we derived a 17-gene expression signature that identifies ovarian cancer patients with shorter overall survival in three independent datasets. We propose that the use of cisplatin resistant cell lines in 3D spheroid models is a viable approach to gain insight into resistance mechanisms relevant to ovarian tumors in patients. Our data support the emerging concept that ovarian cancers can acquire drug resistance through an epithelial-to-mesenchymal transition.

  17. Changes in speed distribution: Applying aggregated safety effect models to individual vehicle speeds.

    Science.gov (United States)

    Vadeby, Anna; Forsman, Åsa

    2017-06-01

    This study investigated the effect of applying two aggregated models (the Power model and the Exponential model) to individual vehicle speeds instead of mean speeds. This is of particular interest when the measure introduced affects different parts of the speed distribution differently. The aim was to examine how the estimated overall risk was affected when assuming the models are valid on an individual vehicle level. Speed data from two applications of speed measurements were used in the study: an evaluation of movable speed cameras and a national evaluation of new speed limits in Sweden. The results showed that when applied on individual vehicle speed level compared with aggregated level, there was essentially no difference between these for the Power model in the case of injury accidents. However, for fatalities the difference was greater, especially for roads with new cameras where those driving fastest reduced their speed the most. For the case with new speed limits, the individual approach estimated a somewhat smaller effect, reflecting that changes in the 15th percentile (P15) were somewhat larger than changes in P85 in this case. For the Exponential model there was also a clear, although small, difference between applying the model to mean speed changes and individual vehicle speed changes when speed cameras were used. This applied both for injury accidents and fatalities. There were also larger effects for the Exponential model than for the Power model, especially for injury accidents. In conclusion, applying the Power or Exponential model to individual vehicle speeds is an alternative that provides reasonable results in relation to the original Power and Exponential models, but more research is needed to clarify the shape of the individual risk curve. It is not surprising that the impact on severe traffic crashes was larger in situations where those driving fastest reduced their speed the most. Further investigations on use of the Power and/or the

  18. Experimental designs for autoregressive models applied to industrial maintenance

    International Nuclear Information System (INIS)

    Amo-Salas, M.; López-Fidalgo, J.; Pedregal, D.J.

    2015-01-01

    Some time series applications require data which are either expensive or technically difficult to obtain. In such cases scheduling the points in time at which the information should be collected is of paramount importance in order to optimize the resources available. In this paper time series models are studied from a new perspective, consisting in the use of Optimal Experimental Design setup to obtain the best times to take measurements, with the principal aim of saving costs or discarding useless information. The model and the covariance function are expressed in an explicit form to apply the usual techniques of Optimal Experimental Design. Optimal designs for various approaches are computed and their efficiencies are compared. The methods working in an application of industrial maintenance of a critical piece of equipment at a petrochemical plant are shown. This simple model allows explicit calculations in order to show openly the procedure to find the correlation structure, needed for computing the optimal experimental design. In this sense the techniques used in this paper to compute optimal designs may be transferred to other situations following the ideas of the paper, but taking into account the increasing difficulty of the procedure for more complex models. - Highlights: • Optimal experimental design theory is applied to AR models to reduce costs. • The first observation has an important impact on any optimal design. • Either the lack of precision or small starting observations claim for large times. • Reasonable optimal times were obtained relaxing slightly the efficiency. • Optimal designs were computed in a predictive maintenance context

  19. Transforming Collaborative Process Models into Interface Process Models by Applying an MDA Approach

    Science.gov (United States)

    Lazarte, Ivanna M.; Chiotti, Omar; Villarreal, Pablo D.

    Collaborative business models among enterprises require defining collaborative business processes. Enterprises implement B2B collaborations to execute these processes. In B2B collaborations the integration and interoperability of processes and systems of the enterprises are required to support the execution of collaborative processes. From a collaborative process model, which describes the global view of the enterprise interactions, each enterprise must define the interface process that represents the role it performs in the collaborative process in order to implement the process in a Business Process Management System. Hence, in this work we propose a method for the automatic generation of the interface process model of each enterprise from a collaborative process model. This method is based on a Model-Driven Architecture to transform collaborative process models into interface process models. By applying this method, interface processes are guaranteed to be interoperable and defined according to a collaborative process.

  20. Applying different quality and safety models in healthcare improvement work: Boundary objects and system thinking

    International Nuclear Information System (INIS)

    Wiig, Siri; Robert, Glenn; Anderson, Janet E.; Pietikainen, Elina; Reiman, Teemu; Macchi, Luigi; Aase, Karina

    2014-01-01

    A number of theoretical models can be applied to help guide quality improvement and patient safety interventions in hospitals. However there are often significant differences between such models and, therefore, their potential contribution when applied in diverse contexts. The aim of this paper is to explore how two such models have been applied by hospitals to improve quality and safety. We describe and compare the models: (1) The Organizing for Quality (OQ) model, and (2) the Design for Integrated Safety Culture (DISC) model. We analyze the theoretical foundations of the models, and show, by using a retrospective comparative case study approach from two European hospitals, how these models have been applied to improve quality and safety. The analysis shows that differences appear in the theoretical foundations, practical approaches and applications of the models. Nevertheless, the case studies indicate that the choice between the OQ and DISC models is of less importance for guiding the practice of quality and safety improvement work, as they are both systemic and share some important characteristics. The main contribution of the models lay in their role as boundary objects directing attention towards organizational and systems thinking, culture, and collaboration

  1. Reverse survival method of fertility estimation: An evaluation

    Directory of Open Access Journals (Sweden)

    Thomas Spoorenberg

    2014-07-01

    Full Text Available Background: For the most part, demographers have relied on the ever-growing body of sample surveys collecting full birth history to derive total fertility estimates in less statistically developed countries. Yet alternative methods of fertility estimation can return very consistent total fertility estimates by using only basic demographic information. Objective: This paper evaluates the consistency and sensitivity of the reverse survival method -- a fertility estimation method based on population data by age and sex collected in one census or a single-round survey. Methods: A simulated population was first projected over 15 years using a set of fertility and mortality age and sex patterns. The projected population was then reverse survived using the Excel template FE_reverse_4.xlsx, provided with Timæus and Moultrie (2012. Reverse survival fertility estimates were then compared for consistency to the total fertility rates used to project the population. The sensitivity was assessed by introducing a series of distortions in the projection of the population and comparing the difference implied in the resulting fertility estimates. Results: The reverse survival method produces total fertility estimates that are very consistent and hardly affected by erroneous assumptions on the age distribution of fertility or by the use of incorrect mortality levels, trends, and age patterns. The quality of the age and sex population data that is 'reverse survived' determines the consistency of the estimates. The contribution of the method for the estimation of past and present trends in total fertility is illustrated through its application to the population data of five countries characterized by distinct fertility levels and data quality issues. Conclusions: Notwithstanding its simplicity, the reverse survival method of fertility estimation has seldom been applied. The method can be applied to a large body of existing and easily available population data

  2. Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer

    NARCIS (Netherlands)

    Petersen, Japke F.; Stuiver, Martijn M.; Timmermans, Adriana J.; Chen, Amy; Zhang, Hongzhen; O'Neill, James P.; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T.; Koch, Wayne; van den Brekel, Michiel W. M.

    2017-01-01

    TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442

  3. A new semi-supervised learning model combined with Cox and SP-AFT models in cancer survival analysis.

    Science.gov (United States)

    Chai, Hua; Li, Zi-Na; Meng, De-Yu; Xia, Liang-Yong; Liang, Yong

    2017-10-12

    Gene selection is an attractive and important task in cancer survival analysis. Most existing supervised learning methods can only use the labeled biological data, while the censored data (weakly labeled data) far more than the labeled data are ignored in model building. Trying to utilize such information in the censored data, a semi-supervised learning framework (Cox-AFT model) combined with Cox proportional hazard (Cox) and accelerated failure time (AFT) model was used in cancer research, which has better performance than the single Cox or AFT model. This method, however, is easily affected by noise. To alleviate this problem, in this paper we combine the Cox-AFT model with self-paced learning (SPL) method to more effectively employ the information in the censored data in a self-learning way. SPL is a kind of reliable and stable learning mechanism, which is recently proposed for simulating the human learning process to help the AFT model automatically identify and include samples of high confidence into training, minimizing interference from high noise. Utilizing the SPL method produces two direct advantages: (1) The utilization of censored data is further promoted; (2) the noise delivered to the model is greatly decreased. The experimental results demonstrate the effectiveness of the proposed model compared to the traditional Cox-AFT model.

  4. Equity and child-survival strategies.

    Science.gov (United States)

    Mulholland, Ek; Smith, L; Carneiro, I; Becher, H; Lehmann, D

    2008-05-01

    Recent advances in child survival have often been at the expense of increasing inequity. Successive interventions are applied to the same population sectors, while the same children in other sectors consistently miss out, leading to a trend towards increasing inequity in child survival. This is particularly important in the case of pneumonia, the leading cause of child death, which is closely linked to poverty and malnutrition, and for which effective community-based case management is more difficult to achieve than for other causes of child death. The key strategies for the prevention of childhood pneumonia are case management, mainly through Integrated Management of Childhood Illness (IMCI), and immunization, particularly the newer vaccines against Haemophilus influenzae type b (Hib) and pneumococcus. There is a tendency to introduce both interventions into communities that already have access to basic health care and preventive services, thereby increasing the relative disadvantage experienced by those children without such access. Both strategies can be implemented in such a way as to decrease rather than increase inequity. It is important to monitor equity when introducing child-survival interventions. Economic poverty, as measured by analyses based on wealth quintiles, is an important determinant of inequity in health outcomes but in some settings other factors may be of greater importance. Geography and ethnicity can both lead to failed access to health care, and therefore inequity in child survival. Poorly functioning health facilities are also of major importance. Countries need to be aware of the main determinants of inequity in their communities so that measures can be taken to ensure that IMCI, new vaccine implementation and other child-survival strategies are introduced in an equitable manner.

  5. Does biological relatedness affect child survival?

    Directory of Open Access Journals (Sweden)

    2003-05-01

    Full Text Available Objective: We studied child survival in Rakai, Uganda where many children are fostered out or orphaned. Methods: Biological relatedness is measured as the average of the Wright's coefficients between each household member and the child. Instrumental variables for fostering include proportion of adult males in household, age and gender of household head. Control variables include SES, religion, polygyny, household size, child age, child birth size, and child HIV status. Results: Presence of both parents in the household increased the odds of survival by 28%. After controlling for the endogeneity of child placement decisions in a multivariate model we found that lower biological relatedness of a child was associated with statistically significant reductions in child survival. The effects of biological relatedness on child survival tend to be stronger for both HIV- and HIV+ children of HIV+ mothers. Conclusions: Reductions in the numbers of close relatives caring for children of HIV+ mothers reduce child survival.

  6. Modelos de predição para sobrevivência de plantas de Eucalyptus grandis Prediction models of Eucalyptus grandis plant survival

    Directory of Open Access Journals (Sweden)

    Telde Natel Custódio

    2009-01-01

    Full Text Available Objetivou-se com este trabalho comparar modelos de predição de plantas sobreviventes de Eucalyptus grandis. Utilizaram-se os seguintes modelos: modelo linear misto com os dados transformados, utilizando-se as transformações angular e BOX-COX; modelo linear generalizado misto com distribuição binomial e funções de ligação logística, probit e complemento log-log; modelo linear generalizado misto com distribuição Poisson e função de ligação logarítmica. Os dados são provenientes de um experimento em blocos ao acaso, para avaliação de progênies maternas de Eucalyptus grandis, aos 5 anos de idade, em que a variável resposta são plantas sobreviventes. Para comparação dos efeitos entre os modelos foram estimadas as correlações de Spearman e aplicado o teste de permutação de Fisher. Foi possível concluir que, o modelo linear generalizado misto com distribuição Poisson e função de ligação logarítmica se ajustou mal aos dados e que as estimativas para os efeitos fixos e predição para os efeitos aleatórios, não se diferenciaram entre os demais modelos estudados.The objective of this work was to compare models for prediction of the survival of plants of Eucalyptus grandis. The following models were used: linear mixed model with the transformed data, by utilizing the angular transformations and BOX-COX; generalized linear mixed model with binomial distribution and logistic functions, probit and complement log-log links; generalized linear mixed model with Poisson distribution and logarithmic link function. The data came from a randomized block experiment for evaluation of Eucalyptus grandis maternal progenies at five years old, in which the variable response are surviving plants. For comparison of the effects among the models the correlations of Spearman were estimated and the test of permutation of Fisher was applied. It was possible to conclude that: the generalized linear mixed model with Poisson distribution and

  7. Cancer survival among Alaska Native people.

    Science.gov (United States)

    Nash, Sarah H; Meisner, Angela L W; Zimpelman, Garrett L; Barry, Marc; Wiggins, Charles L

    2018-03-26

    Recent cancer survival trends among American Indian and Alaska Native (AN) people are not well understood; survival has not been reported among AN people since 2001. This study examined cause-specific survival among AN cancer patients for lung, colorectal, female breast, prostate, and kidney cancers. It evaluated whether survival differed between cancers diagnosed in 1992-2002 (the earlier period) and cancers diagnosed in 2003-2013 (the later period) and by the age at diagnosis (<65 vs ≥65 years), stage at diagnosis (local or regional/distant/unknown), and sex. Kaplan-Meier and Cox proportional hazards models were used to estimate univariate and multivariate-adjusted cause-specific survival for each cancer. An improvement was observed in 5-year survival over time from lung cancer (hazard ratio [HR] for the later period vs the earlier period, 0.83; 95% confidence interval [CI], 0.72-0.97), and a marginally nonsignificant improvement was observed for colorectal cancer (HR, 0.81; 95% CI, 0.66-1.01). Site-specific differences in survival were observed by age and stage at diagnosis. This study presents the first data on cancer survival among AN people in almost 2 decades. During this time, AN people have experienced improvements in survival from lung and colorectal cancers. The reasons for these improvements may include increased access to care (including screening) as well as improvements in treatment. Improving cancer survival should be a priority for reducing the burden of cancer among AN people and eliminating cancer disparities. Cancer 2018. © 2018 American Cancer Society. © 2018 American Cancer Society.

  8. The impact of applying product-modelling techniques in configurator projects

    DEFF Research Database (Denmark)

    Hvam, Lars; Kristjansdottir, Katrin; Shafiee, Sara

    2018-01-01

    This paper aims to increase understanding of the impact of using product-modelling techniques to structure and formalise knowledge in configurator projects. Companies that provide customised products increasingly apply configurators in support of sales and design activities, reaping benefits...... that include shorter lead times, improved quality of specifications and products, and lower overall product costs. The design and implementation of configurators are a challenging task that calls for scientifically based modelling techniques to support the formal representation of configurator knowledge. Even...... the phenomenon model and information model are considered visually, (2) non-UML-based modelling techniques, in which only the phenomenon model is considered and (3) non-formal modelling techniques. This study analyses the impact to companies from increased availability of product knowledge and improved control...

  9. Applied stochastic modelling

    CERN Document Server

    Morgan, Byron JT; Tanner, Martin Abba; Carlin, Bradley P

    2008-01-01

    Introduction and Examples Introduction Examples of data sets Basic Model Fitting Introduction Maximum-likelihood estimation for a geometric model Maximum-likelihood for the beta-geometric model Modelling polyspermy Which model? What is a model for? Mechanistic models Function Optimisation Introduction MATLAB: graphs and finite differences Deterministic search methods Stochastic search methods Accuracy and a hybrid approach Basic Likelihood ToolsIntroduction Estimating standard errors and correlations Looking at surfaces: profile log-likelihoods Confidence regions from profiles Hypothesis testing in model selectionScore and Wald tests Classical goodness of fit Model selection biasGeneral Principles Introduction Parameterisation Parameter redundancy Boundary estimates Regression and influence The EM algorithm Alternative methods of model fitting Non-regular problemsSimulation Techniques Introduction Simulating random variables Integral estimation Verification Monte Carlo inference Estimating sampling distributi...

  10. Estimating survival of dental fillings on the basis of interval-censored data and multi-state models

    DEFF Research Database (Denmark)

    Joly, Pierre; Gerds, Thomas A; Qvist, Vibeke

    2012-01-01

    We aim to compare the life expectancy of a filling in a primary tooth between two types of treatments. We define the probabilities that a dental filling survives without complication until the permanent tooth erupts from beneath (exfoliation). We relate the time to exfoliation of the tooth...... with all these particularities, we propose to use a parametric four-state model with three random effects to take into account the hierarchical cluster structure. For inference, right and interval censoring as well as left truncation have to be dealt with. With the proposed approach, we can conclude...... that the estimated probability that a filling survives without complication until exfoliation is larger for one treatment than for the other, for all ages of the child at the time of treatment....

  11. Modeling the decline of the Porcupine Caribou Herd, 1989-1998: the importance of survival vs. recruitment

    OpenAIRE

    Arthur, Stephen M.; Whitten, Kenneth R.; Mauer, Francis J.; Cooley, Dorothy

    2003-01-01

    The Porcupine caribou (Rangifer tarandus granti) herd increased from approximately 100 000 animals during the 1970s to 178 000 in 1989, then declined to 129 000 by 1998. Our objective was to model the dynamics of this herd and investigate the potential that lower calf recruitment, as was observed during 1991-1993, produced the observed population changes. A deterministic model was prepared using estimates of birth and survival rates that reproduced the pattern of population growth from 1971-1...

  12. Artificial neural network model of survival in patients treated with irradiation with and without concurrent chemotherapy for advanced carcinoma of the head and neck

    International Nuclear Information System (INIS)

    Bryce, Thomas J.; Dewhirst, Mark W.; Floyd, Carey E.; Hars, Vera; Brizel, David M.

    1998-01-01

    Purpose: This study was performed to investigate the feasibility of predicting survival in squamous cell carcinoma of the head and neck (SCCHN) with an artificial neural network (ANN), and to compare ANN performance with conventional models. Methods and Materials: Data were analyzed from a Phase III trial in which patients with locally advanced SCCHN received hyperfractionated irradiation with or without concurrent cisplatin and 5-fluorouracil. Of the 116 randomized patients, 95 who had 2-year follow-up and all required data were evaluated. ANN and logistic regression (LR) models were constructed to predict 2-year total survival using round-robin cross-validation. A modified staging model was also examined. Results: The best LR model used tumor size, nodal stage, and race to predict survival. The best ANN used nodal stage, tumor size, stage, and resectability, and hemoglobin. Treatment type did not predict 2-year survival and was not included in either model. Using the respective best feature sets, the area under the receiver operating characteristic curve (A z ) for the ANN was 0.78 ± 0.05, showing more accurate overall performance than LR (A z = 0.67 ± 0.05, p = 0.07). At 70% sensitivity, the ANN was 72% specific, while LR was 54% specific (p = 0.08). At 70% specificity, the ANN was 72% sensitive, while LR was 54% sensitive (p = 0.07). When both models used the five predictive variables best for an ANN, A z for LR decreased [A z = 0.61 ± 0.06, p z = 0.60 ± 0.07, p = 0.02 (ANN)]. Conclusions: An ANN modeled 2-year survival in this data set more accurately than LR or staging models and employed predictive variables that could not be used by LR. Further work is planned to confirm these results on larger patient samples, examining longer follow-up to incorporate treatment type into the model

  13. Nest survival is influenced by parental behaviour and heterospecifics in a mixed-species colony

    Science.gov (United States)

    Brussee, Brianne E.; Coates, Peter S.; Hothem, Roger L.; Howe, Kristy; Casazza, Michael L.; Eadie, John M.

    2016-01-01

    Studies of avian nest success often focus on examining influences of variation in environmental and seasonal factors. However, in-depth evaluations can also incorporate variation in individual incubation behaviour to further advance our understanding of avian reproductive ecology. We examined these relationships in colonially nesting Black-crowned Night-Herons Nycticorax nycticorax using intensive video-monitoring methods to quantify incubation behaviours. We modelled nest survival as a function of both extrinsic factors and incubation behaviours over a 3-year period (2010–12) on Alcatraz Island, USA. Model-averaged parameter estimates indicated that nest survival increased as a function of greater incubation constancy (% of time spent incubating eggs within a 24-h period), and average daily precipitation throughout the nesting stage. Common Ravens Corvus corax are the only known nest predator of Night-Herons on Alcatraz Island, as on many other coastal Pacific islands. We also investigated the effects of heterospecific nesting of California Gulls Larus californicus and Western Gulls Larus occidentalis in a mixed-species colony with Night-Herons, based on nesting proximity data collected over a 2-year period (2011–12). This second analysis indicated that, in addition to incubation behaviours, nesting heterospecifics are an important factor for explaining variation in Night-Heron nest survival. However, contrary to our original expectation, we found that Night-Herons experienced increased nest survival with increasing distance from gull colony boundaries. These results may apply to other areas with multiple colonial nesting species and similar predator communities and climatic patterns.

  14. Economic evaluation of nivolumab for the treatment of second-line advanced squamous NSCLC in Canada: a comparison of modeling approaches to estimate and extrapolate survival outcomes.

    Science.gov (United States)

    Goeree, Ron; Villeneuve, Julie; Goeree, Jeff; Penrod, John R; Orsini, Lucinda; Tahami Monfared, Amir Abbas

    2016-06-01

    Background Lung cancer is the most common type of cancer in the world and is associated with significant mortality. Nivolumab demonstrated statistically significant improvements in progression-free survival (PFS) and overall survival (OS) for patients with advanced squamous non-small cell lung cancer (NSCLC) who were previously treated. The cost-effectiveness of nivolumab has not been assessed in Canada. A contentious component of projecting long-term cost and outcomes in cancer relates to the modeling approach adopted, with the two most common approaches being partitioned survival (PS) and Markov models. The objectives of this analysis were to estimate the cost-utility of nivolumab and to compare the results using these alternative modeling approaches. Methods Both PS and Markov models were developed using docetaxel and erlotinib as comparators. A three-health state model was used consisting of progression-free, progressed disease, and death. Disease progression and time to progression were estimated by identifying best-fitting survival curves from the clinical trial data for PFS and OS. Expected costs and health outcomes were calculated by combining health-state occupancy with medical resource use and quality-of-life assigned to each of the three health states. The health outcomes included in the model were survival and quality-adjusted-life-years (QALYs). Results Nivolumab was found to have the highest expected per-patient cost, but also improved per-patient life years (LYs) and QALYs. Nivolumab cost an additional $151,560 and $140,601 per QALY gained compared to docetaxel and erlotinib, respectively, using a PS model approach. The cost-utility estimates using a Markov model were very similar ($152,229 and $141,838, respectively, per QALY gained). Conclusions Nivolumab was found to involve a trade-off between improved patient survival and QALYs, and increased cost. It was found that the use of a PS or Markov model produced very similar estimates of expected cost

  15. The limitations of applying rational decision-making models to ...

    African Journals Online (AJOL)

    The aim of this paper is to show the limitations of rational decision-making models as applied to child spacing and more specifically to the use of modern methods of contraception. In the light of factors known to influence low uptake of child spacing services in other African countries, suggestions are made to explain the ...

  16. A nomogram for predicting survival in patients with breast cancer brain metastasis.

    Science.gov (United States)

    Huang, Zhou; Sun, Bing; Wu, Shikai; Meng, Xiangying; Cong, Yang; Shen, Ge; Song, Santai

    2018-05-01

    Brain metastasis (BM) is common in patients with breast cancer. Predicting patient survival is critical for the clinical management of breast cancer brain metastasis (BCBM). The present study was designed to develop and evaluate a prognostic model for patients with newly diagnosed BCBM. Based on the clinical data of patients with BCBM treated in the Affiliated Hospital of Academy of Military Medical Sciences (Beijing, China) between 2002 and 2014, a nomogram was developed to predict survival using proportional hazards regression analysis. The model was validated internally by bootstrapping, and the concordance index (c-index) was calculated. A calibration curve and c-index were used to evaluate discriminatory and predictive ability, in order to compare the nomogram with widely used models, including recursive partitioning analysis (RPA), graded prognostic assessment (GPA) and breast-graded prognostic assessment (Breast-GPA). A total of 411 patients with BCBM were included in the development of this predictive model. The median overall survival time was 14.1 months. Statistically significant predictors for patient survival included biological subtype, Karnofsky performance score, leptomeningeal metastasis, extracranial metastasis, the number of brain metastases and disease-free survival. A nomogram for predicting 1- and 2-year overall survival rates was constructed, which exhibited good accuracy in predicting overall survival with a concordance index of 0.735. This model outperformed RPA, GPA and Breast-GPA, based on the comparisons of the c-indexes. The nomogram constructed based on a multiple factor analysis was able to more accurately predict the individual survival probability of patients with BCBM, compared with existing models.

  17. Linear mixing model applied to AVHRR LAC data

    Science.gov (United States)

    Holben, Brent N.; Shimabukuro, Yosio E.

    1993-01-01

    A linear mixing model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The reflective component of the 3.55 - 3.93 microns channel was extracted and used with the two reflective channels 0.58 - 0.68 microns and 0.725 - 1.1 microns to run a Constraine Least Squares model to generate vegetation, soil, and shade fraction images for an area in the Western region of Brazil. The Landsat Thematic Mapper data covering the Emas National park region was used for estimating the spectral response of the mixture components and for evaluating the mixing model results. The fraction images were compared with an unsupervised classification derived from Landsat TM data acquired on the same day. The relationship between the fraction images and normalized difference vegetation index images show the potential of the unmixing techniques when using coarse resolution data for global studies.

  18. Applying Probabilistic Decision Models to Clinical Trial Design

    Science.gov (United States)

    Smith, Wade P; Phillips, Mark H

    2018-01-01

    Clinical trial design most often focuses on a single or several related outcomes with corresponding calculations of statistical power. We consider a clinical trial to be a decision problem, often with competing outcomes. Using a current controversy in the treatment of HPV-positive head and neck cancer, we apply several different probabilistic methods to help define the range of outcomes given different possible trial designs. Our model incorporates the uncertainties in the disease process and treatment response and the inhomogeneities in the patient population. Instead of expected utility, we have used a Markov model to calculate quality adjusted life expectancy as a maximization objective. Monte Carlo simulations over realistic ranges of parameters are used to explore different trial scenarios given the possible ranges of parameters. This modeling approach can be used to better inform the initial trial design so that it will more likely achieve clinical relevance.

  19. Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

    Science.gov (United States)

    Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

    2018-05-01

    TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

  20. Commercial Consolidation Model Applied to Transport Infrastructure

    Energy Technology Data Exchange (ETDEWEB)

    Guilherme de Aragão, J.J.; Santos Fontes Pereira, L. dos; Yamashita, Y.

    2016-07-01

    Since the 1990s, transport concessions, including public-private partnerships (PPPs), have been increasingly adopted by governments as an alternative for financing and operations in public investments, especially in transport infrastructure. The advantage pointed out by proponents of these models lies in merging the expertise and capital of the private sector to the public interest. Several arrangements are possible and have been employed in different cases. After the duration of the first PPP contracts in transportation, many authors have analyzed the success and failure factors of partnerships. The occurrence of failures in some stages of the process can greatly encumber the public administration, incurring losses to the fiscal responsibility of the competent bodies. This article aims to propose a new commercial consolidation model applied to transport infrastructure to ensure fiscal sustainability and overcome the weaknesses of current models. Initially, a systematic review of the literature covering studies on transport concessions between 1990 and 2015 is offered, where the different approaches between various countries are compared and the critical success factors indicated in the studies are identified. In the subsequent part of the paper, an approach for the commercial consolidation of the infrastructure concessions is presented, where the concessionary is paid following a finalistic performance model, which includes the overall fiscal balance of regional growth. Finally, the papers analyses the usefulness of the model in coping with the critical success factors explained before. (Author)

  1. [Survival analysis with competing risks: estimating failure probability].

    Science.gov (United States)

    Llorca, Javier; Delgado-Rodríguez, Miguel

    2004-01-01

    To show the impact of competing risks of death on survival analysis. We provide an example of survival time without chronic rejection after heart transplantation, where death before rejection acts as a competing risk. Using a computer simulation, we compare the Kaplan-Meier estimator and the multiple decrement model. The Kaplan-Meier method overestimated the probability of rejection. Next, we illustrate the use of the multiple decrement model to analyze secondary end points (in our example: death after rejection). Finally, we discuss Kaplan-Meier assumptions and why they fail in the presence of competing risks. Survival analysis should be adjusted for competing risks of death to avoid overestimation of the risk of rejection produced with the Kaplan-Meier method.

  2. Applied economic model development algorithm for electronics company

    Directory of Open Access Journals (Sweden)

    Mikhailov I.

    2017-01-01

    Full Text Available The purpose of this paper is to report about received experience in the field of creating the actual methods and algorithms that help to simplify development of applied decision support systems. It reports about an algorithm, which is a result of two years research and have more than one-year practical verification. In a case of testing electronic components, the time of the contract conclusion is crucial point to make the greatest managerial mistake. At this stage, it is difficult to achieve a realistic assessment of time-limit and of wage-fund for future work. The creation of estimating model is possible way to solve this problem. In the article is represented an algorithm for creation of those models. The algorithm is based on example of the analytical model development that serves for amount of work estimation. The paper lists the algorithm’s stages and explains their meanings with participants’ goals. The implementation of the algorithm have made possible twofold acceleration of these models development and fulfilment of management’s requirements. The resulting models have made a significant economic effect. A new set of tasks was identified to be further theoretical study.

  3. Mammographic Density Reduction as a Prognostic Marker for Postmenopausal Breast Cancer: Results Using a Joint Longitudinal-Survival Modeling Approach.

    Science.gov (United States)

    Andersson, Therese M-L; Crowther, Michael J; Czene, Kamila; Hall, Per; Humphreys, Keith

    2017-11-01

    Previous studies have linked reductions in mammographic density after a breast cancer diagnosis to an improved prognosis. These studies focused on short-term change, using a 2-stage process, treating estimated change as a fixed covariate in a survival model. We propose the use of a joint longitudinal-survival model. This enables us to model long-term trends in density while accounting for dropout as well as for measurement error. We studied the change in mammographic density after a breast cancer diagnosis and its association with prognosis (measured by cause-specific mortality), overall and with respect to hormone replacement therapy and tamoxifen treatment. We included 1,740 women aged 50-74 years, diagnosed with breast cancer in Sweden during 1993-1995, with follow-up until 2008. They had a total of 6,317 mammographic density measures available from the first 5 years of follow-up, including baseline measures. We found that the impact of the withdrawal of hormone replacement therapy on density reduction was larger than that of tamoxifen treatment. Unlike previous studies, we found that there was an association between density reduction and survival, both for tamoxifen-treated women and women who were not treated with tamoxifen. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

  4. Survival and breeding of polar bears in the southern Beaufort Sea in relation to sea ice.

    Science.gov (United States)

    Regehr, Eric V; Hunter, Christine M; Caswell, Hal; Amstrup, Steven C; Stirling, Ian

    2010-01-01

    1. Observed and predicted declines in Arctic sea ice have raised concerns about marine mammals. In May 2008, the US Fish and Wildlife Service listed polar bears (Ursus maritimus) - one of the most ice-dependent marine mammals - as threatened under the US Endangered Species Act. 2. We evaluated the effects of sea ice conditions on vital rates (survival and breeding probabilities) for polar bears in the southern Beaufort Sea. Although sea ice declines in this and other regions of the polar basin have been among the greatest in the Arctic, to date population-level effects of sea ice loss on polar bears have only been identified in western Hudson Bay, near the southern limit of the species' range. 3. We estimated vital rates using multistate capture-recapture models that classified individuals by sex, age and reproductive category. We used multimodel inference to evaluate a range of statistical models, all of which were structurally based on the polar bear life cycle. We estimated parameters by model averaging, and developed a parametric bootstrap procedure to quantify parameter uncertainty. 4. In the most supported models, polar bear survival declined with an increasing number of days per year that waters over the continental shelf were ice free. In 2001-2003, the ice-free period was relatively short (mean 101 days) and adult female survival was high (0.96-0.99, depending on reproductive state). In 2004 and 2005, the ice-free period was longer (mean 135 days) and adult female survival was low (0.73-0.79, depending on reproductive state). Breeding rates and cub litter survival also declined with increasing duration of the ice-free period. Confidence intervals on vital rate estimates were wide. 5. The effects of sea ice loss on polar bears in the southern Beaufort Sea may apply to polar bear populations in other portions of the polar basin that have similar sea ice dynamics and have experienced similar, or more severe, sea ice declines. Our findings therefore are

  5. Survival during the Breeding Season: Nest Stage, Parental Sex, and Season Advancement Affect Reed Warbler Survival.

    Directory of Open Access Journals (Sweden)

    Kaja Wierucka

    Full Text Available Avian annual survival has received much attention, yet little is known about seasonal patterns in survival, especially of migratory passerines. In order to evaluate survival rates and timing of mortality within the breeding season of adult reed warblers (Acrocephalus scirpaceus, mark-recapture data were collected in southwest Poland, between 2006 and 2012. A total of 612 individuals (304 females and 308 males were monitored throughout the entire breeding season, and their capture-recapture histories were used to model survival rates. Males showed higher survival during the breeding season (0.985, 95% CI: 0.941-0.996 than females (0.869, 95% CI: 0.727-0.937. Survival rates of females declined with the progression of the breeding season (from May to August, while males showed constant survival during this period. We also found a clear pattern within the female (but not male nesting cycle: survival was significantly lower during the laying, incubation, and nestling periods (0.934, 95% CI: 0.898-0.958, when birds spent much time on the nest, compared to the nest building and fledgling periods (1.000, 95% CI: 1.00-1.000, when we did not record any female mortality. These data (coupled with some direct evidence, like bird corpses or blood remains found next to/on the nest may suggest that the main cause of adult mortality was on-nest predation. The calculated survival rates for both sexes during the breeding season were high compared to annual rates reported for this species, suggesting that a majority of mortality occurs at other times of the year, during migration or wintering. These results have implications for understanding survival variation within the reproductive period as well as general trends of avian mortality.

  6. An applied general equilibrium model for Dutch agribusiness policy analysis

    NARCIS (Netherlands)

    Peerlings, J.

    1993-01-01

    The purpose of this thesis was to develop a basic static applied general equilibrium (AGE) model to analyse the effects of agricultural policy changes on Dutch agribusiness. In particular the effects on inter-industry transactions, factor demand, income, and trade are of

  7. Military service, deployments, and exposures in relation to amyotrophic lateral sclerosis survival.

    Directory of Open Access Journals (Sweden)

    John D Beard

    Full Text Available Military veterans may have higher rates of amyotrophic lateral sclerosis (ALS mortality than non-veterans. Few studies, with sparse exposure information and mixed results, have studied relationships between military-related factors and ALS survival. We evaluated associations between military-related factors and ALS survival among U.S. military veteran cases.We followed 616 medical record-confirmed cases from enrollment (2005-2010 in the Genes and Environmental Exposures in Veterans with Amyotrophic Lateral Sclerosis study until death or July 25, 2013, whichever came first. We ascertained vital status information from several sources within the Department of Veterans Affairs. We obtained information regarding military service, deployments, and 39 related exposures via standardized telephone interviews. We used Cox proportional hazards regression models to estimate hazard ratios (HRs and 95% confidence intervals. We adjusted for potential confounding and missing covariate data biases via inverse probability weights. We also used inverse probability weights to adjust for potential selection bias among a case group that included a disproportionate number of long-term survivors at enrollment.We observed 446 deaths during 24,267 person-months of follow-up (median follow-up: 28 months. Survival was shorter for cases who served before 1950, were deployed to World War II, or mixed and applied burning agents, with HRs between 1.58 and 2.57. Longer survival was associated with exposure to: paint, solvents, or petrochemical substances; local food not provided by the Armed Forces; or burning agents or Agent Orange in the field with HRs between 0.56 and 0.73.Although most military-related factors were not associated with survival, associations we observed with shorter survival are potentially important because of the large number of military veterans.

  8. Quantifying discrimination of Framingham risk functions with different survival C statistics.

    Science.gov (United States)

    Pencina, Michael J; D'Agostino, Ralph B; Song, Linye

    2012-07-10

    Cardiovascular risk prediction functions offer an important diagnostic tool for clinicians and patients themselves. They are usually constructed with the use of parametric or semi-parametric survival regression models. It is essential to be able to evaluate the performance of these models, preferably with summaries that offer natural and intuitive interpretations. The concept of discrimination, popular in the logistic regression context, has been extended to survival analysis. However, the extension is not unique. In this paper, we define discrimination in survival analysis as the model's ability to separate those with longer event-free survival from those with shorter event-free survival within some time horizon of interest. This definition remains consistent with that used in logistic regression, in the sense that it assesses how well the model-based predictions match the observed data. Practical and conceptual examples and numerical simulations are employed to examine four C statistics proposed in the literature to evaluate the performance of survival models. We observe that they differ in the numerical values and aspects of discrimination that they capture. We conclude that the index proposed by Harrell is the most appropriate to capture discrimination described by the above definition. We suggest researchers report which C statistic they are using, provide a rationale for their selection, and be aware that comparing different indices across studies may not be meaningful. Copyright © 2012 John Wiley & Sons, Ltd.

  9. Estimating demographic parameters using a combination of known-fate and open N-mixture models.

    Science.gov (United States)

    Schmidt, Joshua H; Johnson, Devin S; Lindberg, Mark S; Adams, Layne G

    2015-10-01

    Accurate estimates of demographic parameters are required to infer appropriate ecological relationships and inform management actions. Known-fate data from marked individuals are commonly used to estimate survival rates, whereas N-mixture models use count data from unmarked individuals to estimate multiple demographic parameters. However, a joint approach combining the strengths of both analytical tools has not been developed. Here we develop an integrated model combining known-fate and open N-mixture models, allowing the estimation of detection probability, recruitment, and the joint estimation of survival. We demonstrate our approach through both simulations and an applied example using four years of known-fate and pack count data for wolves (Canis lupus). Simulation results indicated that the integrated model reliably recovered parameters with no evidence of bias, and survival estimates were more precise under the joint model. Results from the applied example indicated that the marked sample of wolves was biased toward individuals with higher apparent survival rates than the unmarked pack mates, suggesting that joint estimates may be more representative of the overall population. Our integrated model is a practical approach for reducing bias while increasing precision and the amount of information gained from mark-resight data sets. We provide implementations in both the BUGS language and an R package.

  10. WE-H-BRA-08: A Monte Carlo Cell Nucleus Model for Assessing Cell Survival Probability Based On Particle Track Structure Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lee, B [Northwestern Memorial Hospital, Chicago, IL (United States); Georgia Institute of Technology, Atlanta, GA (Georgia); Wang, C [Georgia Institute of Technology, Atlanta, GA (Georgia)

    2016-06-15

    Purpose: To correlate the damage produced by particles of different types and qualities to cell survival on the basis of nanodosimetric analysis and advanced DNA structures in the cell nucleus. Methods: A Monte Carlo code was developed to simulate subnuclear DNA chromatin fibers (CFs) of 30nm utilizing a mean-free-path approach common to radiation transport. The cell nucleus was modeled as a spherical region containing 6000 chromatin-dense domains (CDs) of 400nm diameter, with additional CFs modeled in a sparser interchromatin region. The Geant4-DNA code was utilized to produce a particle track database representing various particles at different energies and dose quantities. These tracks were used to stochastically position the DNA structures based on their mean free path to interaction with CFs. Excitation and ionization events intersecting CFs were analyzed using the DBSCAN clustering algorithm for assessment of the likelihood of producing DSBs. Simulated DSBs were then assessed based on their proximity to one another for a probability of inducing cell death. Results: Variations in energy deposition to chromatin fibers match expectations based on differences in particle track structure. The quality of damage to CFs based on different particle types indicate more severe damage by high-LET radiation than low-LET radiation of identical particles. In addition, the model indicates more severe damage by protons than of alpha particles of same LET, which is consistent with differences in their track structure. Cell survival curves have been produced showing the L-Q behavior of sparsely ionizing radiation. Conclusion: Initial results indicate the feasibility of producing cell survival curves based on the Monte Carlo cell nucleus method. Accurate correlation between simulated DNA damage to cell survival on the basis of nanodosimetric analysis can provide insight into the biological responses to various radiation types. Current efforts are directed at producing cell

  11. Risk Factors for Mortality among Adult HIV/AIDS Patients Following Antiretroviral Therapy in Southwestern Ethiopia: An Assessment through Survival Models

    Directory of Open Access Journals (Sweden)

    Dinberu Seyoum

    2017-03-01

    Full Text Available Introduction: Efforts have been made to reduce HIV/AIDS-related mortality by delivering antiretroviral therapy (ART treatment. However, HIV patients in resource-poor settings are still dying, even if they are on ART treatment. This study aimed to explore the factors associated with HIV/AIDS-related mortality in Southwestern Ethiopia. Method: A non-concurrent retrospective cohort study which collected data from the clinical records of adult HIV/AIDS patients, who initiated ART treatment and were followed between January 2006 and December 2010, was conducted, to explore the factors associated with HIV/AIDS-related mortality at Jimma University Specialized Hospital (JUSH. Survival times (i.e., the time from the onset of ART treatment to the death or censoring and different characteristics of patients were retrospectively examined. A best-fit model was chosen for the survival data, after the comparison between native semi-parametric Cox regression and parametric survival models (i.e., exponential, Weibull, and log-logistic. Result: A total of 456 HIV patients were included in the study, mostly females (312, 68.4%, with a median age of 30 years (inter-quartile range (IQR: 23–37 years. Estimated follow-up until December 2010 accounted for 1245 person-years at risk (PYAR and resulted in 66 (14.5% deaths and 390 censored individuals, representing a median survival time of 34.0 months ( IQR: 22.8–42.0 months. The overall mortality rate was 5.3/100 PYAR: 6.5/100 PYAR for males and 4.8/100 PYAR for females. The Weibull survival model was the best model for fitting the data (lowest AIC. The main factors associated with mortality were: baseline age (>35 years old, AHR = 3.8, 95% CI: 1.6–9.1, baseline weight (AHR = 0.93, 95% CI: 0.90–0.97, baseline WHO stage IV (AHR = 6.2, 95% CI: 2.2–14.2, and low adherence to ART treatment (AHR = 4.2, 95% CI: 2.5–7.1. Conclusion: An effective reduction in HIV/AIDS mortality could be achieved through timely ART

  12. Molecular profiles of screen detected vs. symptomatic breast cancer and their impact on survival: results from a clinical series

    International Nuclear Information System (INIS)

    Crispo, Anna; Esposito, Emanuela; Amore, Alfonso; Di Bonito, Maurizio; Botti, Gerardo; Montella, Maurizio; Barba, Maddalena; D’Aiuto, Giuseppe; De Laurentiis, Michelino; Grimaldi, Maria; Rinaldo, Massimo; Caolo, Giuseppina; D’Aiuto, Massimiliano; Capasso, Immacolata

    2013-01-01

    Stage shift is widely considered a major determinant of the survival benefit conferred by breast cancer screening. However, factors and mechanisms underlying such a prognostic advantage need further clarification. We sought to compare the molecular characteristics of screen detected vs. symptomatic breast cancers and assess whether differences in tumour biology might translate into survival benefit. In a clinical series of 448 women with operable breast cancer, the Kaplan-Meier method and the log-rank test were used to estimate the likelihood of cancer recurrence and death. The Cox proportional hazard model was used for the multivariate analyses including mode of detection, age at diagnosis, tumour size, and lymph node status. These same models were applied to subgroups defined by molecular subtypes. Screen detected breast cancers tended to show more favourable clinicopathological features and survival outcomes compared to symptomatic cancers. The luminal A subtype was more common in women with mammography detected tumours than in symptomatic patients (68.5 vs. 59.0%, p=0.04). Data analysis across categories of molecular subtypes revealed significantly longer disease free and overall survival for screen detected cancers with a luminal A subtype only (p=0.01 and 0.02, respectively). For women with a luminal A subtype, the independent prognostic role of mode of detection on recurrence was confirmed in Cox proportional hazard models (p=0.03). An independent role of modality of detection on survival was also suggested (p=0.05). Molecular subtypes did not substantially explain the differences in survival outcomes between screened and symptomatic patients. However, our results suggest that molecular profiles might play a role in interpreting such differences at least partially. Further studies are warranted to reinterpret the efficacy of screening programmes in the light of tumour biology

  13. Remote sensing applied to numerical modelling. [water resources pollution

    Science.gov (United States)

    Sengupta, S.; Lee, S. S.; Veziroglu, T. N.; Bland, R.

    1975-01-01

    Progress and remaining difficulties in the construction of predictive mathematical models of large bodies of water as ecosystems are reviewed. Surface temperature is at present the only variable than can be measured accurately and reliably by remote sensing techniques, but satellite infrared data are of sufficient resolution for macro-scale modeling of oceans and large lakes, and airborne radiometers are useful in meso-scale analysis (of lakes, bays, and thermal plumes). Finite-element and finite-difference techniques applied to the solution of relevant coupled time-dependent nonlinear partial differential equations are compared, and the specific problem of the Biscayne Bay and environs ecosystem is tackled in a finite-differences treatment using the rigid-lid model and a rigid-line grid system.

  14. Survival analysis of a treatment data for cancer of the larynx

    International Nuclear Information System (INIS)

    Khan, K.

    2002-01-01

    In this paper a survival analysis of the survival time is done. The Cox regression model is fitted to the survival time with the assumption of proportional hazard. A model is selected after inclusion and exclusion of factors and variables as explanatory variables. The assumption of proportional hazards is tested in the manner suggested by Harrell (1986). The assumption of proportional hazards is supported by these tests. However the plot of Schoenfeld residuals against dose gave a little evidence of non validity of the proportional hazard assumption. The assumption seems to be satisfied for variable time. The martingale residuals suggest no pattern for variable age. The functional form of dose is not linear. Hence the quadratic dose is used as an explanatory variable. A comparison of logistic regression analysis and survival analysis is also made in this paper. It can be concluded that Cox proportional hazards model is a better model than the logistic model as it is more parsimonious and utilizes more information. (author)

  15. The synthetic parasite-derived peptide GK1 increases survival in a preclinical mouse melanoma model.

    Science.gov (United States)

    Pérez-Torres, Armando; Vera-Aguilera, Jesús; Hernaiz-Leonardo, Juan Carlos; Moreno-Aguilera, Eduardo; Monteverde-Suarez, Diego; Vera-Aguilera, Carlos; Estrada-Bárcenas, Daniel

    2013-11-01

    The therapeutic efficacy of a synthetic parasite-derived peptide GK1, an immune response booster, was evaluated in a mouse melanoma model. This melanoma model correlates with human stage IIb melanoma, which is treated with wide surgical excision; a parallel study employing a surgical treatment was carried out as an instructive goal. C57BL/6 mice were injected subcutaneously in the flank with 2×10(5) B16-F10 murine melanoma cells. When the tumors reached 20 mm3, mice were separated into two different groups; the GK1 group, treated weekly with peritumoral injections of GK1 (10 μg/100 μL of sterile saline solution) and the control group, treated weekly with an antiseptic peritumoral injection of 100 μL of sterile saline solution without further intervention. All mice were monitored daily for clinical appearance, tumor size, and survival. Surgical treatment was performed in parallel when the tumor size was 20 mm3 (group A), 500 mm3 (group B), and >500 mm3 (group C). The GK1 peptide effectively increased the mean survival time by 9.05 days, corresponding to an increase of 42.58%, and significantly delayed tumor growth from day 3 to 12 of treatment. In addition, tumor necrosis was significantly increased (pcancers remains to be determined, and surgical removal remains a challenge for any new experimental treatment of melanoma in mouse models.

  16. FDTD-based Transcranial Magnetic Stimulation model applied to specific neurodegenerative disorders.

    Science.gov (United States)

    Fanjul-Vélez, Félix; Salas-García, Irene; Ortega-Quijano, Noé; Arce-Diego, José Luis

    2015-01-01

    Non-invasive treatment of neurodegenerative diseases is particularly challenging in Western countries, where the population age is increasing. In this work, magnetic propagation in human head is modelled by Finite-Difference Time-Domain (FDTD) method, taking into account specific characteristics of Transcranial Magnetic Stimulation (TMS) in neurodegenerative diseases. It uses a realistic high-resolution three-dimensional human head mesh. The numerical method is applied to the analysis of magnetic radiation distribution in the brain using two realistic magnetic source models: a circular coil and a figure-8 coil commonly employed in TMS. The complete model was applied to the study of magnetic stimulation in Alzheimer and Parkinson Diseases (AD, PD). The results show the electrical field distribution when magnetic stimulation is supplied to those brain areas of specific interest for each particular disease. Thereby the current approach entails a high potential for the establishment of the current underdeveloped TMS dosimetry in its emerging application to AD and PD. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  17. A model for website analysis and\tconception: the Website Canvas Model applied to\tEldiario.es

    Directory of Open Access Journals (Sweden)

    Carles Sanabre Vives

    2015-11-01

    Full Text Available This article presents the model of ideation and analysis called Website CanvasModel. It allows identifying the key aspects for a website to be successful, and shows how ithas been applied to Eldiario.es. As a result, the key factors prompting the success of thisdigital newspaper have been identified.

  18. A general diagnostic model applied to language testing data.

    Science.gov (United States)

    von Davier, Matthias

    2008-11-01

    Probabilistic models with one or more latent variables are designed to report on a corresponding number of skills or cognitive attributes. Multidimensional skill profiles offer additional information beyond what a single test score can provide, if the reported skills can be identified and distinguished reliably. Many recent approaches to skill profile models are limited to dichotomous data and have made use of computationally intensive estimation methods such as Markov chain Monte Carlo, since standard maximum likelihood (ML) estimation techniques were deemed infeasible. This paper presents a general diagnostic model (GDM) that can be estimated with standard ML techniques and applies to polytomous response variables as well as to skills with two or more proficiency levels. The paper uses one member of a larger class of diagnostic models, a compensatory diagnostic model for dichotomous and partial credit data. Many well-known models, such as univariate and multivariate versions of the Rasch model and the two-parameter logistic item response theory model, the generalized partial credit model, as well as a variety of skill profile models, are special cases of this GDM. In addition to an introduction to this model, the paper presents a parameter recovery study using simulated data and an application to real data from the field test for TOEFL Internet-based testing.

  19. Long-term survival and function after suspected gram-negative sepsis.

    Science.gov (United States)

    Perl, T M; Dvorak, L; Hwang, T; Wenzel, R P

    1995-07-26

    To determine the long-term (> 3 months) survival of septic patients, to develop mathematical models that predict patients likely to survive long-term, and to measure the health and functional status of surviving patients. A large tertiary care university hospital and an associated Veterans Affairs Medical Center. From December 1986 to December 1990, a total of 103 patients with suspected gram-negative sepsis entered a double-blind, placebo-controlled efficacy trial of monoclonal antiendotoxin antibody. Of these, we followed up 100 patients for 7667 patient-months. Beginning in May 1992, we reviewed hospital records and contacted all known survivors. We measured the health status of all surviving patients. The determinants of long-term survival (up to 6 years) were identified through two Cox proportional hazard regression models: one that included patient characteristics identified at the time of sepsis (bedside model) and another that included bedside, infection-related, and treatment characteristics (overall model). Of the 60 patients in the cohort who died at a median interval of 30.5 days after sepsis, 32 died within the first month of the septic episode, seven died within 3 months, and four more died within 6 months. In the bedside multivariate model constructed to predict long-term survival, large hazard ratios (HRs) were associated with severity of underlying illness as classified by McCabe and Jackson criteria (for rapidly fatal disease, HR = 30.4, P respiratory distress syndrome (HR = 2.3; P = .02) predicted patients most likely to die. The Acute Physiology and Chronic Health Evaluation II score was not a significant predictor of outcome when either model included the simpler McCabe and Jackson classification of underlying disease severity. We compared the health status scores with norms for the general population and found that patients with resolved sepsis reported more physical dysfunction (P bedridden), suggesting that the patients' physical function

  20. Applying the Flipped Classroom Model to English Language Arts Education

    Science.gov (United States)

    Young, Carl A., Ed.; Moran, Clarice M., Ed.

    2017-01-01

    The flipped classroom method, particularly when used with digital video, has recently attracted many supporters within the education field. Now more than ever, language arts educators can benefit tremendously from incorporating flipped classroom techniques into their curriculum. "Applying the Flipped Classroom Model to English Language Arts…

  1. Estimating haplotype effects for survival data

    DEFF Research Database (Denmark)

    Scheike, Thomas; Martinussen, Torben; Silver, J

    2010-01-01

    Genetic association studies often investigate the effect of haplotypes on an outcome of interest. Haplotypes are not observed directly, and this complicates the inclusion of such effects in survival models. We describe a new estimating equations approach for Cox's regression model to assess haplo...

  2. Oilseed Meal Effects on the Emergence and Survival of Crop and Weed Species

    Directory of Open Access Journals (Sweden)

    Katie L. Rothlisberger

    2012-01-01

    Full Text Available Oilseed crops are being widely evaluated for potential biodiesel production. Seed meal (SM remaining after extracting oil may have use as bioherbicides or organic fertilizers. Brassicaceae SM often contains glucosinolates that hydrolyze into biologically active compounds that may inhibit various pests. Jatropha curcas SM contains curcin, a phytoxin. A 14-day greenhouse study determined that Sinapis alba (white mustard, Brassica juncea (Indian mustard, Camelina sativa, and Jatropha curcas applied to soil at varying application rates [0, 0.5, 1.0, and 2.5% (w/w] and incubation times (1, 7, and 14 d prior to planting affected seed emergence and seedling survival of cotton [Gossypium hirsutum (L.], sorghum [Sorghum bicolor (L. Moench], johnsongrass (Sorghum halepense, and redroot pigweed (Amaranthus retroflexus. With each species, emergence and survival was most decreased by 2.5% SM application applied at 1 and 7 d incubations. White mustard SM incubated for 1 d applied at low and high rates had similar negative effects on johnsongrass seedlings. Redroot pigweed seedling survival was generally most decreased by all 2.5% SM applications. Based on significant effects determined by ANOVA, results suggested that the type, rate, and timing of SM application should be considered before land-applying SMs in cropping systems.

  3. Radiation therapy improves survival in rectal small cell cancer - Analysis of Surveillance Epidemiology and End Results (SEER) data.

    Science.gov (United States)

    Modrek, Aram S; Hsu, Howard C; Leichman, Cynthia G; Du, Kevin L

    2015-04-24

    Small cell carcinoma of the rectum is a rare neoplasm with scant literature to guide treatment. We used the Surveillance Epidemiology and End Results (SEER) database to investigate the role of radiation therapy in the treatment of this cancer. The SEER database (National Cancer Institute) was queried for locoregional cases of small cell rectal cancer. Years of diagnosis were limited to 1988-2010 (most recent available) to reduce variability in staging criteria or longitudinal changes in surgery and radiation techniques. Two month conditional survival was applied to minimize bias by excluding patients who did not survive long enough to receive cancer-directed therapy. Patient demographics between the RT and No_RT groups were compared using Pearson Chi-Square tests. Overall survival was compared between patients who received radiotherapy (RT, n = 43) and those who did not (No_RT, n = 28) using the Kaplan-Meier method. Multivariate Cox proportional hazards model was used to evaluate important covariates. Median survival was significantly longer for patients who received radiation compared to those who were not treated with radiation; 26 mo vs. 8 mo, respectively (log-rank P = 0.009). We also noted a higher 1-year overall survival rate for those who received radiation (71.1% vs. 37.8%). Unadjusted hazard ratio for death (HR) was 0.495 with the use of radiation (95% CI 0.286-0.858). Among surgery, radiotherapy, sex and age at diagnosis, radiation therapy was the only significant factor for overall survival with a multivariate HR for death of 0.393 (95% CI 0.206-0.750, P = 0.005). Using SEER data, we have identified a significant survival advantage with the use of radiation therapy in the setting of rectal small cell carcinoma. Limitations of the SEER data apply to this study, particularly the lack of information on chemotherapy usage. Our findings strongly support the use of radiation therapy for patients with locoregional small cell rectal cancer.

  4. Applying circular economy innovation theory in business process modeling and analysis

    Science.gov (United States)

    Popa, V.; Popa, L.

    2017-08-01

    The overall aim of this paper is to develop a new conceptual framework for business process modeling and analysis using circular economy innovative theory as a source for business knowledge management. The last part of the paper presents an author’s proposed basic structure for a new business models applying circular economy innovation theories. For people working on new innovative business models in the field of the circular economy this paper provides new ideas for clustering their concepts.

  5. Overexpression of survival motor neuron improves neuromuscular function and motor neuron survival in mutant SOD1 mice.

    Science.gov (United States)

    Turner, Bradley J; Alfazema, Neza; Sheean, Rebecca K; Sleigh, James N; Davies, Kay E; Horne, Malcolm K; Talbot, Kevin

    2014-04-01

    Spinal muscular atrophy results from diminished levels of survival motor neuron (SMN) protein in spinal motor neurons. Low levels of SMN also occur in models of amyotrophic lateral sclerosis (ALS) caused by mutant superoxide dismutase 1 (SOD1) and genetic reduction of SMN levels exacerbates the phenotype of transgenic SOD1(G93A) mice. Here, we demonstrate that SMN protein is significantly reduced in the spinal cords of patients with sporadic ALS. To test the potential of SMN as a modifier of ALS, we overexpressed SMN in 2 different strains of SOD1(G93A) mice. Neuronal overexpression of SMN significantly preserved locomotor function, rescued motor neurons, and attenuated astrogliosis in spinal cords of SOD1(G93A) mice. Despite this, survival was not prolonged, most likely resulting from SMN mislocalization and depletion of gems in motor neurons of symptomatic mice. Our results reveal that SMN upregulation slows locomotor deficit onset and motor neuron loss in this mouse model of ALS. However, disruption of SMN nuclear complexes by high levels of mutant SOD1, even in the presence of SMN overexpression, might limit its survival promoting effects in this specific mouse model. Studies in emerging mouse models of ALS are therefore warranted to further explore the potential of SMN as a modifier of ALS. Copyright © 2014 Elsevier Inc. All rights reserved.

  6. Cougar survival and source-sink structure on Greater Yellowstone's Northern Range

    Science.gov (United States)

    Ruth, T.K.; Haroldson, M.A.; Murphy, K.M.; Buotte, P.C.; Hornocker, M.G.; Quigley, H.B.

    2011-01-01

    We studied survival and causes of mortality of radiocollared cougars (Puma concolor) on the Greater Yellowstone Northern Range (GYNR) prior to (1987–1994) and after wolf (Canis lupus) reintroduction (1998–2005) and evaluated temporal, spatial, and environmental factors that explain variation in adult, subadult, and kitten survival. Using Program MARK and multimodel inference, we modeled cougar survival based on demographic status, season, and landscape attributes. Our best models for adult and independent subadults indicated that females survived better than males and survival increased with age until cougars reached older ages. Lower elevations and increasing density of roads, particularly in areas open to cougar hunting north of Yellowstone National Park (YNP), increased mortality risks for cougars on the GYNR. Indices of ungulate biomass, cougar and wolf population size, winter severity, rainfall, and individual characteristics such as the presence of dependent young, age class, and use of Park or Wilderness were not important predictors of survival. Kitten survival increased with age, was lower during winter, increased with increasing minimum estimates of elk calf biomass, and increased with increasing density of adult male cougars. Using our best model, we mapped adult cougar survival on the GYNR landscape. Results of receiver operating characteristic (ROC) analysis indicated a good model fit for both female (area under the curve [AUC] = 0.81, 95%CI = 0.70–0.92, n = 35 locations) and male cougars (AUC = 0.84, 95%CI = 0.74–0.94, n = 49 locations) relative to hunter harvest locations in our study area. Using minimum estimates of survival necessary to sustain the study population, we developed a source-sink surface and we identify several measures that resource management agencies can take to enhance cougar population management based on a source-sink strategy.

  7. Intracranial AAV-IFN-β gene therapy eliminates invasive xenograft glioblastoma and improves survival in orthotopic syngeneic murine model.

    Science.gov (United States)

    GuhaSarkar, Dwijit; Neiswender, James; Su, Qin; Gao, Guangping; Sena-Esteves, Miguel

    2017-02-01

    The highly invasive property of glioblastoma (GBM) cells and genetic heterogeneity are largely responsible for tumor recurrence after the current standard-of-care treatment and thus a direct cause of death. Previously, we have shown that intracranial interferon-beta (IFN-β) gene therapy by locally administered adeno-associated viral vectors (AAV) successfully treats noninvasive orthotopic glioblastoma models. Here, we extend these findings by testing this approach in invasive human GBM xenograft and syngeneic mouse models. First, we show that a single intracranial injection of AAV encoding human IFN-β eliminates invasive human GBM8 tumors and promotes long-term survival. Next, we screened five AAV-IFN-β vectors with different promoters to drive safe expression of mouse IFN-β in the brain in the context of syngeneic GL261 tumors. Two AAV-IFN-β vectors were excluded due to safety concerns, but therapeutic studies with the other three vectors showed extensive tumor cell death, activation of microglia surrounding the tumors, and a 56% increase in median survival of the animals treated with AAV/P2-Int-mIFN-β vector. We also assessed the therapeutic effect of combining AAV-IFN-β therapy with temozolomide (TMZ). As TMZ affects DNA replication, an event that is crucial for second-strand DNA synthesis of single-stranded AAV vectors before active transcription, we tested two TMZ treatment regimens. Treatment with TMZ prior to AAV-IFN-β abrogated any benefit from the latter, while the reverse order of treatment doubled the median survival compared to controls. These studies demonstrate the therapeutic potential of intracranial AAV-IFN-β therapy in a highly migratory GBM model as well as in a syngeneic mouse model and that combination with TMZ is likely to enhance its antitumor potency. © 2016 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.

  8. Adélie penguin survival: age structure, temporal variability and environmental influences.

    Science.gov (United States)

    Emmerson, Louise; Southwell, Colin

    2011-12-01

    The driving factors of survival, a key demographic process, have been particularly challenging to study, especially for winter migratory species such as the Adélie penguin (Pygoscelis adeliae). While winter environmental conditions clearly influence Antarctic seabird survival, it has been unclear to which environmental features they are most likely to respond. Here, we examine the influence of environmental fluctuations, broad climatic conditions and the success of the breeding season prior to winter on annual survival of an Adélie penguin population using mark-recapture models based on penguin tag and resight data over a 16-year period. This analysis required an extension to the basic Cormack-Jolly-Seber model by incorporating age structure in recapture and survival sub-models. By including model covariates, we show that survival of older penguins is primarily related to the amount and concentration of ice present in their winter foraging grounds. In contrast, fledgling and yearling survival depended on other factors in addition to the physical marine environment and outcomes of the previous breeding season, but we were unable to determine what these were. The relationship between sea-ice and survival differed with penguin age: extensive ice during the return journey to breeding colonies was detrimental to survival for the younger penguins, whereas either too little or too much ice (between 15 and 80% cover) in the winter foraging grounds was detrimental for adults. Our results demonstrate that predictions of Adélie penguin survival can be improved by taking into account penguin age, prior breeding conditions and environmental features.

  9. A Research on the E-commerce Applied to the Construction of Marketing Model

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The function of E-commerce is becoming more and more widely applied to many fields,which bring about some new challenges and opportunities for the construction of marketing model.It is proved that the more E-com- merce applied to the construction of marketing,the more precision of forecast for the enterprises can acquire,which is very helpful for the production and marketing of enterprises.Therefore,the research on the E-commerce applied to the construction of marketing is popular today.This paper applie...

  10. 5 years survival after radiotherapy for lung cancer

    Energy Technology Data Exchange (ETDEWEB)

    Kujawska, J; Strzeszynski, J [Instytut Onkologii, Krakow (Poland)

    1973-01-01

    Radiotherapy was applied to 256 patients with lung cancer treated in the Institute of Oncology in Krakow in the years 1959-1967. Malignancy had been confirmed throughout in organs of the chest cavity, and diagnosed by microscopic examination. Eleven patients, i.e. 4%, survived 5 years. Survival rate was related to the stage of the disease and the microscopic pattern. Some patients were cured after irradiation of lung cancer, using nominal doses lower than the lethal dose for squamous cell cancer. The specific physical conditions of radiation absorption in the chest cavity evidently made the effective dose inside the cavity much higher than the nominal dose.

  11. Apply Functional Modelling to Consequence Analysis in Supervision Systems

    DEFF Research Database (Denmark)

    Zhang, Xinxin; Lind, Morten; Gola, Giulio

    2013-01-01

    This paper will first present the purpose and goals of applying functional modelling approach to consequence analysis by adopting Multilevel Flow Modelling (MFM). MFM Models describe a complex system in multiple abstraction levels in both means-end dimension and whole-part dimension. It contains...... consequence analysis to practical or online applications in supervision systems. It will also suggest a multiagent solution as the integration architecture for developing tools to facilitate the utilization results of functional consequence analysis. Finally a prototype of the multiagent reasoning system...... causal relations between functions and goals. A rule base system can be developed to trace the causal relations and perform consequence propagations. This paper will illustrate how to use MFM for consequence reasoning by using rule base technology and describe the challenges for integrating functional...

  12. Nonparametric Bayesian inference for mean residual life functions in survival analysis.

    Science.gov (United States)

    Poynor, Valerie; Kottas, Athanasios

    2018-01-19

    Modeling and inference for survival analysis problems typically revolves around different functions related to the survival distribution. Here, we focus on the mean residual life (MRL) function, which provides the expected remaining lifetime given that a subject has survived (i.e. is event-free) up to a particular time. This function is of direct interest in reliability, medical, and actuarial fields. In addition to its practical interpretation, the MRL function characterizes the survival distribution. We develop general Bayesian nonparametric inference for MRL functions built from a Dirichlet process mixture model for the associated survival distribution. The resulting model for the MRL function admits a representation as a mixture of the kernel MRL functions with time-dependent mixture weights. This model structure allows for a wide range of shapes for the MRL function. Particular emphasis is placed on the selection of the mixture kernel, taken to be a gamma distribution, to obtain desirable properties for the MRL function arising from the mixture model. The inference method is illustrated with a data set of two experimental groups and a data set involving right censoring. The supplementary material available at Biostatistics online provides further results on empirical performance of the model, using simulated data examples. © The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  13. Robust estimates of environmental effects on population vital rates: an integrated capture–recapture model of seasonal brook trout growth, survival and movement in a stream network

    Science.gov (United States)

    Letcher, Benjamin H.; Schueller, Paul; Bassar, Ronald D.; Nislow, Keith H.; Coombs, Jason A.; Sakrejda, Krzysztof; Morrissey, Michael; Sigourney, Douglas B.; Whiteley, Andrew R.; O'Donnell, Matthew J.; Dubreuil, Todd L.

    2015-01-01

    Modelling the effects of environmental change on populations is a key challenge for ecologists, particularly as the pace of change increases. Currently, modelling efforts are limited by difficulties in establishing robust relationships between environmental drivers and population responses.We developed an integrated capture–recapture state-space model to estimate the effects of two key environmental drivers (stream flow and temperature) on demographic rates (body growth, movement and survival) using a long-term (11 years), high-resolution (individually tagged, sampled seasonally) data set of brook trout (Salvelinus fontinalis) from four sites in a stream network. Our integrated model provides an effective context within which to estimate environmental driver effects because it takes full advantage of data by estimating (latent) state values for missing observations, because it propagates uncertainty among model components and because it accounts for the major demographic rates and interactions that contribute to annual survival.We found that stream flow and temperature had strong effects on brook trout demography. Some effects, such as reduction in survival associated with low stream flow and high temperature during the summer season, were consistent across sites and age classes, suggesting that they may serve as robust indicators of vulnerability to environmental change. Other survival effects varied across ages, sites and seasons, indicating that flow and temperature may not be the primary drivers of survival in those cases. Flow and temperature also affected body growth rates; these responses were consistent across sites but differed dramatically between age classes and seasons. Finally, we found that tributary and mainstem sites responded differently to variation in flow and temperature.Annual survival (combination of survival and body growth across seasons) was insensitive to body growth and was most sensitive to flow (positive) and temperature (negative

  14. Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

    Science.gov (United States)

    Said, Nadia; Engelhart, Michael; Kirches, Christian; Körkel, Stefan; Holt, Daniel V

    2016-01-01

    Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

  15. Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

    Directory of Open Access Journals (Sweden)

    Nadia Said

    Full Text Available Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

  16. Contrasting long-term survival of two outplanted Mojave Desert perennials for post-fire revegetation

    Science.gov (United States)

    Scoles-Sciulla, Sara J.; Defalco, Lesley A.; Esque, Todd C.

    2015-01-01

    Post-fire recovery of arid shrublands is typically slow, and planting greenhouse-raised seedlings may be a means of jump-starting this process. Recovery can be further accelerated by understanding the factors controlling post-planting survival. In fall 2007 and 2009, we outplanted seedlings of two contrasting native evergreen shrubs—fast-growing Nevada jointfir and slow-growing blackbrush—across five burned sites in the Mojave Desert. To increase soil moisture and optimize seedling survival, we experimentally applied and evaluated soil amendments and supplemental watering. We also evaluated two herbicides that reduce competitive invasive annual grasses and two types of herbivore protection. Survival of jointfir outplanted in 2007 was 61% after 43 months, and site largely influenced survival, while herbicide containing imazapic applied more than one year after outplanting reduced survival. Reduced survival of jointfir outplanted in 2009 coincided with delayed seasonal precipitation that intensified foliar damage by small mammals. In contrast, blackbrush survival was 4% after 43 months, and was influenced by site, type of herbivore protection, and greenhouse during the 2007 outplanting, and soil amendment during 2009. Counter to expectations, we found that supplemental watering and soil amendments did not influence long-term survival of either blackbrush or jointfir. Shrub species with rapid growth rates and broad environmental tolerances, such as jointfir, make ideal candidates for outplanting, provided that seedlings are protected from herbivores. Re-introduction of species with slow growth rates and narrow environmental tolerances, such as blackbrush, requires careful consideration to optimize pre- and post-planting conditions.

  17. New graphic AUC-based method to estimate overall survival benefit: pomalidomide reanalysis.

    Science.gov (United States)

    Fenix-Caballero, S; Diaz-Navarro, J; Prieto-Callejero, B; Rios-Sanchez, E; Alegre-del Rey, E J; Borrero-Rubio, J M

    2016-02-01

    Difference in median survival is an erratic measure and sometimes does not provide a good assessment of survival benefit. The aim of this study was to reanalyse the overall survival benefit of pomalidomide from pivotal clinical trial using a new area under curve (AUC)-based method. In the pivotal trial, pomalidomide plus low-dose dexamethasone showed a significant survival benefit over high-dose dexamethasone, with a difference between medians of 4.6 months. The new AUC method applied to the survival curves, obtained an overall survival benefit of 2.6 months for the pomalidomide treatment. This average difference in OS was calculated for the 61.5% of patients for whom the time to event is reliable enough. This 2-month differential would have major clinical and pharmacoeconomic implications, on both cost-effectiveness studies and on the willingness of the healthcare systems to pay for this treatment. © 2015 John Wiley & Sons Ltd.

  18. A two-zone cosmic ray propagation model and its implication of the surviving fraction of radioactive cosmic ray isotopes

    International Nuclear Information System (INIS)

    Simon, M.; Scherzer, R.; Enge, W.

    1977-01-01

    In cosmic ray propagation calculations one can usually assume a homogeneous distribution of interstellar matter. The crucial astrophysical parameters in these models are: The path length distribution, the age of the cosmic ray particles and the interstellar matter density. These values are interrelated. The surviving fraction of radioactive cosmic ray isotopes is often used to determine a mean matter density of that region, where the cosmic ray particles may mainly reside. Using a Monte Carlo Propagation Program we calculated the change in the surviving fraction quantitatively assuming a region around the sources with higher matter density. (author)

  19. Lamb survival analysis from birth to weaning in Iranian Kermani sheep.

    Science.gov (United States)

    Barazandeh, Arsalan; Moghbeli, Sadrollah Molaei; Vatankhah, Mahmood; Hossein-Zadeh, Navid Ghavi

    2012-04-01

    Survival records from 1,763 Kermani lambs born between 1996 and 2004 from 294 ewes and 81 rams were used to determine genetic and non-genetic factors affecting lamb survival. Traits included were lamb survival across five periods from birth to 7, 14, 56, 70, and 90 days of age. Traits were analyzed under Weibull proportional hazard sire models. Several binary analyses were also conducted using animal models. Statistical models included the fixed class effects of sex of lamb, month and year of birth, a covariate effect of birth weight, and random genetic effects of both sire (in survival analyses) and animal (in binary analyses). The average survival to 90 days of age was 94.8%. Hazard rates ranged from 1.00 (birth to 90 days of age) to 1.73 (birth to 7 days of age) between the two sexes indicating that male lambs were at higher risk of mortality than females (P lamb survival and lamb birth weight, suggesting that viability and birth weight could be considered simultaneously in the selection programs to obtain optimal birth weight in Kermani lambs. Estimates of heritabilities from survival analyses were medium and ranged from 0.23 to 0.29. In addition, heritability estimates obtained from binary analyses were low and varied from 0.04 to 0.09. The results of this study suggest that progress in survival traits could be possible through managerial strategies and genetic selection.

  20. Agrochemical fate models applied in agricultural areas from Colombia

    Science.gov (United States)

    Garcia-Santos, Glenda; Yang, Jing; Andreoli, Romano; Binder, Claudia

    2010-05-01

    The misuse application of pesticides in mainly agricultural catchments can lead to severe problems for humans and environment. Especially in developing countries where there is often found overuse of agrochemicals and incipient or lack of water quality monitoring at local and regional levels, models are needed for decision making and hot spots identification. However, the complexity of the water cycle contrasts strongly with the scarce data availability, limiting the number of analysis, techniques, and models available to researchers. Therefore there is a strong need for model simplification able to appropriate model complexity and still represent the processes. We have developed a new model so-called Westpa-Pest to improve water quality management of an agricultural catchment located in the highlands of Colombia. Westpa-Pest is based on the fully distributed hydrologic model Wetspa and a fate pesticide module. We have applied a multi-criteria analysis for model selection under the conditions and data availability found in the region and compared with the new developed Westpa-Pest model. Furthermore, both models were empirically calibrated and validated. The following questions were addressed i) what are the strengths and weaknesses of the models?, ii) which are the most sensitive parameters of each model?, iii) what happens with uncertainties in soil parameters?, and iv) how sensitive are the transfer coefficients?

  1. Fuzzy model predictive control algorithm applied in nuclear power plant

    International Nuclear Information System (INIS)

    Zuheir, Ahmad

    2006-01-01

    The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)

  2. Postfledging survival of Grasshopper Sparrows in grasslands managed with fire and grazing

    Science.gov (United States)

    Hovick, Torre J.; Miller, James R.; Koford, Rolf R.; Engle, David M.; Debinski, Diane M.

    2011-01-01

    More accurate estimates of survival after nestlings fledge are needed for population models to be parameterized and population dynamics to be understood during this vulnerable life stage. The period after fledging is the time when chicks learn to fly, forage, and hide from predators. We monitored postfledging survival, causespecific mortality, and movements of Grasshopper Sparrows (Ammodramus savannarum) in grassland managed with fire and grazing. In 2009, we attached radio transmitters to 50 nestlings from 50 different broods and modeled their survival in response to climatic, biological, and ecological variables. There was no effect of treatment on survival. The factor most influencing postfledging survival was age; no other variable was significant. The majority of chicks (74%) died within 3 days of radio-transmitter attachment. We attributed most mortality to mesopredators (48%) and exposure (28%). Fledglings' movements increased rapidly for the first 4 days after they left the nest and were relatively stable for the remaining 10 days we tracked them. On average, fledglings took flight for the first time 4 days after fledging and flew ≥10 m 9 days after fledging. Our data show that the Grasshopper Sparrow's survival rates may be less than most models relying on nest-success estimates predict, and we emphasize the importance of incorporating estimates of survival during the postfledging period in demographic models.

  3. Breast cancer data analysis for survivability studies and prediction.

    Science.gov (United States)

    Shukla, Nagesh; Hagenbuchner, Markus; Win, Khin Than; Yang, Jack

    2018-03-01

    Breast cancer is the most common cancer affecting females worldwide. Breast cancer survivability prediction is challenging and a complex research task. Existing approaches engage statistical methods or supervised machine learning to assess/predict the survival prospects of patients. The main objectives of this paper is to develop a robust data analytical model which can assist in (i) a better understanding of breast cancer survivability in presence of missing data, (ii) providing better insights into factors associated with patient survivability, and (iii) establishing cohorts of patients that share similar properties. Unsupervised data mining methods viz. the self-organising map (SOM) and density-based spatial clustering of applications with noise (DBSCAN) is used to create patient cohort clusters. These clusters, with associated patterns, were used to train multilayer perceptron (MLP) model for improved patient survivability analysis. A large dataset available from SEER program is used in this study to identify patterns associated with the survivability of breast cancer patients. Information gain was computed for the purpose of variable selection. All of these methods are data-driven and require little (if any) input from users or experts. SOM consolidated patients into cohorts of patients with similar properties. From this, DBSCAN identified and extracted nine cohorts (clusters). It is found that patients in each of the nine clusters have different survivability time. The separation of patients into clusters improved the overall survival prediction accuracy based on MLP and revealed intricate conditions that affect the accuracy of a prediction. A new, entirely data driven approach based on unsupervised learning methods improves understanding and helps identify patterns associated with the survivability of patient. The results of the analysis can be used to segment the historical patient data into clusters or subsets, which share common variable values and

  4. Drawing Nomograms with R: applications to categorical outcome and survival data.

    Science.gov (United States)

    Zhang, Zhongheng; Kattan, Michael W

    2017-05-01

    Outcome prediction is a major task in clinical medicine. The standard approach to this work is to collect a variety of predictors and build a model of appropriate type. The model is a mathematical equation that connects the outcome of interest with the predictors. A new patient with given clinical characteristics can be predicted for outcome with this model. However, the equation describing the relationship between predictors and outcome is often complex and the computation requires software for practical use. There is another method called nomogram which is a graphical calculating device allowing an approximate graphical computation of a mathematical function. In this article, we describe how to draw nomograms for various outcomes with nomogram() function. Binary outcome is fit by logistic regression model and the outcome of interest is the probability of the event of interest. Ordinal outcome variable is also discussed. Survival analysis can be fit with parametric model to fully describe the distributions of survival time. Statistics such as the median survival time, survival probability up to a specific time point are taken as the outcome of interest.

  5. STI571 (Gleevec) improves tumor growth delay and survival in irradiated mouse models of glioblastoma

    International Nuclear Information System (INIS)

    Geng Ling; Shinohara, Eric T.; Kim, Dong; Tan Jiahuai; Osusky, Kate; Shyr, Yu; Hallahan, Dennis E.

    2006-01-01

    Purpose: Glioblastoma multiforme (GBM) is a devastating brain neoplasm that is essentially incurable. Although radiation therapy prolongs survival, GBMs progress within areas of irradiation. Recent studies in invertebrates have shown that STI571 (Gleevec; Novartis, East Hanover, NJ) enhances the cytotoxicity of ionizing radiation. In the present study, the effectiveness of STI571 in combination with radiation was studied in mouse models of GBM. Methods and Materials: Murine GL261 and human D54 GBM cell lines formed tumors in brains and hind limbs of C57BL6 and nude mice, respectively. GL261 and D54 cells were treated with 5 μmol/L of STI571 for 1 h and/or irradiated with 3 Gy. Protein was analyzed by Western immunoblots probed with antibodies to caspase 3, cleaved caspase 3, phospho-Akt, Akt, and platelet-derived growth factor receptor (PDGFR) α and β. Tumor volumes were assessed in mice bearing GL261 or D54 tumors treated with 21 Gy administered in seven fractionated doses. Histologic sections from STI571-treated mice were stained with phospho-Akt and phospho-PDGFR β antibodies. Kaplan-Meier survival curves were used to study the response of mice bearing intracranial implants of GL261. Results: STI571 penetrated the blood-brain barrier, which resulted in a reduction in phospho-PDGFR in GBM. STI571-induced apoptosis in GBM was significantly enhanced by irradiation. STI571 combined with irradiation induced caspase 3 cleavage in GBM cells. Glioblastoma multiforme response to therapy correlated with an increase in tumor growth delay and survival when STI571 was administered in conjunction with daily irradiation. Conclusion: These findings suggest that STI571 has the potential to augment radiotherapy and thereby improve median survival

  6. Nutritional intra-amniotic therapy increases survival in a rabbit model of fetal growth restriction

    Science.gov (United States)

    Illa, Miriam; Pla, Laura; Zamora, Monica; Crispi, Fatima; Gratacos, Eduard

    2018-01-01

    Objective To evaluate the perinatal effects of a prenatal therapy based on intra-amniotic nutritional supplementation in a rabbit model of intrauterine growth restriction (IUGR). Methods IUGR was surgically induced in pregnant rabbits at gestational day 25 by ligating 40–50% of uteroplacental vessels of each gestational sac. At the same time, modified-parenteral nutrition solution (containing glucose, amino acids and electrolytes) was injected into the amniotic sac of nearly half of the IUGR fetuses (IUGR-T group n = 106), whereas sham injections were performed in the rest of fetuses (IUGR group n = 118). A control group without IUGR induction but sham injection was also included (n = 115). Five days after the ligation procedure, a cesarean section was performed to evaluate fetal cardiac function, survival and birth weight. Results Survival was significantly improved in the IUGR fetuses that were treated with intra-amniotic nutritional supplementation as compared to non-treated IUGR animals (survival rate: controls 71% vs. IUGR 44% p = 0.003 and IUGR-T 63% vs. IUGR 44% p = 0.02), whereas, birth weight (controls mean 43g ± SD 9 vs. IUGR 36g ± SD 9 vs. IUGR-T 35g ± SD 8, p = 0.001) and fetal cardiac function were similar among the IUGR groups. Conclusion Intra-amniotic injection of a modified-parenteral nutrient solution appears to be a promising therapy for reducing mortality among IUGR. These results provide an opportunity to develop new intra-amniotic nutritional strategies to reach the fetus by bypassing the placental insufficiency. PMID:29466434

  7. Positive end-expiratory pressure improves survival in a rodent model of cardiopulmonary resuscitation using high-dose epinephrine.

    LENUS (Irish Health Repository)

    McCaul, Conán

    2009-10-01

    Multiple interventions have been tested in models of cardiopulmonary resuscitation (CPR) to optimize drug use, chest compressions, and ventilation. None has studied the effects of positive end-expiratory pressure (PEEP) on outcome. We hypothesized that because PEEP can reverse pulmonary atelectasis, lower pulmonary vascular resistance, and potentially improve cardiac output, its use during CPR would increase survival.

  8. Survival of Saplings in Recovery of Riparian Vegetation of Pandeiros River (MG

    Directory of Open Access Journals (Sweden)

    Nathalle Cristine Alencar Fagundes

    2018-02-01

    Full Text Available ABSTRACT This study monitored the survival of saplings planted according to different recovery models in a riparian forest of the Pandeiros river (Januária, MG. The models consisted of planting the saplings in lines of 2 or 4 m with presence (T2S and T4S, respectively or absence of direct seeding (T2 and T4, respectively. We planted 16,259 saplings of 17 botanical families, 32 genera and 33 species. The saplings, in general, presented a survival rate after one year of 34.4% (±1.8. The species with highest survival rates were Jacaranda brasiliana, with 85.0% (±13.5 of survival, Anadenanthera colubrina, with 70.1% (±7.0, and Triplaris gardneriana, with 69.3% (±9.1. Survival did not vary between the models tested, probably due to the short evaluation period (12 months.

  9. Survival prognosis in plantations of Pinus caribaea Morelet var. caribaea Barrett & Golfari

    Directory of Open Access Journals (Sweden)

    Ouorou Ganni Mariel Guera

    2018-01-01

    Full Text Available The present study was carried out with the objective of obtaining regression equations and Artificial Neural Networks (ANNs for the prognosis of Pinus caribaea var. caribaea survival in Macurije Forest Company, province of Pinar del Río - Cuba. The data used in the modeling comes from the measurement of the variables age (years and survival (density in circular permanent plots of 500 m² established in P. caribaea var. caribaea plantations. The study was divided into three stages: i Adjustment of survival traditional regression models; ii Training of ANNs for survival prognosis, including categorical variables «site» and «Basic Units of Forest Production»; iii Comparison of regression equations performance with those of ANNs in survival prognosis. The best models and ANNs were selected based on: adjusted determination coefficient - R2aj (%, square root of the mean square error - RMSE (% and residue distribution analysis. The evaluation of the models goodness of fit also included the verification of the assumptions of normality, homocedasticity and absence of serial autocorrelation in the residues by Kolmogorov-Smirnov, White and Durbin-Watson tests, respectively. The model of Pienaar and Shiver (1981 turned out to be the best fit in survival prognosis. The ANN MLP 13-10-1 was the one with the best generalization capacity and presented a performance similar to that of Pienaar and Shiver equation.

  10. Advanced age negatively impacts survival in an experimental brain tumor model.

    Science.gov (United States)

    Ladomersky, Erik; Zhai, Lijie; Gritsina, Galina; Genet, Matthew; Lauing, Kristen L; Wu, Meijing; James, C David; Wainwright, Derek A

    2016-09-06

    Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with an average age of 64 years at the time of diagnosis. To study GBM, a number of mouse brain tumor models have been utilized. In these animal models, subjects tend to range from 6 to 12 weeks of age, which is analogous to that of a human teenager. Here, we examined the impact of age on host immunity and the gene expression associated with immune evasion in immunocompetent mice engrafted with syngeneic intracranial GL261. The data indicate that, in mice with brain tumors, youth conveys an advantage to survival. While age did not affect the tumor-infiltrating T cell phenotype or quantity, we discovered that old mice express higher levels of the immunoevasion enzyme, IDO1, which was decreased by the presence of brain tumor. Interestingly, other genes associated with promoting immunosuppression including CTLA-4, PD-L1 and FoxP3, were unaffected by age. These data highlight the possibility that IDO1 contributes to faster GBM outgrowth with advanced age, providing rationale for future investigation into immunotherapeutic targeting in the future. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  11. Perioperative blood transfusion does not decrease survival after surgical treatment of spinal metastases

    DEFF Research Database (Denmark)

    Clausen, Caroline; Lönn, Lars; Morgen, Søren Schmidt

    2014-01-01

    PURPOSE: To assess whether perioperative allogenic blood transfusions in patients undergoing surgical treatment for spinal metastases independently influence patient survival. METHODS: A retrospective study including 170 consecutive patients undergoing surgical treatment for spinal metastases in ...... 12-month survival. Future studies should assess if a liberal transfusion regime can be applied to this group of patients; thereby, prioritizing early postoperative mobilization....

  12. A retrospective analysis of the results of p(65) + Be neutron therapy for the treatment of prostate adenocarcinoma at the cyclotron of Louvain-la-Neuve. Part I: survival and progression-free survival

    International Nuclear Information System (INIS)

    Scalliet, P.G.M.; Remouchamps, V.; Wambersie, A.; Richard, F.; Lhoas, F.; Van Glabbeke, M.; Curran, D.; Van Cangh, P.; Ledent, T.

    2001-01-01

    Purpose. -To retrospectively evaluate survival, progression free survival (PFS) and biological response in a series of patients irradiated with mixed neutron/photon beams for locally advanced prostate cancer in our institution. Patients and methods. - Three hundred and eight patients were treated between January 1990 and December 1996. Fifty-five of these were recruited for pT3 or pN1 tumors after radical prostatectomy. Neo-adjuvant androgen deprivation was given in 106 patients. The treatment protocol consisted of a mixed photon/neutron irradiation in a two-to-three proportion, up to a total equivalent dose of 66 Gy (assuming a clinical RBE value of 2.8). Pre- and post-treatment PSA determinations were available in practically all cases. Study endpoints were overall survival (OAS) and progression-free survival (PFS). The Cox proportional hazard regression model was used to investigate the prognostic value of baseline characteristics on survival and progression-free survival were a progression was defined as local, regional, metastatic or biological progression. Mean age was 69 years (49-86); mean pretreatment PSA was 15 (0.5-330) in all patients and 14 (0.5-160) in those receiving neo-adjuvant hormonotherapy; seven patients only had an initial PSA S 4 ng/mL; 15% were T1, 46% were T2, 28% were T3 or pT3 and 4% were T4 (7% unspecified); WHO grade of differentiation was I in 38%, II in 38% and III in 14% (5% unspecified). Results. -The median follow-up was 2.8 years (0-7.8). Five year overall survival (OAS) was 79% (95% CI: 71-87%) and 5-year progression-free survival (PFS) was 64% (95% Cl: 54-74%) for the entire series. PFS in patients with an initial PSA >- 20 ng/mL was the same. PFS could be predicted by two optimal Cox regression models, one including histological grade (p = 0.003) and initial PSA (p = 0.0009) as cofactors, the other including histological grade (p = 0.003) and T stage (p = 0.02). The main prognostic factors for overall survival were PSA and age

  13. Sex differences in lung cancer survival: long-term trends using population-based cancer registry data in Osaka, Japan.

    Science.gov (United States)

    Kinoshita, Fukuaki Lee; Ito, Yuri; Morishima, Toshitaka; Miyashiro, Isao; Nakayama, Tomio

    2017-09-01

    Several studies of sex differences in lung cancer survival have been reported. However, large-size population-based studies based on long-term observation are scarce. We investigated long-term trends in sex differences in lung cancer survival using population-based cancer registry data from Osaka, Japan. We analyzed 79 330 cases from the Osaka Cancer Registry (OCR) diagnosed between 1975 and 2007. We calculated 5-year relative survival in the six periods (1975-1980, 1981-1986, 1987-1992, 1993-1997, 1998-2002 and 2003-2007). To estimate the trends in sex differences in lung cancer survival throughout the study period, we applied a multivariate excess hazard model to control for confounders. The proportion of adenocarcinoma (ADC) and 5-year relative relative survival have increased for both sexes. Sex differences in lung cancer survival have widened over the period, especially in ADC and since the late 1990s. The excess hazard ratio of death within 5 years for males was 1.19 (95% CI: 1.16-1.21), adjusting for period at diagnosis, histologic type, stage, age group and treatment. We reported that females have better prognosis in lung cancer than males and the sex differences in lung cancer survival have become wider in Osaka, Japan. This can be partly explained by the sex differences in the proportions of histologic type and stage. Further studies considering other factors that influence sex differences in lung cancer survival are needed. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

  14. Coordination Motor Skills of Military Pilots Subjected to Survival Training.

    Science.gov (United States)

    Tomczak, Andrzej

    2015-09-01

    Survival training of military pilots in the Polish Army gains significance because polish pilots have taken part in more and more military missions. Prolonged exercise of moderate intensity with restricted sleep or sleep deprivation is known to deteriorate performance. The aim of the study was thus to determine the effects of a strenuous 36-hour exercise with restricted sleep on selected motor coordination and psychomotor indices. Thirteen military pilots aged 30-56 years were examined twice: pretraining and posttraining. The following tests were applied: running motor adjustment (15-m sprint, 3 × 5-m shuttle run, 15-m slalom, and 15-m squat), divided attention, dynamic body balance, handgrip strength differentiation. Survival training resulted in significant decreases in maximum handgrip strength (from 672 to 630 N), corrected 50% max handgrip (from 427 to 367 N), error 50% max (from 26 to 17%), 15-m sprint (from 5.01 to 4.64 m·s), and 15-m squat (2.20 to 1.98 m·s). The training improvements took place in divided attention test (from 48.2 to 57.2%). The survival training applied to pilots only moderately affected some of their motor adjustment skills, the divided attention, and dynamic body balance remaining unaffected or even improved. Further studies aimed at designing a set of tests for coordination motor skills and of soldiers' capacity to fight for survival under conditions of isolation are needed.

  15. Survival and its predictors from age 75 to 85 in men and women belonging to cohorts with marked survival differences to age 75

    DEFF Research Database (Denmark)

    Heikkinen, E; Kauppinen, M; Schroll, M

    2016-01-01

    focusing on different domains of health, functional capacity, and physical and social activities. RESULTS: The proportion of survivors to age 75 was markedly smaller among the Finnish men and women than Danish or Swedish subjects. In the local population no marked differences in survival from age 75 to 85...... among three local Nordic populations using survival data on national cohorts as background information. METHODS: The data were derived from national registers and from samples of 75-year old living in Denmark, Sweden, and Finland. The subjects were invited to take part in interviews and examinations...... were observed between the groups of men, while women survived longer than men and longer in Göteborg than in Glostrup or Jyväskylä. Univariate models revealed 12 predictors of survival. In the multivariate models, the significant predictors among men related to physical fitness, whereas among women...

  16. Communication Efficacy and Couples’ Cancer Management: Applying a Dyadic Appraisal Model

    OpenAIRE

    Magsamen-Conrad, Kate; Checton, Maria G.; Venetis, Maria K.; Greene, Kathryn

    2014-01-01

    The purpose of the present study was to apply Berg and Upchurch’s (2007) developmental-conceptual model to understand better how couples cope with cancer. Specifically, we hypothesized a dyadic appraisal model in which proximal factors (relational quality), dyadic appraisal (prognosis uncertainty), and dyadic coping (communication efficacy) predicted adjustment (cancer management). The study was cross-sectional and included 83 dyads in which one partner had been diagnosed with and/or treated ...

  17. The asymmetric rotator model applied to odd-mass iridium isotopes

    International Nuclear Information System (INIS)

    Piepenbring, R.

    1980-04-01

    The method of inversion of the eigenvalue problem previously developed for nuclei with axial symmetry is extended to asymmetric equilibrium shapes. This new approach of the asymmetric rotator model is applied to the odd-mass iridium isotopes. A satisfactory and coherent description of the observed energy spectra is obtained, especially for the lighter isotopes

  18. Memory and survival after microbeam radiation therapy

    International Nuclear Information System (INIS)

    Schueltke, Elisabeth; Juurlink, Bernhard H.J.; Ataelmannan, Khalid; Laissue, Jean; Blattmann, Hans; Braeuer-Krisch, Elke; Bravin, Alberto; Minczewska, Joanna; Crosbie, Jeffrey; Taherian, Hadi; Frangou, Evan; Wysokinsky, Tomasz; Chapman, L. Dean; Griebel, Robert; Fourney, Daryl

    2008-01-01

    Background: Disturbances of memory function are frequently observed in patients with malignant brain tumours and as adverse effects after radiotherapy to the brain. Experiments in small animal models of malignant brain tumour using synchrotron-based microbeam radiation therapy (MRT) have shown a promising prolongation of survival times. Materials and methods: Two animal models of malignant brain tumour were used to study survival and memory development after MRT. Thirteen days after implantation of tumour cells, animals were submitted to MRT either with or without adjuvant therapy (buthionine-SR-sulfoximine = BSO or glutamine). We used two orthogonal 1-cm wide arrays of 50 microplanar quasiparallel microbeams of 25 μm width and a center-to-center distance of about 200 μm, created by a multislit collimator, with a skin entrance dose of 350 Gy for each direction. Object recognition tests were performed at day 13 after tumour cell implantation and in monthly intervals up to 1 year after tumour cell implantation. Results: In both animal models, MRT with and without adjuvant therapy significantly increased survival times. BSO had detrimental effects on memory function early after therapy, while administration of glutamine resulted in improved memory

  19. Quantifying the changes in survival inequality for Indigenous people diagnosed with cancer in Queensland, Australia.

    Science.gov (United States)

    Baade, Peter D; Dasgupta, Paramita; Dickman, Paul W; Cramb, Susanna; Williamson, John D; Condon, John R; Garvey, Gail

    2016-08-01

    The survival inequality faced by Indigenous Australians after a cancer diagnosis is well documented; what is less understood is whether this inequality has changed over time and what this means in terms of the impact a cancer diagnosis has on Indigenous people. Survival information for all patients identified as either Indigenous (n=3168) or non-Indigenous (n=211,615) and diagnosed in Queensland between 1997 and 2012 were obtained from the Queensland Cancer Registry, with mortality followed up to 31st December, 2013. Flexible parametric survival models were used to quantify changes in the cause-specific survival inequalities and the number of lives that might be saved if these inequalities were removed. Among Indigenous cancer patients, the 5-year cause-specific survival (adjusted by age, sex and broad cancer type) increased from 52.9% in 1997-2006 to 58.6% in 2007-2012, while it improved from 61.0% to 64.9% among non-Indigenous patients. This meant that the adjusted 5-year comparative survival ratio (Indigenous: non-Indigenous) increased from 0.87 [0.83-0.88] to 0.89 [0.87-0.93], with similar improvements in the 1-year comparative survival. Using a simulated cohort corresponding to the number and age-distribution of Indigenous people diagnosed with cancer in Queensland each year (n=300), based on the 1997-2006 cohort mortality rates, 35 of the 170 deaths due to cancer (21%) expected within five years of diagnosis were due to the Indigenous: non-Indigenous survival inequality. This percentage was similar when applying 2007-2012 cohort mortality rates (19%; 27 out of 140 deaths). Indigenous people diagnosed with cancer still face a poorer survival outlook than their non-Indigenous counterparts, particularly in the first year after diagnosis. The improving survival outcomes among both Indigenous and non-Indigenous cancer patients, and the decreasing absolute impact of the Indigenous survival disadvantage, should provide increased motivation to continue and enhance

  20. Survival of the insulator under the electrical stress condition at cryogenic temperature

    Energy Technology Data Exchange (ETDEWEB)

    Baek, Seung Myeong [Dept. of Fire Protection Engineering, Changwon Moonsung University, Changwon (Korea, Republic of); Kim, Sang Hyun [Dept. of Electrical Engineering, Gyeongsang National University, Jinju (Korea, Republic of)

    2013-12-15

    We have clearly investigated with respect to the survival of the insulator at cryogenic temperature under the electrical stress. The breakdown and voltage-time characteristics of turn-to-turn models for point contact geometry and surface contact geometry using copper multi wrapped with polyimide film for an HTS transformer were investigated under AC and impulse voltage at 77 K. Polyimide film (Kapton) 0.025 mm thick is used for multi wrapping of the electrode. As expected, the breakdown voltages for the surface contact geometry are lower than that of the point contact geometry, because the contact area of the surface contact geometry is lager than that of the point contact geometry. The time to breakdown t50 decreases as the applied voltage is increased, and the lifetime indices increase slightly as the number of layers is increased. The electric field amplitude at the position where breakdown occurs is about 80% of the maximum electric field value. The relationship between survival probability and the electrical stress at cryogenic temperature was evident.

  1. The survival of Coxiella burnetii in soils

    Science.gov (United States)

    Evstigneeva, A. S.; Ul'Yanova, T. Yu.; Tarasevich, I. V.

    2007-05-01

    Coxiella burnetii is a pathogen of Q-fever—a widespread zoonosis. The effective adaptation of C. burnetii to intracellular existence is in contrast with its ability to survive in the environment outside the host cells and its resistance to chemical and physical agents. Its mechanism of survival remains unknown. However, its survival appears to be related to the developmental cycle of the microorganism itself, i.e., to the formation of its dormant forms. The survival of Coxiella burnetii was studied for the first time. The pathogenic microorganism was inoculated into different types of soil and cultivated under different temperatures. The survival of the pathogen was verified using a model with laboratory animals (mice). Viable C. burnetii were found in the soil even 20 days after their inoculation. The relationship between the organic carbon content in the soils and the survival of C. burnetii was revealed. Thus, the results obtained were the first to demonstrate that the soil may serve as a reservoir for the preservation and further spreading of the Q-fever pathogen in the environment, on the one hand, and reduce the risk of epidemics, on the other.

  2. Using Survival Analysis to Evaluate Medical Equipment Battery Life.

    Science.gov (United States)

    Kuhajda, David

    2016-01-01

    As hospital medical device managers obtain more data, opportunities exist for using the data to improve medical device management, enhance patient safety, and evaluate costs of decisions. As a demonstration of the ability to use data analytics, this article applies survival analysis statistical techniques to assist in making decisions on medical equipment maintenance. The analysis was performed on a large amount of data related to failures of an infusion pump manufacturer's lithium battery and two aftermarket replacement lithium batteries from one hospital facility. The survival analysis resulted in statistical evidence showing that one of the third-party batteries had a lower survival curve than the infusion pump manufacturer's battery. This lower survival curve translates to a shorter expected life before replacement is needed. The data suggested that to limit unexpected failures, replacing batteries at a two-year interval, rather than the current industry recommendation of three years, may be warranted. For less than $5,400 in additional annual cost, the risk of unexpected battery failures can be reduced from an estimated 28% to an estimated 7%.

  3. Review and evaluation of performance measures for survival prediction models in external validation settings

    Directory of Open Access Journals (Sweden)

    M. Shafiqur Rahman

    2017-04-01

    Full Text Available Abstract Background When developing a prediction model for survival data it is essential to validate its performance in external validation settings using appropriate performance measures. Although a number of such measures have been proposed, there is only limited guidance regarding their use in the context of model validation. This paper reviewed and evaluated a wide range of performance measures to provide some guidelines for their use in practice. Methods An extensive simulation study based on two clinical datasets was conducted to investigate the performance of the measures in external validation settings. Measures were selected from categories that assess the overall performance, discrimination and calibration of a survival prediction model. Some of these have been modified to allow their use with validation data, and a case study is provided to describe how these measures can be estimated in practice. The measures were evaluated with respect to their robustness to censoring and ease of interpretation. All measures are implemented, or are straightforward to implement, in statistical software. Results Most of the performance measures were reasonably robust to moderate levels of censoring. One exception was Harrell’s concordance measure which tended to increase as censoring increased. Conclusions We recommend that Uno’s concordance measure is used to quantify concordance when there are moderate levels of censoring. Alternatively, Gönen and Heller’s measure could be considered, especially if censoring is very high, but we suggest that the prediction model is re-calibrated first. We also recommend that Royston’s D is routinely reported to assess discrimination since it has an appealing interpretation. The calibration slope is useful for both internal and external validation settings and recommended to report routinely. Our recommendation would be to use any of the predictive accuracy measures and provide the corresponding predictive

  4. Flux balance modeling to predict bacterial survival during pulsed-activity events

    Science.gov (United States)

    Jose, Nicholas A.; Lau, Rebecca; Swenson, Tami L.; Klitgord, Niels; Garcia-Pichel, Ferran; Bowen, Benjamin P.; Baran, Richard; Northen, Trent R.

    2018-04-01

    Desert biological soil crusts (BSCs) are cyanobacteria-dominated surface soil microbial communities common to plant interspaces in arid environments. The capability to significantly dampen their metabolism allows them to exist for extended periods in a desiccated dormant state that is highly robust to environmental stresses. However, within minutes of wetting, metabolic functions reboot, maximizing activity during infrequent permissive periods. Microcoleus vaginatus, a primary producer within the crust ecosystem and an early colonizer, initiates crust formation by binding particles in the upper layer of soil via exopolysaccharides, making microbial dominated biological soil crusts highly dependent on the viability of this organism. Previous studies have suggested that biopolymers play a central role in the survival of this organism by powering resuscitation, rapidly forming compatible solutes, and fueling metabolic activity in dark, hydrated conditions. To elucidate the mechanism of this phenomenon and provide a basis for future modeling of BSCs, we developed a manually curated, genome-scale metabolic model of Microcoleus vaginatus (iNJ1153). To validate this model, gas chromatography-mass spectroscopy (GC-MS) and liquid chromatography-mass spectroscopy (LC-MS) were used to characterize the rate of biopolymer accumulation and depletion in in hydrated Microcoleus vaginatus under light and dark conditions. Constraint-based flux balance analysis showed agreement between model predictions and experimental reaction fluxes. A significant amount of consumed carbon and light energy is invested into storage molecules glycogen and polyphosphate, while β-polyhydroxybutyrate may function as a secondary resource. Pseudo-steady-state modeling suggests that glycogen, the primary carbon source with the fastest depletion rate, will be exhausted if M. vaginatus experiences dark wetting events 4 times longer than light wetting events.

  5. Model output statistics applied to wind power prediction

    Energy Technology Data Exchange (ETDEWEB)

    Joensen, A; Giebel, G; Landberg, L [Risoe National Lab., Roskilde (Denmark); Madsen, H; Nielsen, H A [The Technical Univ. of Denmark, Dept. of Mathematical Modelling, Lyngby (Denmark)

    1999-03-01

    Being able to predict the output of a wind farm online for a day or two in advance has significant advantages for utilities, such as better possibility to schedule fossil fuelled power plants and a better position on electricity spot markets. In this paper prediction methods based on Numerical Weather Prediction (NWP) models are considered. The spatial resolution used in NWP models implies that these predictions are not valid locally at a specific wind farm. Furthermore, due to the non-stationary nature and complexity of the processes in the atmosphere, and occasional changes of NWP models, the deviation between the predicted and the measured wind will be time dependent. If observational data is available, and if the deviation between the predictions and the observations exhibits systematic behavior, this should be corrected for; if statistical methods are used, this approaches is usually referred to as MOS (Model Output Statistics). The influence of atmospheric turbulence intensity, topography, prediction horizon length and auto-correlation of wind speed and power is considered, and to take the time-variations into account, adaptive estimation methods are applied. Three estimation techniques are considered and compared, Extended Kalman Filtering, recursive least squares and a new modified recursive least squares algorithm. (au) EU-JOULE-3. 11 refs.

  6. Methodology for Applying Cyber Security Risk Evaluation from BN Model to PSA Model

    Energy Technology Data Exchange (ETDEWEB)

    Shin, Jin Soo; Heo, Gyun Young [Kyung Hee University, Youngin (Korea, Republic of); Kang, Hyun Gook [KAIST, Dajeon (Korea, Republic of); Son, Han Seong [Joongbu University, Chubu (Korea, Republic of)

    2014-08-15

    There are several advantages to use digital equipment such as cost, convenience, and availability. It is inevitable to use the digital I and C equipment replaced analog. Nuclear facilities have already started applying the digital system to I and C system. However, the nuclear facilities also have to change I and C system even though it is difficult to use digital equipment due to high level of safety, irradiation embrittlement, and cyber security. A cyber security which is one of important concerns to use digital equipment can affect the whole integrity of nuclear facilities. For instance, cyber-attack occurred to nuclear facilities such as the SQL slammer worm, stuxnet, DUQU, and flame. The regulatory authorities have published many regulatory requirement documents such as U.S. NRC Regulatory Guide 5.71, 1.152, IAEA guide NSS-17, IEEE Standard, and KINS Regulatory Guide. One of the important problem of cyber security research for nuclear facilities is difficulty to obtain the data through the penetration experiments. Therefore, we make cyber security risk evaluation model with Bayesian network (BN) for nuclear reactor protection system (RPS), which is one of the safety-critical systems to trip the reactor when the accident is happened to the facilities. BN can be used for overcoming these problems. We propose a method to apply BN cyber security model to probabilistic safety assessment (PSA) model, which had been used for safety assessment of system, structure and components of facility. The proposed method will be able to provide the insight of safety as well as cyber risk to the facility.

  7. Methodology for Applying Cyber Security Risk Evaluation from BN Model to PSA Model

    International Nuclear Information System (INIS)

    Shin, Jin Soo; Heo, Gyun Young; Kang, Hyun Gook; Son, Han Seong

    2014-01-01

    There are several advantages to use digital equipment such as cost, convenience, and availability. It is inevitable to use the digital I and C equipment replaced analog. Nuclear facilities have already started applying the digital system to I and C system. However, the nuclear facilities also have to change I and C system even though it is difficult to use digital equipment due to high level of safety, irradiation embrittlement, and cyber security. A cyber security which is one of important concerns to use digital equipment can affect the whole integrity of nuclear facilities. For instance, cyber-attack occurred to nuclear facilities such as the SQL slammer worm, stuxnet, DUQU, and flame. The regulatory authorities have published many regulatory requirement documents such as U.S. NRC Regulatory Guide 5.71, 1.152, IAEA guide NSS-17, IEEE Standard, and KINS Regulatory Guide. One of the important problem of cyber security research for nuclear facilities is difficulty to obtain the data through the penetration experiments. Therefore, we make cyber security risk evaluation model with Bayesian network (BN) for nuclear reactor protection system (RPS), which is one of the safety-critical systems to trip the reactor when the accident is happened to the facilities. BN can be used for overcoming these problems. We propose a method to apply BN cyber security model to probabilistic safety assessment (PSA) model, which had been used for safety assessment of system, structure and components of facility. The proposed method will be able to provide the insight of safety as well as cyber risk to the facility

  8. Network survivability performance

    Science.gov (United States)

    1993-11-01

    This technical report has been developed to address the survivability of telecommunications networks including services. It responds to the need for a common understanding of, and assessment techniques for network survivability, availability, integrity, and reliability. It provides a basis for designing and operating telecommunications networks to user expectations for network survivability and a foundation for continuing industry activities in the subject area. This report focuses on the survivability of both public and private networks and covers a wide range of users. Two frameworks are established for quantifying and categorizing service outages, and for classifying network survivability techniques and measures. The performance of the network survivability techniques is considered; however, recommended objectives are not established for network survivability performance.

  9. Complexity for survival of livings

    Energy Technology Data Exchange (ETDEWEB)

    Zak, Michail [Jet Propulsion Laboratory, California Institute of Technology, Advance Computing Algorithms and IVHM Group, Pasadena, CA 91109 (United States)]. E-mail: Michail.Zak@jpl.nasa.gov

    2007-05-15

    A connection between survivability of livings and complexity of their behavior is established. New physical paradigms-exchange of information via reflections, and chain of abstractions-explaining and describing progressive evolution of complexity in living (active) systems are introduced. A biological origin of these paradigms is associated with a recently discovered mirror neuron that is able to learn by imitation. As a result, an active element possesses the self-nonself images and interacts with them creating the world of mental dynamics. Three fundamental types of complexity of mental dynamics that contribute to survivability are identified. Mathematical model of the corresponding active systems is described by coupled motor-mental dynamics represented by Langevin and Fokker-Planck equations, respectively, while the progressive evolution of complexity is provided by nonlinear evolution of probability density. Application of the proposed formalism to modeling common-sense-based decision-making process is discussed.

  10. Complexity for survival of livings

    International Nuclear Information System (INIS)

    Zak, Michail

    2007-01-01

    A connection between survivability of livings and complexity of their behavior is established. New physical paradigms-exchange of information via reflections, and chain of abstractions-explaining and describing progressive evolution of complexity in living (active) systems are introduced. A biological origin of these paradigms is associated with a recently discovered mirror neuron that is able to learn by imitation. As a result, an active element possesses the self-nonself images and interacts with them creating the world of mental dynamics. Three fundamental types of complexity of mental dynamics that contribute to survivability are identified. Mathematical model of the corresponding active systems is described by coupled motor-mental dynamics represented by Langevin and Fokker-Planck equations, respectively, while the progressive evolution of complexity is provided by nonlinear evolution of probability density. Application of the proposed formalism to modeling common-sense-based decision-making process is discussed

  11. Fuzzy uncertainty modeling applied to AP1000 nuclear power plant LOCA

    International Nuclear Information System (INIS)

    Ferreira Guimaraes, Antonio Cesar; Franklin Lapa, Celso Marcelo; Lamego Simoes Filho, Francisco Fernando; Cabral, Denise Cunha

    2011-01-01

    Research highlights: → This article presents an uncertainty modelling study using a fuzzy approach. → The AP1000 Westinghouse NPP was used and it is provided of passive safety systems. → The use of advanced passive safety systems in NPP has limited operational experience. → Failure rates and basic events probabilities used on the fault tree analysis. → Fuzzy uncertainty approach was employed to reliability of the AP1000 large LOCA. - Abstract: This article presents an uncertainty modeling study using a fuzzy approach applied to the Westinghouse advanced nuclear reactor. The AP1000 Westinghouse Nuclear Power Plant (NPP) is provided of passive safety systems, based on thermo physics phenomenon, that require no operating actions, soon after an incident has been detected. The use of advanced passive safety systems in NPP has limited operational experience. As it occurs in any reliability study, statistically non-significant events report introduces a significant uncertainty level about the failure rates and basic events probabilities used on the fault tree analysis (FTA). In order to model this uncertainty, a fuzzy approach was employed to reliability analysis of the AP1000 large break Loss of Coolant Accident (LOCA). The final results have revealed that the proposed approach may be successfully applied to modeling of uncertainties in safety studies.

  12. Novel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study.

    Science.gov (United States)

    Kim, Hwi Young; Lee, Dong Hyeon; Lee, Jeong-Hoon; Cho, Young Youn; Cho, Eun Ju; Yu, Su Jong; Kim, Yoon Jun; Yoon, Jung-Hwan

    2018-03-20

    Prediction of the outcome of sorafenib therapy using biomarkers is an unmet clinical need in patients with advanced hepatocellular carcinoma (HCC). The aim was to develop and validate a biomarker-based model for predicting sorafenib response and overall survival (OS). This prospective cohort study included 124 consecutive HCC patients (44 with disease control, 80 with progression) with Child-Pugh class A liver function, who received sorafenib. Potential serum biomarkers (namely, hepatocyte growth factor [HGF], fibroblast growth factor [FGF], vascular endothelial growth factor receptor-1, CD117, and angiopoietin-2) were tested. After identifying independent predictors of tumor response, a risk scoring system for predicting OS was developed and 3-fold internal validation was conducted. A risk scoring system was developed with six covariates: etiology, platelet count, Barcelona Clinic Liver Cancer stage, protein induced by vitamin K absence-II, HGF, and FGF. When patients were stratified into low-risk (score ≤ 5), intermediate-risk (score 6), and high-risk (score ≥ 7) groups, the model provided good discriminant functions on tumor response (concordance [c]-index, 0.884) and 12-month survival (area under the curve [AUC], 0.825). The median OS was 19.0, 11.2, and 6.1 months in the low-, intermediate-, and high-risk group, respectively (P functions on tumor response (c-index, 0.825) and 12-month survival (AUC, 0.803), and good calibration functions (all P > 0.05 between expected and observed values). This new model including serum FGF and HGF showed good performance in predicting the response to sorafenib and survival in patients with advanced HCC.

  13. A Numerical Procedure for Model Identifiability Analysis Applied to Enzyme Kinetics

    DEFF Research Database (Denmark)

    Daele, Timothy, Van; Van Hoey, Stijn; Gernaey, Krist

    2015-01-01

    The proper calibration of models describing enzyme kinetics can be quite challenging. In the literature, different procedures are available to calibrate these enzymatic models in an efficient way. However, in most cases the model structure is already decided on prior to the actual calibration...... and Pronzato (1997) and which can be easily set up for any type of model. In this paper the proposed approach is applied to the forward reaction rate of the enzyme kinetics proposed by Shin and Kim(1998). Structural identifiability analysis showed that no local structural model problems were occurring......) identifiability problems. By using the presented approach it is possible to detect potential identifiability problems and avoid pointless calibration (and experimental!) effort....

  14. Modelling of the process of micromycetus survival in fruit and berry syrups

    Directory of Open Access Journals (Sweden)

    L. Osipova

    2017-06-01

    Full Text Available In order to develop methods for preserving fruit and berry syrup, which exclude the use of high-temperature sterilization and preservatives, the survival of spores of micromycetes (B. nivea molds in model media with different concentration of food osmotically active substances (sucrose, ethyl alcohol, citric acid at a certain concentration of lethal effects on microorganisms. It has been established that model media (juice based syrups from blueberries with a mass content of 4 % and 6 % alcohol, 50 % sucrose, 1 % and 2 % titrated acids, have a lethal effect on spores of B. nivea molds. The regression equation is obtained expressing the dependence of the amount of spores of B. nivea molds on the concentration of sucrose, acid, alcohol and the storage time of syrups. The form of the dependence and direction of the connection between the variables is established – a negative linear regression, which is expressed in the uniform decrease of the function. The estimation of quality of the received regression model is defined. The deviations of the calculated data from the data of the initial set are calculated. The proposed model has sufficient reliability, since the regression function is defined, interpreted and justified, and the estimation of the accuracy of the regression analysis meets the requirements.

  15. Applying a Knowledge Management Modeling Tool for Manufacturing Vision (MV) Development

    DEFF Research Database (Denmark)

    Wang, Chengbo; Luxhøj, James T.; Johansen, John

    2004-01-01

    This paper introduces an empirical application of an experimental model for knowledge management within an organization, namely a case-based reasoning model for manufacturing vision development (CBRM). The model integrates the development process of manufacturing vision with the methodology of case......-based reasoning. This paper briefly describes the model's theoretical fundamentals and its conceptual structure; conducts a detailed introduction of the critical elements within the model; exhibits a real world application of the model; and summarizes the review of the model through academia and practice. Finds...... that the CBRM is supportive to the decision-making process of applying and augmenting organizational knowledge. It provides a new angle to tackle strategic management issues within the manufacturing system of a business operation. Explores a new proposition within strategic manufacturing management by enriching...

  16. Novel bifunctional anthracycline and nitrosourea chemotherapy for human bladder cancer: analysis in a preclinical survival model.

    Science.gov (United States)

    Glaves, D; Murray, M K; Raghavan, D

    1996-08-01

    A hybrid drug [N-2-chloroethylnitrosoureidodaunorubicin (AD312)] that combines structural and functional features of both anthracyclines and nitrosoureas was evaluated in a preclinical survival model of human bladder cancer. To measure the therapeutic activity of AD312, UCRU-BL13 transitional cell carcinoma cells were grown as xenografts in nude mice, and tumor growth rates were compared after i.v. administration of the drug at three dose levels. AD312 treatment at 45 and 60 mg/kg achieved 7-10-fold inhibition of tumor growth and increased host survival by 156 and 249%, respectively. Doses of 60 mg/kg showed optimal therapeutic efficacy, with sustained tumor growth inhibition, an over 2-fold increase in life span, and 40% of mice tumor free ("cured") at 120 days. Tumors were unresponsive to maximum tolerated doses of doxorubicin, a standard anthracycline used as a single agent and in combination therapies for bladder cancer. 1,3-Bis-[2-chloroethyl]-1-nitrosourea was used as a control for the apparently enhanced response of human tumors in murine hosts to nitrosoureas. 1, 3-Bis-[2-chloroethyl]-1-nitrosourea administered in three injections of 20 mg/kg did not cure mice but temporarily inhibited tumor growth by 70% and prolonged survival by 55%; its activity in this model suggests that it may be included in the repertoire of alkylating agents currently used for treatment of bladder cancers. AD312 showed increased antitumor activity with less toxicity than doxorubicin, and its bifunctional properties provide the opportunity for simultaneous treatment of individual cancer cells with two cytotoxic modalities as well as treatment of heterogeneous populations typical of bladder cancers. This novel cytotoxic drug cured doxorubicin-refractory disease and should be investigated for the clinical management of bladder cancer.

  17. Conditionally replicating adenovirus expressing TIMP2 increases survival in a mouse model of disseminated ovarian cancer.

    Directory of Open Access Journals (Sweden)

    Sherry W Yang

    Full Text Available Ovarian cancer remains difficult to treat mainly due to presentation of the disease at an advanced stage. Conditionally-replicating adenoviruses (CRAds are promising anti-cancer agents that selectively kill the tumor cells. The present study evaluated the efficacy of a novel CRAd (Ad5/3-CXCR4-TIMP2 containing the CXCR4 promoter for selective viral replication in cancer cells together with TIMP2 as a therapeutic transgene, targeting the matrix metalloproteases (MMPs in a murine orthotopic model of disseminated ovarian cancer. An orthotopic model of ovarian cancer was established in athymic nude mice by intraperitonal injection of the human ovarian cancer cell line, SKOV3-Luc, expressing luciferase. Upon confirmation of peritoneal dissemination of the cells by non-invasive imaging, mice were randomly divided into four treatment groups: PBS, Ad-ΔE1-TIMP2, Ad5/3-CXCR4, and Ad5/3-CXCR4-TIMP2. All mice were imaged weekly to monitor tumor growth and were sacrificed upon reaching any of the predefined endpoints, including high tumor burden and significant weight loss along with clinical evidence of pain and distress. Survival analysis was performed using the Log-rank test. The median survival for the PBS cohort was 33 days; for Ad-ΔE1-TIMP2, 39 days; for Ad5/3-CXCR4, 52.5 days; and for Ad5/3-CXCR4-TIMP2, 63 days. The TIMP2-armed CRAd delayed tumor growth and significantly increased survival when compared to the unarmed CRAd. This therapeutic effect was confirmed to be mediated through inhibition of MMP9. Results of the in vivo study support the translational potential of Ad5/3-CXCR4-TIMP2 for treatment of human patients with advanced ovarian cancer.

  18. Risk assessment and food allergy: the probabilistic model applied to allergens

    NARCIS (Netherlands)

    Spanjersberg, M.Q.I.; Kruizinga, A.G.; Rennen, M.A.J.; Houben, G.F.

    2007-01-01

    In order to assess the risk of unintended exposure to food allergens, traditional deterministic risk assessment is usually applied, leading to inconsequential conclusions as 'an allergic reaction cannot be excluded'. TNO therefore developed a quantitative risk assessment model for allergens based on

  19. Growth and survival of larval and early juvenile lesser sandeel in patchy prey field in the North Sea: An examination using individual-based modelling

    DEFF Research Database (Denmark)

    Gürkan, Zeren; Christensen, Asbjørn; Deurs, Mikael van

    2012-01-01

    -stages in the North Sea. Simulations of patchiness related starvation mortality are able to explain observed patterns of variation in sandeel growth. Reduced prey densities within patches decrease growth and survival rate of larvae and match–mismatch affect growth and survival of larvae with different hatch time due...... by modeling copepod size spectra dynamics and patchiness based on particle count transects and Continuous Plankton Recorder time series data. The study analyzes the effects of larval hatching time, presence of zooplankton patchiness and within patch abundance on growth and survival of sandeel early life...

  20. Survival and breeding of polar bears in the southern Beaufort Sea in relation to sea ice

    Science.gov (United States)

    Regehr, E.V.; Hunter, C.M.; Caswell, H.; Amstrup, Steven C.; Stirling, I.

    2010-01-01

    1. Observed and predicted declines in Arctic sea ice have raised concerns about marine mammals. In May 2008, the US Fish and Wildlife Service listed polar bears (Ursus maritimus) - one of the most ice-dependent marine mammals - as threatened under the US Endangered Species Act. 2. We evaluated the effects of sea ice conditions on vital rates (survival and breeding probabilities) for polar bears in the southern Beaufort Sea. Although sea ice declines in this and other regions of the polar basin have been among the greatest in the Arctic, to date population-level effects of sea ice loss on polar bears have only been identified in western Hudson Bay, near the southern limit of the species' range. 3. We estimated vital rates using multistate capture-recapture models that classified individuals by sex, age and reproductive category. We used multimodel inference to evaluate a range of statistical models, all of which were structurally based on the polar bear life cycle. We estimated parameters by model averaging, and developed a parametric bootstrap procedure to quantify parameter uncertainty. 4. In the most supported models, polar bear survival declined with an increasing number of days per year that waters over the continental shelf were ice free. In 2001-2003, the ice-free period was relatively short (mean 101 days) and adult female survival was high (0 ∙ 96-0 ∙ 99, depending on reproductive state). In 2004 and 2005, the ice-free period was longer (mean 135 days) and adult female survival was low (0 ∙ 73-0 ∙ 79, depending on reproductive state). Breeding rates and cub litter survival also declined with increasing duration of the ice-free period. Confidence intervals on vital rate estimates were wide. 5. The effects of sea ice loss on polar bears in the southern Beaufort Sea may apply to polar bear populations in other portions of the polar basin that have similar sea ice dynamics and have experienced similar, or more severe, sea ice declines. Our findings

  1. A unified framework for benchmark dose estimation applied to mixed models and model averaging

    DEFF Research Database (Denmark)

    Ritz, Christian; Gerhard, Daniel; Hothorn, Ludwig A.

    2013-01-01

    for hierarchical data structures, reflecting increasingly common types of assay data. We illustrate the usefulness of the methodology by means of a cytotoxicology example where the sensitivity of two types of assays are evaluated and compared. By means of a simulation study, we show that the proposed framework......This article develops a framework for benchmark dose estimation that allows intrinsically nonlinear dose-response models to be used for continuous data in much the same way as is already possible for quantal data. This means that the same dose-response model equations may be applied to both...

  2. Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy

    International Nuclear Information System (INIS)

    Jayasurya, K.; Fung, G.; Yu, S.; Dehing-Oberije, C.; De Ruysscher, D.; Hope, A.; De Neve, W.; Lievens, Y.; Lambin, P.; Dekker, A. L. A. J.

    2010-01-01

    Purpose: Classic statistical and machine learning models such as support vector machines (SVMs) can be used to predict cancer outcome, but often only perform well if all the input variables are known, which is unlikely in the medical domain. Bayesian network (BN) models have a natural ability to reason under uncertainty and might handle missing data better. In this study, the authors hypothesize that a BN model can predict two-year survival in non-small cell lung cancer (NSCLC) patients as accurately as SVM, but will predict survival more accurately when data are missing. Methods: A BN and SVM model were trained on 322 inoperable NSCLC patients treated with radiotherapy from Maastricht and validated in three independent data sets of 35, 47, and 33 patients from Ghent, Leuven, and Toronto. Missing variables occurred in the data set with only 37, 28, and 24 patients having a complete data set. Results: The BN model structure and parameter learning identified gross tumor volume size, performance status, and number of positive lymph nodes on a PET as prognostic factors for two-year survival. When validated in the full validation set of Ghent, Leuven, and Toronto, the BN model had an AUC of 0.77, 0.72, and 0.70, respectively. A SVM model based on the same variables had an overall worse performance (AUC 0.71, 0.68, and 0.69) especially in the Ghent set, which had the highest percentage of missing the important GTV size data. When only patients with complete data sets were considered, the BN and SVM model performed more alike. Conclusions: Within the limitations of this study, the hypothesis is supported that BN models are better at handling missing data than SVM models and are therefore more suitable for the medical domain. Future works have to focus on improving the BN performance by including more patients, more variables, and more diversity.

  3. Multilevel survival analysis of health inequalities in life expectancy

    Directory of Open Access Journals (Sweden)

    Merlo Juan

    2009-08-01

    Full Text Available Abstract Background The health status of individuals is determined by multiple factors operating at both micro and macro levels and the interactive effects of them. Measures of health inequalities should reflect such determinants explicitly through sources of levels and combining mean differences at group levels and the variation of individuals, for the benefits of decision making and intervention planning. Measures derived recently from marginal models such as beta-binomial and frailty survival, address this issue to some extent, but are limited in handling data with complex structures. Beta-binomial models were also limited in relation to measuring inequalities of life expectancy (LE directly. Methods We propose a multilevel survival model analysis that estimates life expectancy based on survival time with censored data. The model explicitly disentangles total health inequalities in terms of variance components of life expectancy compared to the source of variation at the level of individuals in households and parishes and so on, and estimates group differences of inequalities at the same time. Adjusted distributions of life expectancy by gender and by household socioeconomic level are calculated. Relative and absolute health inequality indices are derived based on model estimates. The model based analysis is illustrated on a large Swedish cohort of 22,680 men and 26,474 women aged 65–69 in 1970 and followed up for 30 years. Model based inequality measures are compared to the conventional calculations. Results Much variation of life expectancy is observed at individual and household levels. Contextual effects at Parish and Municipality level are negligible. Women have longer life expectancy than men and lower inequality. There is marked inequality by the level of household socioeconomic status measured by the median life expectancy in each socio-economic group and the variation in life expectancy within each group. Conclusion Multilevel

  4. Covariate analysis of bivariate survival data

    Energy Technology Data Exchange (ETDEWEB)

    Bennett, L.E.

    1992-01-01

    The methods developed are used to analyze the effects of covariates on bivariate survival data when censoring and ties are present. The proposed method provides models for bivariate survival data that include differential covariate effects and censored observations. The proposed models are based on an extension of the univariate Buckley-James estimators which replace censored data points by their expected values, conditional on the censoring time and the covariates. For the bivariate situation, it is necessary to determine the expectation of the failure times for one component conditional on the failure or censoring time of the other component. Two different methods have been developed to estimate these expectations. In the semiparametric approach these expectations are determined from a modification of Burke's estimate of the bivariate empirical survival function. In the parametric approach censored data points are also replaced by their conditional expected values where the expected values are determined from a specified parametric distribution. The model estimation will be based on the revised data set, comprised of uncensored components and expected values for the censored components. The variance-covariance matrix for the estimated covariate parameters has also been derived for both the semiparametric and parametric methods. Data from the Demographic and Health Survey was analyzed by these methods. The two outcome variables are post-partum amenorrhea and breastfeeding; education and parity were used as the covariates. Both the covariate parameter estimates and the variance-covariance estimates for the semiparametric and parametric models will be compared. In addition, a multivariate test statistic was used in the semiparametric model to examine contrasts. The significance of the statistic was determined from a bootstrap distribution of the test statistic.

  5. Applying model predictive control to power system frequency control

    OpenAIRE

    Ersdal, AM; Imsland, L; Cecilio, IM; Fabozzi, D; Thornhill, NF

    2013-01-01

    16.07.14 KB Ok to add accepted version to Spiral Model predictive control (MPC) is investigated as a control method which may offer advantages in frequency control of power systems than the control methods applied today, especially in presence of increased renewable energy penetration. The MPC includes constraints on both generation amount and generation rate of change, and it is tested on a one-area system. The proposed MPC is tested against a conventional proportional-integral (PI) cont...

  6. Multinational Companies, Technology Spillovers, and Plant Survival

    OpenAIRE

    Holger Görg; Eric Strobl

    2003-01-01

    This paper examines the effect of the presence of multinational companies on plant survival in the host country. We postulate that multinational companies can impact positively on plant survival through technology spillovers. We study the nature of the effect of multinationals using a Cox proportional hazard model which we estimate using plant level data for Irish manufacturing industries. Our results show that the presence of multinationals has a life enhancing effect only on indigenous plan...

  7. Experiment and modeling of an atmospheric pressure arc in an applied oscillating magnetic field

    International Nuclear Information System (INIS)

    Karasik, Max; Roquemore, A. L.; Zweben, S. J.

    2000-01-01

    A set of experiments are carried out to measure and understand the response of a free-burning atmospheric pressure carbon arc to applied transverse dc and ac magnetic fields. The arc is found to deflect parabolically for the dc field and assumes a growing sinusoidal structure for the ac field. A simple analytic two-parameter fluid model of the arc dynamics is derived, in which the arc response is governed by the arc jet originating at the cathode, with the applied JxB force balanced by inertia. Time variation of the applied field allows evaluation of the parameters individually. A fit of the model to the experimental data gives a value for the average jet speed an order of magnitude below Maecker's estimate of the maximum jet speed [H. Maecker, Z. Phys. 141, 198 (1955)]. An example industrial application of the model is considered. (c) 2000 American Institute of Physics

  8. Environmental pollution has sex-dependent effects on local survival

    Science.gov (United States)

    Eeva, Tapio; Hakkarainen, Harri; Laaksonen, Toni; Lehikoinen, Esa

    2006-01-01

    Environmental pollutants cause a potential hazard for survival in free-living animal populations. We modelled local survival (including emigration) by using individual mark–recapture histories of males and females in a population of a small insectivorous passerine bird, the pied flycatcher (Ficedula hypoleuca) living around a point source of heavy metals (copper smelter). Local survival of F. hypoleuca females did not differ between polluted and unpolluted environments. Males, however, showed a one-third higher local-survival probability in the polluted area. Low fledgling production was generally associated with decreased local survival, but males in the polluted area showed relatively high local survival, irrespective of their fledgling number. A possible explanation of higher local survival of males in the polluted area could be a pollution-induced change in hormone (e.g. corticosterone or testosterone) levels of males. It could make them to invest more on their own survival or affect the hormonal control of breeding dispersal. The local survival of males decreased in the polluted area over the study period along with the simultaneous decrease in heavy metal emissions. This temporal trend is in agreement with the stress hormone hypothesis. PMID:17148387

  9. Determinants of IPO survival on the Johannesburg securities exchange

    Directory of Open Access Journals (Sweden)

    Brownhilder Ngek Neneh

    2014-10-01

    Full Text Available The purpose of this paper was to establish the determinants of IPO survival on the Johannesburg Securities Exchange (JSE. Using the Kaplan-Meier test, this study established that firms less than five years prior to listing on the JSE have a significant smaller mean survival time; firms with a gross proceed less than the median have a significant shorter mean survival time; overpriced IPOs have a significant higher survival time; IPOs listed during the hot market period on the JSE have a significant smaller mean survival time and IPOs with return on asset, operating profit margin, and return on equity less than or equal to zero have a low mean survival time. Also, being in the internet industry significantly shortens the mean survival time of an IPO. Moreover, based on the Cox Proportional Hazard model, it was established that the determinants of IPO survivability on the JSE are the firms’ age, size, market period, return on equity and operating profit margin are. These findings provide investors and companies in the JSE with empirical evidence of the determinants of IPO survivability of the JSE. As such, investors are advised to consider these factors when selecting their portfolios

  10. Survival analysis approach to account for non-exponential decay rate effects in lifetime experiments

    International Nuclear Information System (INIS)

    Coakley, K.J.; Dewey, M.S.; Huber, M.G.; Huffer, C.R.; Huffman, P.R.; Marley, D.E.; Mumm, H.P.; O'Shaughnessy, C.M.; Schelhammer, K.W.; Thompson, A.K.; Yue, A.T.

    2016-01-01

    In experiments that measure the lifetime of trapped particles, in addition to loss mechanisms with exponential survival probability functions, particles can be lost by mechanisms with non-exponential survival probability functions. Failure to account for such loss mechanisms produces systematic measurement error and associated systematic uncertainties in these measurements. In this work, we develop a general competing risks survival analysis method to account for the joint effect of loss mechanisms with either exponential or non-exponential survival probability functions, and a method to quantify the size of systematic effects and associated uncertainties for lifetime estimates. As a case study, we apply our survival analysis formalism and method to the Ultra Cold Neutron lifetime experiment at NIST. In this experiment, neutrons can escape a magnetic trap before they decay due to a wall loss mechanism with an associated non-exponential survival probability function.

  11. Survival analysis approach to account for non-exponential decay rate effects in lifetime experiments

    Energy Technology Data Exchange (ETDEWEB)

    Coakley, K.J., E-mail: kevincoakley@nist.gov [National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305 (United States); Dewey, M.S.; Huber, M.G. [National Institute of Standards and Technology, 100 Bureau Drive, Stop 8461, Gaithersburg, MD 20899 (United States); Huffer, C.R.; Huffman, P.R. [North Carolina State University, 2401 Stinson Drive, Box 8202, Raleigh, NC 27695 (United States); Triangle Universities Nuclear Laboratory, 116 Science Drive, Box 90308, Durham, NC 27708 (United States); Marley, D.E. [National Institute of Standards and Technology, 100 Bureau Drive, Stop 8461, Gaithersburg, MD 20899 (United States); North Carolina State University, 2401 Stinson Drive, Box 8202, Raleigh, NC 27695 (United States); Mumm, H.P. [National Institute of Standards and Technology, 100 Bureau Drive, Stop 8461, Gaithersburg, MD 20899 (United States); O' Shaughnessy, C.M. [University of North Carolina at Chapel Hill, 120 E. Cameron Ave., CB #3255, Chapel Hill, NC 27599 (United States); Triangle Universities Nuclear Laboratory, 116 Science Drive, Box 90308, Durham, NC 27708 (United States); Schelhammer, K.W. [North Carolina State University, 2401 Stinson Drive, Box 8202, Raleigh, NC 27695 (United States); Triangle Universities Nuclear Laboratory, 116 Science Drive, Box 90308, Durham, NC 27708 (United States); Thompson, A.K.; Yue, A.T. [National Institute of Standards and Technology, 100 Bureau Drive, Stop 8461, Gaithersburg, MD 20899 (United States)

    2016-03-21

    In experiments that measure the lifetime of trapped particles, in addition to loss mechanisms with exponential survival probability functions, particles can be lost by mechanisms with non-exponential survival probability functions. Failure to account for such loss mechanisms produces systematic measurement error and associated systematic uncertainties in these measurements. In this work, we develop a general competing risks survival analysis method to account for the joint effect of loss mechanisms with either exponential or non-exponential survival probability functions, and a method to quantify the size of systematic effects and associated uncertainties for lifetime estimates. As a case study, we apply our survival analysis formalism and method to the Ultra Cold Neutron lifetime experiment at NIST. In this experiment, neutrons can escape a magnetic trap before they decay due to a wall loss mechanism with an associated non-exponential survival probability function.

  12. NanOx, a new model to predict cell survival in the context of particle therapy

    Science.gov (United States)

    Cunha, M.; Monini, C.; Testa, E.; Beuve, M.

    2017-02-01

    Particle therapy is increasingly attractive for the treatment of tumors and the number of facilities offering it is rising worldwide. Due to the well-known enhanced effectiveness of ions, it is of utmost importance to plan treatments with great care to ensure tumor killing and healthy tissues sparing. Hence, the accurate quantification of the relative biological effectiveness (RBE) of ions, used in the calculation of the biological dose, is critical. Nevertheless, the RBE is a complex function of many parameters and its determination requires modeling. The approaches currently used have allowed particle therapy to thrive, but still show some shortcomings. We present herein a short description of a new theoretical framework, NanOx, to calculate cell survival in the context of particle therapy. It gathers principles from existing approaches, while addressing some of their weaknesses. NanOx is a multiscale model that takes the stochastic nature of radiation at nanometric and micrometric scales fully into account, integrating also the chemical aspects of radiation-matter interaction. The latter are included in the model by means of a chemical specific energy, determined from the production of reactive chemical species induced by irradiation. Such a production represents the accumulation of oxidative stress and sublethal damage in the cell, potentially generating non-local lethal events in NanOx. The complementary local lethal events occur in a very localized region and can, alone, lead to cell death. Both these classes of events contribute to cell death. The comparison between experimental data and model predictions for the V79 cell line show a good agreement. In particular, the dependence of the typical shoulders of cell survival curves on linear energy transfer are well described, but also the effectiveness of different ions, including the overkill effect. These results required the adjustment of a number of parameters compatible with the application of the model in

  13. Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients.

    Science.gov (United States)

    Tunali, Ilke; Stringfield, Olya; Guvenis, Albert; Wang, Hua; Liu, Ying; Balagurunathan, Yoganand; Lambin, Philippe; Gillies, Robert J; Schabath, Matthew B

    2017-11-10

    The goal of this study was to extract features from radial deviation and radial gradient maps which were derived from thoracic CT scans of patients diagnosed with lung adenocarcinoma and assess whether these features are associated with overall survival. We used two independent cohorts from different institutions for training (n= 61) and test (n= 47) and focused our analyses on features that were non-redundant and highly reproducible. To reduce the number of features and covariates into a single parsimonious model, a backward elimination approach was applied. Out of 48 features that were extracted, 31 were eliminated because they were not reproducible or were redundant. We considered 17 features for statistical analysis and identified a final model containing the two most highly informative features that were associated with lung cancer survival. One of the two features, radial deviation outside-border separation standard deviation, was replicated in a test cohort exhibiting a statistically significant association with lung cancer survival (multivariable hazard ratio = 0.40; 95% confidence interval 0.17-0.97). Additionally, we explored the biological underpinnings of these features and found radial gradient and radial deviation image features were significantly associated with semantic radiological features.

  14. A model of the radiation-induced bystander effect based on an analogy with ferromagnets. Application to modelling tissue response in a uniform field

    Science.gov (United States)

    Vassiliev, O. N.

    2014-12-01

    We propose a model of the radiation-induced bystander effect based on an analogy with magnetic systems. The main benefit of this approach is that it allowed us to apply powerful methods of statistical mechanics. The model exploits the similarity between how spin-spin interactions result in correlations of spin states in ferromagnets, and how signalling from a damaged cell reduces chances of survival of neighbour cells, resulting in correlated cell states. At the root of the model is a classical Hamiltonian, similar to that of an Ising ferromagnet with long-range interactions. The formalism is developed in the framework of the Mean Field Theory. It is applied to modelling tissue response in a uniform radiation field. In this case the results are remarkably simple and at the same time nontrivial. They include cell survival curves, expressions for the tumour control probability and effects of fractionation. The model extends beyond of what is normally considered as bystander effects. It offers an insight into low-dose hypersensitivity and into mechanisms behind threshold doses for deterministic effects.

  15. New Insights to Compare and Choose TKTD Models for Survival Based on an Interlaboratory Study for Lymnaea stagnalis Exposed to Cd.

    Science.gov (United States)

    Baudrot, Virgile; Preux, Sara; Ducrot, Virginie; Pave, Alain; Charles, Sandrine

    2018-02-06

    Toxicokinetic-toxicodynamic (TKTD) models, as the General Unified Threshold model of Survival (GUTS), provide a consistent process-based framework compared to classical dose-response models to analyze both time and concentration-dependent data sets. However, the extent to which GUTS models (Stochastic Death (SD) and Individual Tolerance (IT)) lead to a better fitting than classical dose-response model at a given target time (TT) has poorly been investigated. Our paper highlights that GUTS estimates are generally more conservative and have a reduced uncertainty through smaller credible intervals for the studied data sets than classical TT approaches. Also, GUTS models enable estimating any x% lethal concentration at any time (LC x,t ), and provide biological information on the internal processes occurring during the experiments. While both GUTS-SD and GUTS-IT models outcompete classical TT approaches, choosing one preferentially to the other is still challenging. Indeed, the estimates of survival rate over time and LC x,t are very close between both models, but our study also points out that the joint posterior distributions of SD model parameters are sometimes bimodal, while two parameters of the IT model seems strongly correlated. Therefore, the selection between these two models has to be supported by the experimental design and the biological objectives, and this paper provides some insights to drive this choice.

  16. Fusion probability and survivability in estimates of heaviest nuclei production

    International Nuclear Information System (INIS)

    Sagaidak, Roman

    2012-01-01

    A number of theoretical models have been recently developed to predict production cross sections for the heaviest nuclei in fusion-evaporation reactions. All the models reproduce cross sections obtained in experiments quite well. At the same time they give fusion probability values P fus ≡ P CN differed within several orders of the value. This difference implies a corresponding distinction in the calculated values of survivability. The production of the heaviest nuclei (from Cm to the region of superheavy elements (SHE) close to Z = 114 and N = 184) in fusion-evaporation reactions induced by heavy ions has been considered in a systematic way within the framework of the barrier-passing (fusion) model coupled with the standard statistical model (SSM) of the compound nucleus (CN) decay. Both models are incorporated into the HIVAP code. Available data on the excitation functions for fission and evaporation residues (ER) produced in very asymmetric combinations can be described rather well within the framework of HIVAP. Cross-section data obtained in these reactions allow one to choose model parameters quite definitely. Thus one can scale and fix macroscopic (liquid-drop) fission barriers for nuclei involved in the evaporation-fission cascade. In less asymmetric combinations (with 22 Ne and heavier projectiles) effects of fusion suppression caused by quasi-fission are starting to appear in the entrance channel of reactions. The P fus values derived from the capture-fission and fusion-fission cross-sections obtained at energies above the Bass barrier were plotted as a function of the Coulomb parameter. For more symmetric combinations one can deduce the P fus values semi-empirically, using the ER and fission excitation functions measured in experiments, and applying SSM model with parameters obtained in the analysis of a very asymmetric combination leading to the production of (nearly) the same CN, as was done for reactions leading to the pre-actinide nuclei formation

  17. Testing homogeneity in Weibull-regression models.

    Science.gov (United States)

    Bolfarine, Heleno; Valença, Dione M

    2005-10-01

    In survival studies with families or geographical units it may be of interest testing whether such groups are homogeneous for given explanatory variables. In this paper we consider score type tests for group homogeneity based on a mixing model in which the group effect is modelled as a random variable. As opposed to hazard-based frailty models, this model presents survival times that conditioned on the random effect, has an accelerated failure time representation. The test statistics requires only estimation of the conventional regression model without the random effect and does not require specifying the distribution of the random effect. The tests are derived for a Weibull regression model and in the uncensored situation, a closed form is obtained for the test statistic. A simulation study is used for comparing the power of the tests. The proposed tests are applied to real data sets with censored data.

  18. Bayesian Analysis for Dynamic Generalized Linear Latent Model with Application to Tree Survival Rate

    Directory of Open Access Journals (Sweden)

    Yu-sheng Cheng

    2014-01-01

    Full Text Available Logistic regression model is the most popular regression technique, available for modeling categorical data especially for dichotomous variables. Classic logistic regression model is typically used to interpret relationship between response variables and explanatory variables. However, in real applications, most data sets are collected in follow-up, which leads to the temporal correlation among the data. In order to characterize the different variables correlations, a new method about the latent variables is introduced in this study. At the same time, the latent variables about AR (1 model are used to depict time dependence. In the framework of Bayesian analysis, parameters estimates and statistical inferences are carried out via Gibbs sampler with Metropolis-Hastings (MH algorithm. Model comparison, based on the Bayes factor, and forecasting/smoothing of the survival rate of the tree are established. A simulation study is conducted to assess the performance of the proposed method and a pika data set is analyzed to illustrate the real application. Since Bayes factor approaches vary significantly, efficiency tests have been performed in order to decide which solution provides a better tool for the analysis of real relational data sets.

  19. Overall survival and disease-free survival in endometrial cancer: prognostic factors in 276 patients

    Directory of Open Access Journals (Sweden)

    Tejerizo-García A

    2013-09-01

    Full Text Available Álvaro Tejerizo-García,1 Jesús S Jiménez-López,1 José L Muñoz-González,1 Sara Bartolomé-Sotillos,1 Laura Marqueta-Marqués,1 Gregorio López-González,1 José F Pérez-Regadera Gómez21Service of Obstetrics and Gynecology, 2Radiation Oncology Service, Hospital Universitario 12 de Octubre, Madrid, SpainObjective: The aim of the study reported here was to assess the disease-free survival and overall survival of patients with endometrial cancer and to determine independent factors affecting the prognosis.Materials and methods: This was a retrospective study of a single-center clinical series of 276 patients (mean age 64 years with histologically confirmed cancer of the corpus uteri. The standard treatments were extrafascial total hysterectomy and bilateral salpingo-oophorectomy with selective pelvic/para-aortic node dissection, according to risk for recurrence. Actuarial overall survival and disease-free survival were estimated according to the Kaplan–Meier method. Univariate and multivariate Cox proportional hazards analyses were used to assess the prognostic significance of the different variables.Results: The estimated median follow-up, determined using the inverse Kaplan–Meier method, was 45 months (95% confidence interval [CI] 41.2–48.8 for disease-free survival and 46 months (95% CI 43.0–49.0 for overall survival. The statistically significant variables affecting disease-free survival and overall survival were age, serous-papillary and clear-cell histological types, outer-half myometrial invasion, advanced International Federation of Gynecology and Obstetrics (FIGO stage, tumor grades G2 and G3, incomplete surgical resection, positive lymph nodes, lymphovascular space invasion, tumor remnants of >1 cm after surgery, and high-risk group. In the multivariate Cox regression model, predictors of tumor recurrence included advanced FIGO stage (hazard ratio [HR] 4.90, 95% CI 2.57–9.36, P < 0.001 and tumor grades G2 (HR 4.79, 95

  20. Relative effects of survival and reproduction on the population dynamics of emperor geese

    Science.gov (United States)

    Schmutz, Joel A.; Rockwell, Robert F.; Petersen, Margaret R.

    1997-01-01

    Populations of emperor geese (Chen canagica) in Alaska declined sometime between the mid-1960s and the mid-1980s and have increased little since. To promote recovery of this species to former levels, managers need to know how much their perturbations of survival and/or reproduction would affect population growth rate (λ). We constructed an individual-based population model to evaluate the relative effect of altering mean values of various survival and reproductive parameters on λ and fall age structure (AS, defined as the proportion of juv), assuming additive rather than compensatory relations among parameters. Altering survival of adults had markedly greater relative effects on λ than did equally proportionate changes in either juvenile survival or reproductive parameters. We found the opposite pattern for relative effects on AS. Due to concerns about bias in the initial parameter estimates used in our model, we used 5 additional sets of parameter estimates with this model structure. We found that estimates of survival based on aerial survey data gathered each fall resulted in models that corresponded more closely to independent estimates of λ than did models that used mark-recapture estimates of survival. This disparity suggests that mark-recapture estimates of survival are biased low. To further explore how parameter estimates affected estimates of λ, we used values of survival and reproduction found in other goose species, and we examined the effect of an hypothesized correlation between an individual's clutch size and the subsequent survival of her young. The rank order of parameters in their relative effects on λ was consistent for all 6 parameter sets we examined. The observed variation in relative effects on λ among the 6 parameter sets is indicative of how relative effects on λ may vary among goose populations. With this knowledge of the relative effects of survival and reproductive parameters on λ, managers can make more informed decisions about

  1. Obesity adversely affects survival in pancreatic cancer patients.

    Science.gov (United States)

    McWilliams, Robert R; Matsumoto, Martha E; Burch, Patrick A; Kim, George P; Halfdanarson, Thorvardur R; de Andrade, Mariza; Reid-Lombardo, Kaye; Bamlet, William R

    2010-11-01

    Higher body-mass index (BMI) has been implicated as a risk factor for developing pancreatic cancer, but its effect on survival has not been thoroughly investigated. The authors assessed the association of BMI with survival in a sample of pancreatic cancer patients and used epidemiologic and clinical information to understand the contribution of diabetes and hyperglycemia. A survival analysis using Cox proportional hazards by usual adult BMI was performed on 1861 unselected patients with pancreatic adenocarcinoma; analyses were adjusted for covariates that included clinical stage, age, and sex. Secondary analyses incorporated self-reported diabetes and fasting blood glucose in the survival model. BMI as a continuous variable was inversely associated with survival from pancreatic adenocarcinoma (hazard ratio [HR], 1.019 for each increased unit of BMI [kg/m2], PFasting blood glucose and diabetes did not affect the results. Higher BMI is associated with decreased survival in pancreatic cancer. Although the mechanism of this association remains undetermined, diabetes and hyperglycemia do not appear to account for the observed association. Copyright © 2010 American Cancer Society.

  2. Inferential Statistics from Black Hispanic Breast Cancer Survival Data

    Directory of Open Access Journals (Sweden)

    Hafiz M. R. Khan

    2014-01-01

    Full Text Available In this paper we test the statistical probability models for breast cancer survival data for race and ethnicity. Data was collected from breast cancer patients diagnosed in United States during the years 1973–2009. We selected a stratified random sample of Black Hispanic female patients from the Surveillance Epidemiology and End Results (SEER database to derive the statistical probability models. We used three common model building criteria which include Akaike Information Criteria (AIC, Bayesian Information Criteria (BIC, and Deviance Information Criteria (DIC to measure the goodness of fit tests and it was found that Black Hispanic female patients survival data better fit the exponentiated exponential probability model. A novel Bayesian method was used to derive the posterior density function for the model parameters as well as to derive the predictive inference for future response. We specifically focused on Black Hispanic race. Markov Chain Monte Carlo (MCMC method was used for obtaining the summary results of posterior parameters. Additionally, we reported predictive intervals for future survival times. These findings would be of great significance in treatment planning and healthcare resource allocation.

  3. Nature preservation acceptance model applied to tanker oil spill simulations

    DEFF Research Database (Denmark)

    Friis-Hansen, Peter; Ditlevsen, Ove Dalager

    2003-01-01

    is exemplified by a study of oil spills due to simulated tanker collisions in the Danish straits. It is found that the distribution of the oil spill volume per spill is well represented by an exponential distribution both in Oeresund and in Great Belt. When applied in the Poisson model, a risk profile reasonably...... acceptance criterion for the pollution of the environment. This NPWI acceptance criterion is applied to the oil spill example....... be defined in a similar way as the so-called Life Quality Index defined by Nathwani et al [Nathwani JS, Lind NC, Padey MD. Affordable safety by choice: the life quality method. Institute for Risk Research, University of Waterloo; Waterloo (Ontario, Canada):1997], and can be used to quantify the risk...

  4. Applying Catastrophe Theory to an Information-Processing Model of Problem Solving in Science Education

    Science.gov (United States)

    Stamovlasis, Dimitrios; Tsaparlis, Georgios

    2012-01-01

    In this study, we test an information-processing model (IPM) of problem solving in science education, namely the working memory overload model, by applying catastrophe theory. Changes in students' achievement were modeled as discontinuities within a cusp catastrophe model, where working memory capacity was implemented as asymmetry and the degree…

  5. Adipose-derived stromal cells enhance auditory neuron survival in an animal model of sensory hearing loss.

    Science.gov (United States)

    Schendzielorz, Philipp; Vollmer, Maike; Rak, Kristen; Wiegner, Armin; Nada, Nashwa; Radeloff, Katrin; Hagen, Rudolf; Radeloff, Andreas

    2017-10-01

    A cochlear implant (CI) is an electronic prosthesis that can partially restore speech perception capabilities. Optimum information transfer from the cochlea to the central auditory system requires a proper functioning auditory nerve (AN) that is electrically stimulated by the device. In deafness, the lack of neurotrophic support, normally provided by the sensory cells of the inner ear, however, leads to gradual degeneration of auditory neurons with undesirable consequences for CI performance. We evaluated the potential of adipose-derived stromal cells (ASCs) that are known to produce neurotrophic factors to prevent neural degeneration in sensory hearing loss. For this, co-cultures of ASCs with auditory neurons have been studied, and autologous ASC transplantation has been performed in a guinea pig model of gentamicin-induced sensory hearing loss. In vitro ASCs were neuroprotective and considerably increased the neuritogenesis of auditory neurons. In vivo transplantation of ASCs into the scala tympani resulted in an enhanced survival of auditory neurons. Specifically, peripheral AN processes that are assumed to be the optimal activation site for CI stimulation and that are particularly vulnerable to hair cell loss showed a significantly higher survival rate in ASC-treated ears. ASC transplantation into the inner ear may restore neurotrophic support in sensory hearing loss and may help to improve CI performance by enhanced AN survival. Copyright © 2017 International Society for Cellular Therapy. Published by Elsevier Inc. All rights reserved.

  6. Preliminary study of the effects of smectite granules (WoundStat) on vascular repair and wound healing in a swine survival model.

    Science.gov (United States)

    Gerlach, Travis; Grayson, J Kevin; Pichakron, Kullada O; Sena, Matthew J; DeMartini, Steven D; Clark, Beth Z; Estep, J Scot; Zierold, Dustin

    2010-11-01

    WoundStat (WS) (TraumaCure, Bethesda, MD) is a topical hemostatic agent that effectively stops severe hemorrhage in animal models. To the best of our knowledge, no survival study has been conducted to ensure long-term product safety. We evaluated vascular patency and tissue responses to WS in a swine femoral artery injury model with survival up to 5 weeks. Anesthetized swine received a standardized femoral artery injury with free hemorrhage for 45 seconds followed by WS application. One hour after application, the WS was removed, the wound copiously irrigated, and the artery repaired using a vein patch. Six groups of three animals received WS and were killed either immediately after surgery or at weekly intervals up to 5 weeks. Three control animals were treated with gauze packing and direct pressure followed by identical vascular repair and survival for 1 week. At the time of killing, angiograms were performed, and tissue was collected for histopathology. Hemostasis was complete in all WS animals. All animals survived the procedure, and there were no clinically evident postoperative complications. Vascular repairs were angiographically patent in 15 of 18 animals (83%) receiving WS. Histopathologic examination of WS animals revealed severe diffuse fibrogranulomatous inflammation, early endothelial degeneration with subsequent intimal hyperplasia, moderate myocyte necrosis, and fibrogranulomatous nerve entrapment with axonal degeneration. Although an effective hemostatic agent, WS use was associated with a substantial local inflammatory response and neurovascular changes up to 5 weeks postinjury.

  7. Project management characteristics and new product survival

    NARCIS (Netherlands)

    Thieme, R.J.; Song, X.M.; Shin, C.S.

    2003-01-01

    We develop a conceptual model of new product development (NPD) based on seminal and review articles in order to answer the question, "What project management characteristics will foster the development of new products that are more likely to survive in the marketplace?" Our model adopts Ruekert and

  8. Simulation of parametric model towards the fixed covariate of right censored lung cancer data

    Science.gov (United States)

    Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Ridwan Olaniran, Oyebayo; Enera Amran, Syahila

    2017-09-01

    In this study, simulation procedure was applied to measure the fixed covariate of right censored data by using parametric survival model. The scale and shape parameter were modified to differentiate the analysis of parametric regression survival model. Statistically, the biases, mean biases and the coverage probability were used in this analysis. Consequently, different sample sizes were employed to distinguish the impact of parametric regression model towards right censored data with 50, 100, 150 and 200 number of sample. R-statistical software was utilised to develop the coding simulation with right censored data. Besides, the final model of right censored simulation was compared with the right censored lung cancer data in Malaysia. It was found that different values of shape and scale parameter with different sample size, help to improve the simulation strategy for right censored data and Weibull regression survival model is suitable fit towards the simulation of survival of lung cancer patients data in Malaysia.

  9. Method for widespread microRNA-155 inhibition prolongs survival in ALS-model mice

    Science.gov (United States)

    Koval, Erica D.; Shaner, Carey; Zhang, Peter; du Maine, Xavier; Fischer, Kimberlee; Tay, Jia; Chau, B. Nelson; Wu, Gregory F.; Miller, Timothy M.

    2013-01-01

    microRNAs (miRNAs) are dysregulated in a variety of disease states, suggesting that this newly discovered class of gene expression repressors may be viable therapeutic targets. A microarray of miRNA changes in ALS-model superoxide dismutase 1 (SOD1)G93A rodents identified 12 miRNAs as significantly changed. Six miRNAs tested in human ALS tissues were confirmed increased. Specifically, miR-155 was increased 5-fold in mice and 2-fold in human spinal cords. To test miRNA inhibition in the central nervous system (CNS) as a potential novel therapeutic, we developed oligonucleotide-based miRNA inhibitors (anti-miRs) that could inhibit miRNAs throughout the CNS and in the periphery. Anti-miR-155 caused global derepression of targets in peritoneal macrophages and, following intraventricular delivery, demonstrated widespread functional distribution in the brain and spinal cord. After treating SOD1G93A mice with anti-miR-155, we significantly extended survival by 10 days and disease duration by 15 days (38%) while a scrambled control anti-miR did not significantly improve survival or disease duration. Therefore, antisense oligonucleotides may be used to successfully inhibit miRNAs throughout the brain and spinal cord, and miR-155 is a promising new therapeutic target for human ALS. PMID:23740943

  10. Simvastatin Treatment Improves Survival in a Murine Model of Burn Sepsis

    Science.gov (United States)

    Beffa, David C; Fischman, Alan J.; Fagan, Shawn P.; Hamrahi, Victoria F.; Kaneki, Masao; Yu, Yong-Ming; Tompkins, Ronald G.; Carter, Edward A.

    2014-01-01

    Infection is the most common and most serious complication of a major burn injury related to burn size. Despite improvements in antimicrobial therapies sepsis still accounts for 50–60% of deaths in burn patients. Given the acute onset and unpredictable nature of sepsis, primary prevention was rarely attempted in its management. However, recent studies have demonstrated that statin treatment can decrease mortality is a murine model of sepsis by preservation of cardiac function and reversal of inflammatory alterations. In addition, it has been shown that treatment with statins is associated with reduced incidence of sepsis in human patients. In the current study groups of CD1 male mice (n=12) were anesthetized and subjected to a dorsal 30% TBSA scald burn injury. Starting 2 hours post burn, the animals were divided into a treatment group receiving 0.2 µ/g simvastatin or a sham group receiving placebo. Simvastatin and placebo were administered by intraperitoneal injection with two dosing regimens; once daily and every 12 hours. On Post burn day 7 cecal ligation and puncture with a 21-gauge needle was performed under ketamine/xylazine anesthesia and the two different dosing schedules were continued. A simvastatin dose dependant improvement in survival was observed in the burn sepsis model. PMID:21145172

  11. Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

    Science.gov (United States)

    Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; More, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

    2016-03-01

    How to rationally identify epithelial ovarian cancer (EOC) patients who will benefit from bevacizumab or other antiangiogenic therapies is a critical issue in EOC treatments. The motivation of this study is to quantitatively measure adiposity features from CT images and investigate the feasibility of predicting potential benefit of EOC patients with or without receiving bevacizumab-based chemotherapy treatment using multivariate statistical models built based on quantitative adiposity image features. A dataset involving CT images from 59 advanced EOC patients were included. Among them, 32 patients received maintenance bevacizumab after primary chemotherapy and the remaining 27 patients did not. We developed a computer-aided detection (CAD) scheme to automatically segment subcutaneous fat areas (VFA) and visceral fat areas (SFA) and then extracted 7 adiposity-related quantitative features. Three multivariate data analysis models (linear regression, logistic regression and Cox proportional hazards regression) were performed respectively to investigate the potential association between the model-generated prediction results and the patients' progression-free survival (PFS) and overall survival (OS). The results show that using all 3 statistical models, a statistically significant association was detected between the model-generated results and both of the two clinical outcomes in the group of patients receiving maintenance bevacizumab (p<0.01), while there were no significant association for both PFS and OS in the group of patients without receiving maintenance bevacizumab. Therefore, this study demonstrated the feasibility of using quantitative adiposity-related CT image features based statistical prediction models to generate a new clinical marker and predict the clinical outcome of EOC patients receiving maintenance bevacizumab-based chemotherapy.

  12. Dynamic Models Applied to Landslides: Study Case Angangueo, MICHOACÁN, MÉXICO.

    Science.gov (United States)

    Torres Fernandez, L.; Hernández Madrigal, V. M., , Dr; Capra, L.; Domínguez Mota, F. J., , Dr

    2017-12-01

    Most existing models for landslide zonification are static type, do not consider the dynamic behavior of the trigger factor. This results in a limited representation of the actual zonation of slope instability, present a short-term validity, cańt be applied for the design of early warning systems, etc. Particularly in Mexico, these models are static because they do not consider triggering factor such as precipitation. In this work, we present a numerical evaluation to know the landslide susceptibility, based on probabilistic methods. Which are based on the generation of time series, which are generated from the meteorological stations, having limited information an interpolation is made to generate the simulation of the precipitation in the zone. The obtained information is integrated in PCRaster and in conjunction with the conditioning factors it is possible to generate a dynamic model. This model will be applied for landslide zoning in the municipality of Angangueo, characterized by frequent logging of debris and mud flow, translational and rotational landslides, detonated by atypical precipitations, such as those recorded in 2010. These caused economic losses and humans. With these models, it would be possible to generate probable scenarios that help the Angangueo's population to reduce the risks and to carry out actions of constant resilience activities.

  13. Evaluation of the Weibull and log normal distribution functions as survival models of Escherichia coli under isothermal and non isothermal conditions.

    Science.gov (United States)

    Aragao, Glaucia M F; Corradini, Maria G; Normand, Mark D; Peleg, Micha

    2007-11-01

    Published survival curves of Escherichia coli in two growth media, with and without the presence of salt, at various temperatures and in a Greek eggplant salad having various levels of essential oil, all had a characteristic downward concavity when plotted on semi logarithmic coordinates. Some also exhibited what appeared as a 'shoulder' of considerable length. Regardless of whether a shoulder was noticed, the survival pattern could be considered as a manifestation of an underlying unimodal distribution of the cells' death times. Mathematically, the data could be described equally well by the Weibull and log normal distribution functions, which had similar modes, means, standard deviations and coefficients of skewness. When plotted in their probability density function (PDF) form, the curves also appeared very similar visually. This enabled us to quantify and compare the effect of temperature or essential oil concentration on the organism's survival in terms of these temporal distributions' characteristics. Increased lethality was generally expressed in a shorter mean and mode, a smaller standard deviation and increased overall symmetry as judged by the distributions' degree of skewness. The 'shoulder', as expected, simply indicated that the distribution's standard deviation was much smaller than its mode. Rate models based on the two distribution functions could be used to predict non isothermal survival patterns. They were derived on the assumption that the momentary inactivation rate is the isothermal rate at the momentary temperature at a time that corresponds to the momentary survival ratio. In this application, however, the Weibullian model with a fixed power was not only simpler and more convenient mathematically than the one based on the log normal distribution, but it also provided more accurate estimates of the dynamic inactivation patterns.

  14. An effective correlated mean-field theory applied in the spin-1/2 Ising ferromagnetic model

    Energy Technology Data Exchange (ETDEWEB)

    Roberto Viana, J.; Salmon, Octávio R. [Universidade Federal do Amazonas – UFAM, Manaus 69077-000, AM (Brazil); Ricardo de Sousa, J. [Universidade Federal do Amazonas – UFAM, Manaus 69077-000, AM (Brazil); National Institute of Science and Technology for Complex Systems, Universidade Federal do Amazonas, 3000, Japiim, 69077-000 Manaus, AM (Brazil); Neto, Minos A.; Padilha, Igor T. [Universidade Federal do Amazonas – UFAM, Manaus 69077-000, AM (Brazil)

    2014-11-15

    We developed a new treatment for mean-field theory applied in spins systems, denominated effective correlated mean-field (ECMF). We apply this theory to study the spin-1/2 Ising ferromagnetic model with nearest-neighbor interactions on a square lattice. We use clusters of finite sizes and study the criticality of the ferromagnetic system, where we obtain a convergence of critical temperature for the value k{sub B}T{sub c}/J≃2.27905±0.00141. Also the behavior of magnetic and thermodynamic properties, using the condition of minimum energy of the physical system is obtained. - Highlights: • We developed spin models to study real magnetic systems. • We study the thermodynamic and magnetic properties of the ferromagnetism. • We enhanced a mean-field theory applied in spins models.

  15. Hemodialysis versus Peritoneal Dialysis: A Comparison of Survival Outcomes in South-East Asian Patients with End-Stage Renal Disease.

    Directory of Open Access Journals (Sweden)

    Fan Yang

    Full Text Available Studies comparing patient survival of hemodialysis (HD and peritoneal dialysis (PD have yielded conflicting results and no such study was from South-East Asia. This study aimed to compare the survival outcomes of patients with end-stage renal disease (ESRD who started dialysis with HD and PD in Singapore.Survival data for a maximum of 5 years from a single-center cohort of 871 ESRD patients starting dialysis with HD (n = 641 or PD (n = 230 from 2005-2010 was analyzed using the flexible Royston-Parmar (RP model. The model was also applied to a subsample of 225 propensity-score-matched patient pairs and subgroups defined by age, diabetes mellitus, and cardiovascular disease.After adjusting for the effect of socio-demographic and clinical characteristics, the risk of death was higher in patients initiating dialysis with PD than those initiating dialysis with HD (hazard ratio [HR]: 2.08; 95% confidence interval [CI]: 1.67-2.59; p<0.001, although there was no significant difference in mortality between the two modalities in the first 12 months of treatment. Consistently, in the matched subsample, patients starting PD had a higher risk of death than those starting HD (HR: 1.73, 95% CI: 1.30-2.28, p<0.001. Subgroup analysis showed that PD may be similar to or better than HD in survival outcomes among young patients (≤65 years old without diabetes or cardiovascular disease.ESRD patients who initiated dialysis with HD experienced better survival outcomes than those who initiated dialysis with PD in Singapore, although survival outcomes may not differ between the two dialysis modalities in young and healthier patients. These findings are potentially confounded by selection bias, as patients were not randomized to the two dialysis modalities in this cohort study.

  16. Formulating accident occurrence as a survival process.

    Science.gov (United States)

    Chang, H L; Jovanis, P P

    1990-10-01

    A conceptual framework for accident occurrence is developed based on the principle of the driver as an information processor. The framework underlies the development of a modeling approach that is consistent with the definition of exposure to risk as a repeated trial. Survival theory is proposed as a statistical technique that is consistent with the conceptual structure and allows the exploration of a wide range of factors that contribute to highway operating risk. This survival model of accident occurrence is developed at a disaggregate level, allowing safety researchers to broaden the scope of studies which may be limited by the use of traditional aggregate approaches. An application of the approach to motor carrier safety is discussed as are potential applications to a variety of transportation industries. Lastly, a typology of highway safety research methodologies is developed to compare the properties of four safety methodologies: laboratory experiments, on-the-road studies, multidisciplinary accident investigations, and correlational studies. The survival theory formulation has a mathematical structure that is compatible with each safety methodology, so it may facilitate the integration of findings across methodologies.

  17. Neuro-peptide treatment with Cerebrolysin improves the survival of neural stem cell grafts in an APP transgenic model of Alzheimer disease

    Directory of Open Access Journals (Sweden)

    Edward Rockenstein

    2015-07-01

    Full Text Available Neural stem cells (NSCs have been considered as potential therapy in Alzheimer's disease (AD but their use is hampered by the poor survival of grafted cells. Supply of neurotrophic factors to the grafted cells has been proposed as a way to augment survival of the stem cells. In this context, we investigated the utility of Cerebrolysin (CBL, a peptidergic mixture with neurotrophic-like properties, as an adjunct to stem cell therapy in an APP transgenic (tg model of AD. We grafted murine NSCs into the hippocampus of non-tg and APP tg that were treated systemically with CBL and analyzed after 1, 3, 6 and 9 months post grafting. Compared to vehicle-treated non-tg mice, in the vehicle-treated APP tg mice there was considerable reduction in the survival of the grafted NSCs. Whereas, CBL treatment enhanced the survival of NSCs in both non-tg and APP tg with the majority of the surviving NSCs remaining as neuroblasts. The NSCs of the CBL treated mice displayed reduced numbers of caspase-3 and TUNEL positive cells and increased brain derived neurotrophic factor (BDNF and furin immunoreactivity. These results suggest that CBL might protect grafted NSCs and as such be a potential adjuvant therapy when combined with grafting.

  18. Survival, transport, and sources of fecal bacteria in streams and survival in land-applied poultry litter in the upper Shoal Creek basin, southwestern Missouri, 2001-2002

    Science.gov (United States)

    Schumacher, John G.

    2003-01-01

    five sampling sites along the 5.7-mi study reach of Shoal Creek, but the trends at successive downstream sites were out of phase and could not be explained by simple advection and dispersion. At base-flow conditions, the travel time of bacteria in Shoal Creek along the 5.7-mi reach between State Highway W (site 2) and the MDNR sampling site (site 3) was about 26 hours. Substantial dispersion and dilution occurs along the upper 4.1 mi of this reach because of inflows from a number of springs and tributaries and the presence of several long pools and channel meanders. Minimal dispersion and dilution occurs along the 1.6-mi reach immediately upstream from the MDNR sampling site. Measurements of fecal bacteria decay in Shoal Creek during July 2001 indicated that about 8 percent of fecal coliform and E. coli bacteria decay each hour with an average first-order decay constant of 0.084 h-1 (per hour). Results of field test plots indicated that substantial numbers of fecal bacteria present in poul try litter can survive in fields for as much as 8 weeks after the application of the litter to the land surface. Median densities of fecal coliform and E. coli in slurry-water samples collected from fields increased from less than 60 col/100 mL before the application of turkey and broiler litter, to as large as 420,000 and 290,000 col/100 mL after the application of litter. Bacteria densities in the test plots generally decreased in a exponential manner over time with decay rates ranging from 0.085 to 0.185 d-1 (per day) for fecal coliform to between 0.100 and 0.250 d-1 for E. coli. The apparent survival of significant numbers of fecal bacteria on fields where poultry litter has been applied indicates that runoff from these fields is a potential source of fecal bacteria to vicinity streams for many weeks following litter application.

  19. Survival of Lactobacillus rhamnosus strains in the upper gastrointestinal tract.

    Science.gov (United States)

    Pitino, Iole; Randazzo, Cinzia Lucia; Mandalari, Giuseppina; Lo Curto, Alberto; Faulks, Richard Martin; Le Marc, Yvan; Bisignano, Carlo; Caggia, Cinzia; Wickham, Martin Sean John

    2010-12-01

    In the present study six probiotic Lactobacillus rhamnosus strains were investigated for their ability to survive in the human upper gastrointestinal tract through a dynamic gastric model of digestion. MRS broth was used as delivery vehicle and survival was investigated during in vitro gastric and gastric plus duodenal digestion. Results highlighted that all tested strains showed good survival rate during both gastric and duodenal digestion. In particular, three strains exhibited a great survival showing a recovery percentage in the range between 117 and 276%. In agreement with survival data, high lactic acid production was detected for all strains, confirming their metabolic activity during digestion. Copyright © 2010 Elsevier Ltd. All rights reserved.

  20. Surviving a cluster collapse: risk aversion as a core value

    NARCIS (Netherlands)

    Schiele, Holger; Hospers, Gerrit J.; van der Zee, D.J.

    2012-01-01

    Purpose – This paper analyses firms, which survived in a collapsed regional cluster. The target is to analyze whether the principles for enduring success identified researching success factors of very old firms also apply in such an environment. Design/methodology/approach – The authors conduct a

  1. Survival of Sami cancer patients

    Directory of Open Access Journals (Sweden)

    Leena Soininen

    2012-07-01

    Full Text Available Objectives. The incidence of cancer among the indigenous Sami people of Northern Finland is lower than among the Finnish general population. The survival of Sami cancer patients is not known, and therefore it is the object of this study. Study design. The cohort consisted of 2,091 Sami and 4,161 non-Sami who lived on 31 December 1978 in the two Sami municipalities of Inari and Utsjoki, which are located in Northern Finland and are 300–500 km away from the nearest central hospital. The survival experience of Sami and non-Sami cancer patients diagnosed in this cohort during 1979–2009 was compared with that of the Finnish patients outside the cohort. Methods. The Sami and non-Sami cancer patients were matched to other Finnish cancer patients for gender, age and year of diagnosis and for the site of cancer. An additional matching was done for the stage at diagnosis. Cancer-specific survival analyses were made using the Kaplan–Meier method and Cox regression modelling. Results. There were 204 Sami and 391 non-Sami cancer cases in the cohort, 20,181 matched controls without matching with stage, and 7,874 stage-matched controls. In the cancer-specific analysis without stage variable, the hazard ratio for Sami was 1.05 (95% confidence interval 0.85–1.30 and for non-Sami 1.02 (0.86–1.20, indicating no difference between the survival of those groups and other patients in Finland. Likewise, when the same was done by also matching the stage, there was no difference in cancer survival. Conclusion. Long distances to medical care or Sami ethnicity have no influence on the cancer patient survival in Northern Finland.

  2. A two-stage model in a Bayesian framework to estimate a survival endpoint in the presence of confounding by indication.

    Science.gov (United States)

    Bellera, Carine; Proust-Lima, Cécile; Joseph, Lawrence; Richaud, Pierre; Taylor, Jeremy; Sandler, Howard; Hanley, James; Mathoulin-Pélissier, Simone

    2018-04-01

    Background Biomarker series can indicate disease progression and predict clinical endpoints. When a treatment is prescribed depending on the biomarker, confounding by indication might be introduced if the treatment modifies the marker profile and risk of failure. Objective Our aim was to highlight the flexibility of a two-stage model fitted within a Bayesian Markov Chain Monte Carlo framework. For this purpose, we monitored the prostate-specific antigens in prostate cancer patients treated with external beam radiation therapy. In the presence of rising prostate-specific antigens after external beam radiation therapy, salvage hormone therapy can be prescribed to reduce both the prostate-specific antigens concentration and the risk of clinical failure, an illustration of confounding by indication. We focused on the assessment of the prognostic value of hormone therapy and prostate-specific antigens trajectory on the risk of failure. Methods We used a two-stage model within a Bayesian framework to assess the role of the prostate-specific antigens profile on clinical failure while accounting for a secondary treatment prescribed by indication. We modeled prostate-specific antigens using a hierarchical piecewise linear trajectory with a random changepoint. Residual prostate-specific antigens variability was expressed as a function of prostate-specific antigens concentration. Covariates in the survival model included hormone therapy, baseline characteristics, and individual predictions of the prostate-specific antigens nadir and timing and prostate-specific antigens slopes before and after the nadir as provided by the longitudinal process. Results We showed positive associations between an increased prostate-specific antigens nadir, an earlier changepoint and a steeper post-nadir slope with an increased risk of failure. Importantly, we highlighted a significant benefit of hormone therapy, an effect that was not observed when the prostate-specific antigens trajectory was

  3. Correlation between the CT manifestations and post-operative survival time in patients with thymic epithelial tumor

    International Nuclear Information System (INIS)

    Chen Juan; Tan Ye; Wang Xiangyang; Du Jun; Pan Jishu; Wei Jiahu

    2011-01-01

    Objective: To describe the CT manifestations of thymic epithelial tumor and explore the correlation between CT findings and post-operative tumor-related survival time. Methods: Ninety-one patients who underwent CT scan before operation were reviewed retrospectively. All cases had operation and were classified according to the WHO classification. The size, contour, shape, density and enhancement of the tumors on CT were assessed. Presence of mediastinal lymphadenopathy, great vessel invasion, metastasis to the lung or plural, myasthenia gravis (MG) were also analyzed. The survival rate was obtained using, the Kaplan-Meier method. The Cox model was applied to determine the factors affecting the tumor-related survivals. Chi square test was used to analyze the relationship between CT findings and WHO classification. Results: Two patients were excluded because of dying of myocardial infarction and colon cancer. The total 5-year survival rate was 84.3% (n=75). Eighty-nine patients had total 91 tumors. Tumors with diameter larger than 5 cm, lobular contour, heterogenous density, and presence of great vessel invasion, mediastinal lymphadenopathy, and metastasis were adverse factors which could significantly affect the survival time. Five-year survival rates of these factors were 72.7%, 77.3%, 76.7%, 73.8%, 30.0%, and 68.8%, respectively. Presence of MG was a favorable factor which also significantly affected the survival time (P 0.05). The result of the Cox multivariate analysis was consistent with that of the Log-rank test. For different WHO classification, there were significant different among the size or contour of the tumors, presence of great vessel invasion, mediastinal lymphadenopathy, and metastasis (χ 2 value were 6.598, 5.737, 18.307, 8.465, and 15.608, respectively P<0.05). Conclusions: CT findings may be served as predictors of clinical prognosis of the thymic epithelial tumors. Adverse factors for survival time are the size of the tumors and presence of

  4. SU-F-R-04: Radiomics for Survival Prediction in Glioblastoma (GBM)

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, H; Molitoris, J; Bhooshan, N; Choi, W; Lu, W; Mehta, M; D’Souza, W [University of Maryland School of Medicine, Baltimore, MD (United States); Tan, S [Huazhong University of Science & Technology, Wuhan (China); Giacomelli, I; Scartoni, D [University of Florence, Florence (Italy); Gzell, C [Northern Sydney Cancer Centre, Sydney (Australia)

    2016-06-15

    Purpose: To develop a quantitative radiomics approach for survival prediction of glioblastoma (GBM) patients treated with chemoradiotherapy (CRT). Methods: 28 GBM patients who received CRT at our institution were retrospectively studied. 255 radiomic features were extracted from 3 gadolinium-enhanced T1 weighted MRIs for 2 regions of interest (ROIs) (the surgical cavity and its surrounding enhancement rim). The 3 MRIs were at pre-treatment, 1-month and 3-month post-CRT. The imaging features comprehensively quantified the intensity, spatial variation (texture), geometric property and their spatial-temporal changes for the 2 ROIs. 3 demographics features (age, race, gender) and 12 clinical parameters (KPS, extent of resection, whether concurrent temozolomide was adjusted/stopped and radiotherapy related information) were also included. 4 Machine learning models (logistic regression (LR), support vector machine (SVM), decision tree (DT), neural network (NN)) were applied to predict overall survival (OS) and progression-free survival (PFS). The number of cases and percentage of cases predicted correctly were collected and AUC (area under the receiver operating characteristic (ROC) curve) were determined after leave-one-out cross-validation. Results: From univariate analysis, 27 features (1 demographic, 1 clinical and 25 imaging) were statistically significant (p<0.05) for both OS and PFS. Two sets of features (each contained 24 features) were algorithmically selected from all features to predict OS and PFS. High prediction accuracy of OS was achieved by using NN (96%, 27 of 28 cases were correctly predicted, AUC = 0.99), LR (93%, 26 of 28 cases were correctly predicted, AUC = 0.95) and SVM (93%, 26 of 28 cases were correctly predicted, AUC = 0.90). When predicting PFS, NN obtained the highest prediction accuracy (89%, 25 of 28 cases were correctly predicted, AUC = 0.92). Conclusion: Radiomics approach combined with patients’ demographics and clinical parameters can

  5. PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer.

    Science.gov (United States)

    Wishart, Gordon C; Azzato, Elizabeth M; Greenberg, David C; Rashbass, Jem; Kearins, Olive; Lawrence, Gill; Caldas, Carlos; Pharoah, Paul D P

    2010-01-01

    The aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK. Using the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation. Differences in overall actual and predicted mortality were detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort.

  6. Are global and regional improvements in life expectancy and in child, adult and senior survival slowing?

    Directory of Open Access Journals (Sweden)

    Ryan J Hum

    Full Text Available Improvements in life expectancy have been considerable over the past hundred years. Forecasters have taken to applying historical trends under an assumption of continuing improvements in life expectancy in the future. A linear mixed effects model was used to estimate the trends in global and regional rates of improvements in life expectancy, child, adult, and senior survival, in 166 countries between 1950 and 2010. Global improvements in life expectancy, including both child and adult survival rates, decelerated significantly over the study period. Overall life expectancy gains were estimated to have declined from 5.9 to 4.0 months per year for a mean deceleration of -0.07 months/year2; annual child survival gains declined from 4.4 to 1.6 deaths averted per 1000 for a mean deceleration of -0.06 deaths/1000/year2; adult survival gains were estimated to decline from 4.8 to 3.7 deaths averted per 1000 per year for a mean deceleration of -0.08 deaths/1000/year2. Senior survival gains however increased from 2.4 to 4.2 deaths averted per 1000 per year for an acceleration of 0.03 deaths/1000/year2. Regional variation in the four measures was substantial. The rates of global improvements in life expectancy, child survival, and adult survival have declined since 1950 despite an increase in the rate of improvements among seniors. We postulate that low-cost innovation, related to the last half-century progress in health-primarily devoted to children and middle age, is reaping diminishing returns on its investments. Trends are uneven across regions and measures, which may be due in part to the state of epidemiological transition between countries and regions and disparities in the diffusion of innovation, accessible only in high-income countries where life expectancy is already highest.

  7. Complete hazard ranking to analyze right-censored data: An ALS survival study.

    Science.gov (United States)

    Huang, Zhengnan; Zhang, Hongjiu; Boss, Jonathan; Goutman, Stephen A; Mukherjee, Bhramar; Dinov, Ivo D; Guan, Yuanfang

    2017-12-01

    Survival analysis represents an important outcome measure in clinical research and clinical trials; further, survival ranking may offer additional advantages in clinical trials. In this study, we developed GuanRank, a non-parametric ranking-based technique to transform patients' survival data into a linear space of hazard ranks. The transformation enables the utilization of machine learning base-learners including Gaussian process regression, Lasso, and random forest on survival data. The method was submitted to the DREAM Amyotrophic Lateral Sclerosis (ALS) Stratification Challenge. Ranked first place, the model gave more accurate ranking predictions on the PRO-ACT ALS dataset in comparison to Cox proportional hazard model. By utilizing right-censored data in its training process, the method demonstrated its state-of-the-art predictive power in ALS survival ranking. Its feature selection identified multiple important factors, some of which conflicts with previous studies.

  8. The Value of Serum NR2 Antibody in Prediction of Post-Cardiopulmonary Resuscitation Survival

    Directory of Open Access Journals (Sweden)

    Ali Bidari

    2015-07-01

    Full Text Available Introduction: N-methyl-D-aspartate receptor subunits antibody (NR2-ab is a sensitive marker of ischemic brain damage in clinical circumstances, such as cerebrovascular accidents. We aimed to assess the value of serum NR2-ab in predicting the post-cardiopulmonary resuscitation (CPR survival. Methods: In this cohort study, we examined serum NR2-ab levels 1 hour after the return of spontaneous circulation (ROSC in 49 successfully resuscitated patients. Patients with traumatic or asphyxic arrests, prior neurological insults, or major medical illnesses were excluded. Participants were followed until death or hospital discharge. Demographic data, coronary artery disease risk factors, time before initiation of CPR, and CPR duration were documented.  In addition, Glasgow coma scale (GCS, blood pressure, and survival status of patients were recorded at 1, 6, 24, and 72 hour(s after ROSC. Descriptive analyses were performed, and the Cox proportional hazard model was applied to assess if NR2-ab level is an independent predictive factor of survival. Results: 49 successfully resuscitated patients were evaluated; 27 (55% survived to hospital discharge, 4 (8.1% were in vegetative state, 10 (20.4% were physically disabled, and 13 (26.5% were physically functional. Within 72 hours of ROSC all of the 12 NR2-ab positive patients died. In contrast, 31 (84% of the NR2-ab negative patients survived. Sensitivity, specificity, positive and negative likelihood ratios of NR2-ab in prediction of survival were 54.5% (95%CI=32.7%-74.9%, 100% (95%CI=84.5%-100%, infinite, and 45.5% (95%CI=28.8%-71.8%, respectively. Subsequent analysis showed that both NR2-ab status and GCS were independent risk factors of death. Conclusions: A positive NR2-ab serum test 1 hour after ROSC correlated with lower 72-hour survival. Further studies are required to validate this finding and demonstrate the value of a quantitative NR2-ab assay and its optimal time of measurement.

  9. Applying a Dynamic Resource Supply Model in a Smart Grid

    Directory of Open Access Journals (Sweden)

    Kaiyu Wan

    2014-09-01

    Full Text Available Dynamic resource supply is a complex issue to resolve in a cyber-physical system (CPS. In our previous work, a resource model called the dynamic resource supply model (DRSM has been proposed to handle resources specification, management and allocation in CPS. In this paper, we are integrating the DRSM with service-oriented architecture and applying it to a smart grid (SG, one of the most complex CPS examples. We give the detailed design of the SG for electricity charging request and electricity allocation between plug-in hybrid electric vehicles (PHEV and DRSM through the Android system. In the design, we explain a mechanism for electricity consumption with data collection and re-allocation through ZigBee network. In this design, we verify the correctness of this resource model for expected electricity allocation.

  10. Prognostic factors for survival in patients with colorectal liver metastases: experience of a single brazilian cancer center

    Directory of Open Access Journals (Sweden)

    Héber Salvador de Castro Ribeiro

    2012-12-01

    Full Text Available CONTEXT: Liver metastases are a common event in the clinical outcome of patients with colorectal cancer and account for 2/3 of deaths from this disease. There is considerable controversy among the data in the literature regarding the results of surgical treatment and prognostic factors of survival, and no analysis have been done in a large cohort of patients in Brazil. OBJECTIVES: To characterize the results of surgical treatment of patients with colorectal liver metastases, and to establish prognostic factors of survival in a Brazilian population. METHOD: This was a retrospective study of patients undergoing liver resection for colorectal metastases in a tertiary cancer hospital from 1998 to 2009. We analyzed epidemiologic variables and the clinical characteristics of primary tumors, metastatic disease and its treatment, surgical procedures and follow-up, and survival results. Survival analyzes were done by the Kaplan-Meier method and the log-rank test was applied to determine the influence of variables on overall and disease-free survival. All variables associated with survival with P<0.20 in univariate analysis, were included in multivariate analysis using a Cox proportional hazard regression model. RESULTS: During the period analyzed, 209 procedures were performed on 170 patients. Postope-rative mortality in 90 days was 2.9% and 5-year overall survival was 64.9%. Its independent prognostic factors were the presence of extrahepatic disease at diagnosis of liver metastases, bilateral nodules and the occurrence of major complications after liver surgery. The estimated 5-year disease-free survival was 39.1% and its prognostic factors included R1 resection, extrahepatic disease, bilateral nodules, lymph node involvement in the primary tumor and primary tumors located in the rectum. CONCLUSION: Liver resection for colorectal metastases is safe and effective and the analysis of prognostic factors of survival in a large cohort of Brazilian patients

  11. Guidelines for Applying Cohesive Models to the Damage Behaviour of Engineering Materials and Structures

    CERN Document Server

    Schwalbe, Karl-Heinz; Cornec, Alfred

    2013-01-01

    This brief provides guidance for the application of cohesive models to determine damage and fracture in materials and structural components. This can be done for configurations with or without a pre-existing crack. Although the brief addresses structural behaviour, the methods described herein may also be applied to any deformation induced material damage and failure, e.g. those occurring during manufacturing processes. The methods described are applicable to the behaviour of ductile metallic materials and structural components made thereof. Hints are also given for applying the cohesive model to other materials.

  12. Increased survival rate by local release of diclofenac in a murine model of recurrent oral carcinoma

    Directory of Open Access Journals (Sweden)

    Will OM

    2016-10-01

    Full Text Available Olga Maria Will,1,* Nicolai Purcz,2,* Athena Chalaris,3 Carola Heneweer,4,5 Susann Boretius,1 Larissa Purcz,2 Lila Nikkola,6 Nureddin Ashammakhi,6 Holger Kalthoff,7 Claus-Christian Glüer,1 Jörg Wiltfang,2 Yahya Açil,2 Sanjay Tiwari1 1Section Biomedical Imaging, Clinic for Radiology and Neuroradiology, MOIN CC, 2Department of Oral and Maxillofacial Surgery, University Hospital Schleswig-Holstein, 3Institute of Biochemistry, Christian-Albrechts-Universität zu Kiel, 4Clinic for Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Kiel, 5Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany; 6Department of Biomedical Engineering, Tampere University of Technology, Tampere, Finland; 7Institute for Experimental Cancer Research, University Hospital Schleswig-Holstein, Kiel, Germany *These authors contributed equally to this work Abstract: Despite aggressive treatment with radiation and combination chemotherapy following tumor resection, the 5-year survival rate for patients with head and neck cancer is at best only 50%. In this study, we examined the therapeutic potential of localized release of diclofenac from electrospun nanofibers generated from poly(d,l-lactide-co-glycolide polymer. Diclofenac was chosen since anti-inflammatory agents that inhibit cyclooxygenase have shown great potential in their ability to directly inhibit tumor growth as well as suppress inflammation-mediated tumor growth. A mouse resection model of oral carcinoma was developed by establishing tumor growth in the oral cavity by ultrasound-guided injection of 1 million SCC-9 cells in the floor of the mouth. Following resection, mice were allocated into four groups with the following treatment: 1 no treatment, 2 implanted scaffolds without diclofenac, 3 implanted scaffolds loaded with diclofenac, and 4 diclofenac given orally. Small animal ultrasound and magnetic resonance imaging were utilized for longitudinal

  13. Apoptosis and survival parameters during protection from radiation-induced thymocyte death by a candidate radioprotector, GC-2112, from Allium sativum

    International Nuclear Information System (INIS)

    Bunagan, J.; Perey, K.; Deocaris, C.C.

    1996-01-01

    Biomedical studies on nuclear fallout effects show that whole-body exposure to relatively low doses of ionizing radiation (2-10 Gy) induces the hematopoietic syndrome (HS) characterized by severe anemia and immunodeficiency and death within 10-30 days. The thymocyte model applies in many cell death researches and is found to undergo a morphologically and molecularly distinct p53-based apoptosis with DNA-damaging insults, such as radiation exposure. We have shown that exogenously applied radioprotector from allium sativum (garlic), GC-2112, improves total cellular survival for various observation periods concomitantly shifting the LD 50/24 from 7 Gy (control) to 21 Gy (GC-2112). This increased survival characteristic of the radioprotected macrophage-free thymocytes, however, fails to correlate with the prevention of apoptosis-associated DNA scissions. Mechanisms to the observed radiomodification may possibly involve cysteine compounds found rich in garlic. These preliminary findings show promise in the applications of selected herbal drugs as dietary prophylaxis against clinical morbidities arising from either medical, occupational or environmental exposures to ionizing radiation. (author)

  14. Apoptosis and survival parameters during protection from radiation-induced thymocyte death by a candidate radioprotector, GC-2112, from Allium sativum

    Energy Technology Data Exchange (ETDEWEB)

    Bunagan, J; Perey, K [Pamantasan ng Lungsod ng Maynila, Manila (Philippines); Deocaris, C C [Philippine Nuclear Research Inst., Diliman, Quezon City (Philippines)

    1997-12-31

    Biomedical studies on nuclear fallout effects show that whole-body exposure to relatively low doses of ionizing radiation (2-10 Gy) induces the hematopoietic syndrome (HS) characterized by severe anemia and immunodeficiency and death within 10-30 days. The thymocyte model applies in many cell death researches and is found to undergo a morphologically and molecularly distinct p53-based apoptosis with DNA-damaging insults, such as radiation exposure. We have shown that exogenously applied radioprotector from allium sativum (garlic), GC-2112, improves total cellular survival for various observation periods concomitantly shifting the LD{sub 50/24} from 7 Gy (control) to 21 Gy (GC-2112). This increased survival characteristic of the radioprotected macrophage-free thymocytes, however, fails to correlate with the prevention of apoptosis-associated DNA scissions. Mechanisms to the observed radiomodification may possibly involve cysteine compounds found rich in garlic. These preliminary findings show promise in the applications of selected herbal drugs as dietary prophylaxis against clinical morbidities arising from either medical, occupational or environmental exposures to ionizing radiation. (author).

  15. Biological effectiveness and application of heavy ions in radiation therapy described by a physical and biological model

    International Nuclear Information System (INIS)

    Olsen, K.J.; Hansen, J.W.

    1982-12-01

    A description is given of the physical basis for applying track structure theory in the determination of the effectiveness of heavy-ion irradiation of single- and multi-hit target systems. It will be shown that for applying the theory to biological systems the effectiveness of heavy-ion irradiation is inadequately described by an RBE-factor, whereas the complete formulation of the probability of survival must be used, as survival depends on both radiation quality and dose. The theoretical model of track structure can be used in dose-effect calculations for neutron-, high-LET, and low-LET radiation applied simultaneously in therapy. (author)

  16. Aggregate Demand–Inflation Adjustment Model Applied to Southeast European Economies

    Directory of Open Access Journals (Sweden)

    Apostolov Mico

    2016-01-01

    Full Text Available Applying IS-MP-IA model and the Taylor rule to selected Southeast European economies (Albania, Bosnia and Herzegovina, Macedonia and Serbia we find that the change of effective exchange rate positively affects output, while the change of the world interest rate negatively affects output or it does not affect the output at all, and additional world output would help to increase output of the selected economies.

  17. Log-normal frailty models fitted as Poisson generalized linear mixed models.

    Science.gov (United States)

    Hirsch, Katharina; Wienke, Andreas; Kuss, Oliver

    2016-12-01

    The equivalence of a survival model with a piecewise constant baseline hazard function and a Poisson regression model has been known since decades. As shown in recent studies, this equivalence carries over to clustered survival data: A frailty model with a log-normal frailty term can be interpreted and estimated as a generalized linear mixed model with a binary response, a Poisson likelihood, and a specific offset. Proceeding this way, statistical theory and software for generalized linear mixed models are readily available for fitting frailty models. This gain in flexibility comes at the small price of (1) having to fix the number of pieces for the baseline hazard in advance and (2) having to "explode" the data set by the number of pieces. In this paper we extend the simulations of former studies by using a more realistic baseline hazard (Gompertz) and by comparing the model under consideration with competing models. Furthermore, the SAS macro %PCFrailty is introduced to apply the Poisson generalized linear mixed approach to frailty models. The simulations show good results for the shared frailty model. Our new %PCFrailty macro provides proper estimates, especially in case of 4 events per piece. The suggested Poisson generalized linear mixed approach for log-normal frailty models based on the %PCFrailty macro provides several advantages in the analysis of clustered survival data with respect to more flexible modelling of fixed and random effects, exact (in the sense of non-approximate) maximum likelihood estimation, and standard errors and different types of confidence intervals for all variance parameters. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  18. Instrumental variable estimation in a survival context

    DEFF Research Database (Denmark)

    Tchetgen Tchetgen, Eric J; Walter, Stefan; Vansteelandt, Stijn

    2015-01-01

    for regression analysis in a survival context, primarily under an additive hazards model, for which we describe 2 simple methods for estimating causal effects. The first method is a straightforward 2-stage regression approach analogous to 2-stage least squares commonly used for IV analysis in linear regression....... The IV approach is very well developed in the context of linear regression and also for certain generalized linear models with a nonlinear link function. However, IV methods are not as well developed for regression analysis with a censored survival outcome. In this article, we develop the IV approach....... In this approach, the fitted value from a first-stage regression of the exposure on the IV is entered in place of the exposure in the second-stage hazard model to recover a valid estimate of the treatment effect of interest. The second method is a so-called control function approach, which entails adding...

  19. Sex Steroid Hormone Receptor Expression Affects Ovarian Cancer Survival

    DEFF Research Database (Denmark)

    Jönsson, Jenny-Maria; Skovbjerg Arildsen, Nicolai; Malander, Susanne

    2015-01-01

    BACKGROUND AND AIMS: Although most ovarian cancers express estrogen (ER), progesterone (PR), and androgen (AR) receptors, they are currently not applied in clinical decision making. We explored the prognostic impact of sex steroid hormone receptor protein and mRNA expression on survival...... in epithelial ovarian cancer. METHODS: Immunohistochemical stainings for ERα, ERβ, PR, and AR were assessed in relation to survival in 118 serous and endometrioid ovarian cancers. Expression of the genes encoding the four receptors was studied in relation to prognosis in the molecular subtypes of ovarian cancer...... in ovarian cancer and support that tumors should be stratified based on molecular as well as histological subtypes in future studies investigating the role of endocrine treatment in ovarian cancer....

  20. Survival and Neurodevelopmental Outcomes among Periviable Infants.

    Science.gov (United States)

    Younge, Noelle; Goldstein, Ricki F; Bann, Carla M; Hintz, Susan R; Patel, Ravi M; Smith, P Brian; Bell, Edward F; Rysavy, Matthew A; Duncan, Andrea F; Vohr, Betty R; Das, Abhik; Goldberg, Ronald N; Higgins, Rosemary D; Cotten, C Michael

    2017-02-16

    Data reported during the past 5 years indicate that rates of survival have increased among infants born at the borderline of viability, but less is known about how increased rates of survival among these infants relate to early childhood neurodevelopmental outcomes. We compared survival and neurodevelopmental outcomes among infants born at 22 to 24 weeks of gestation, as assessed at 18 to 22 months of corrected age, across three consecutive birth-year epochs (2000-2003 [epoch 1], 2004-2007 [epoch 2], and 2008-2011 [epoch 3]). The infants were born at 11 centers that participated in the National Institute of Child Health and Human Development Neonatal Research Network. The primary outcome measure was a three-level outcome - survival without neurodevelopmental impairment, survival with neurodevelopmental impairment, or death. After accounting for differences in infant characteristics, including birth center, we used multinomial generalized logit models to compare the relative risk of survival without neurodevelopmental impairment, survival with neurodevelopmental impairment, and death. Data on the primary outcome were available for 4274 of 4458 infants (96%) born at the 11 centers. The percentage of infants who survived increased from 30% (424 of 1391 infants) in epoch 1 to 36% (487 of 1348 infants) in epoch 3 (Pneurodevelopmental impairment increased from 16% (217 of 1391) in epoch 1 to 20% (276 of 1348) in epoch 3 (P=0.001), whereas the percentage of infants who survived with neurodevelopmental impairment did not change significantly (15% [207 of 1391] in epoch 1 and 16% [211 of 1348] in epoch 3, P=0.29). After adjustment for changes in the baseline characteristics of the infants over time, both the rate of survival with neurodevelopmental impairment (as compared with death) and the rate of survival without neurodevelopmental impairment (as compared with death) increased over time (adjusted relative risks, 1.27 [95% confidence interval {CI}, 1.01 to 1.59] and 1

  1. Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models.

    Science.gov (United States)

    Martinussen, Torben; Vansteelandt, Stijn; Tchetgen Tchetgen, Eric J; Zucker, David M

    2017-12-01

    The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity may be attributed to the natural occurrence of instrumental variables in observational studies that incorporate elements of randomization, either by design or by nature (e.g., random inheritance of genes). Instrumental variables estimation of exposure effects is well established for continuous outcomes and to some extent for binary outcomes. It is, however, largely lacking for time-to-event outcomes because of complications due to censoring and survivorship bias. In this article, we make a novel proposal under a class of structural cumulative survival models which parameterize time-varying effects of a point exposure directly on the scale of the survival function; these models are essentially equivalent with a semi-parametric variant of the instrumental variables additive hazards model. We propose a class of recursive instrumental variable estimators for these exposure effects, and derive their large sample properties along with inferential tools. We examine the performance of the proposed method in simulation studies and illustrate it in a Mendelian randomization study to evaluate the effect of diabetes on mortality using data from the Health and Retirement Study. We further use the proposed method to investigate potential benefit from breast cancer screening on subsequent breast cancer mortality based on the HIP-study. © 2017, The International Biometric Society.

  2. The development of a curved beam element model applied to finite elements method

    International Nuclear Information System (INIS)

    Bento Filho, A.

    1980-01-01

    A procedure for the evaluation of the stiffness matrix for a thick curved beam element is developed, by means of the minimum potential energy principle, applied to finite elements. The displacement field is prescribed through polynomial expansions, and the interpolation model is determined by comparison of results obtained by the use of a sample of different expansions. As a limiting case of the curved beam, three cases of straight beams, with different dimensional ratios are analised, employing the approach proposed. Finally, an interpolation model is proposed and applied to a curved beam with great curvature. Desplacements and internal stresses are determined and the results are compared with those found in the literature. (Author) [pt

  3. Survival of Alzheimer's disease patients in Korea.

    Science.gov (United States)

    Go, Seok Min; Lee, Kang Soo; Seo, Sang Won; Chin, Juhee; Kang, Sue J; Moon, So Young; Na, Duk L; Cheong, Hae-Kwan

    2013-01-01

    The natural history of Alzheimer's disease (AD) has rarely been studied in the Korean population. Our study on survival analyses in Korean AD patients potentially provides a basis for cross-cultural comparisons. We studied 724 consecutive patients from a memory disorder clinic in a tertiary hospital in Seoul, who were diagnosed as having AD between April 1995 and December 2005. Deaths were identified by the Statistics Korea database. The Kaplan-Meier method was used for survival analysis, and a Cox proportional hazard model was used to assess factors related to patient survival. The overall median survival from the onset of first symptoms and from the time of diagnosis was 12.6 years (95% confidence interval 11.7-13.4) and 9.3 years (95% confidence interval 8.7-9.9), respectively. The age of onset, male gender, history of diabetes mellitus, lower Mini-Mental State Examination score, and higher Clinical Dementia Rating score were negatively associated with survival. There was a reversal of risk of AD between early-onset and later-onset AD, 9.1 years after onset. The results of our study show a different pattern of survival compared to those studies carried out with western AD populations. Mortality risk of early-onset AD varied depending on the duration of follow-up. Copyright © 2013 S. Karger AG, Basel.

  4. Does HPV status influence survival after vulvar cancer?

    DEFF Research Database (Denmark)

    Rasmussen, Christina Louise; Sand, Freja Laerke; Hoffmann Frederiksen, Marie

    2018-01-01

    High-risk human papillomavirus (HPV) infection is essential in the carcinogenesis of a substantial part of anogenital and oropharyngeal cancers and has additionally been shown to be a possible predictive marker for survival, especially in oropharyngeal cancer. Studies examining the influence of HPV...... status on survival after vulvar cancer have been conflicting and limited by small study populations. Therefore, the aim of this review and meta-analysis was to examine whether HPV status influences survival after vulvar cancer, which, to our knowledge, has not been done before. We conducted a systematic...... search of PubMed, Cochrane Library and Embase to identify studies examining survival after histologically verified and HPV tested vulvar cancer. A total of 18 studies were eligible for inclusion. Study-specific and pooled HRs of the 5-year OS and DFS were calculated using a fixed effects model. The I2...

  5. Track structure model of cell damage in space flight

    Science.gov (United States)

    Katz, Robert; Cucinotta, Francis A.; Wilson, John W.; Shinn, Judy L.; Ngo, Duc M.

    1992-01-01

    The phenomenological track-structure model of cell damage is discussed. A description of the application of the track-structure model with the NASA Langley transport code for laboratory and space radiation is given. Comparisons to experimental results for cell survival during exposure to monoenergetic, heavy-ion beams are made. The model is also applied to predict cell damage rates and relative biological effectiveness for deep-space exposures.

  6. On the concept of survivability, with application to spacecraft and space-based networks

    International Nuclear Information System (INIS)

    Castet, Jean-Francois; Saleh, Joseph H.

    2012-01-01

    Survivability is an important attribute and requirement for military systems. Recently, survivability has become increasingly important for public infrastructure systems as well. In this work, we bring considerations of survivability to bear on space systems. We develop a conceptual framework and quantitative analyses based on stochastic Petri nets (SPN) to characterize and compare the survivability of different space architectures. The architectures here considered are a monolith spacecraft and a space-based network. To build the stochastic Petri net models for the degradations and failures of these two architectures, we conducted statistical analyses of historical multi-state failure data of spacecraft subsystems, and we assembled these subsystems, and their SPN models, in ways to create our monolith and networked systems. Preliminary results indicate, and quantify the extent to which, a space-based network is more survivable than the monolith spacecraft with respect to on-orbit anomalies and failures. For space systems, during the design and acquisition process, different architectures are benchmarked against several metrics; we argue that if survivability is not accounted for, then the evaluation process is likely to be biased in favor of the traditional dominant design, namely the monolith spacecraft. If however in a given context, survivability is a critical requirement for a customer, the survivability framework here proposed, and the stochastic modeling capability developed, can demonstrate the extent to which a networked space architecture may better satisfy this requirement than a monolith spacecraft. These results should be of interest to operators whose space assets require high levels of survivability, especially in the light of emerging threats.

  7. Immediate survival focus: synthesizing life history theory and dual process models to explain substance use.

    Science.gov (United States)

    Richardson, George B; Hardesty, Patrick

    2012-01-01

    Researchers have recently applied evolutionary life history theory to the understanding of behaviors often conceived of as prosocial or antisocial. In addition, researchers have applied cognitive science to the understanding of substance use and used dual process models, where explicit cognitive processes are modeled as relatively distinct from implicit cognitive processes, to explain and predict substance use behaviors. In this paper we synthesized these two theoretical perspectives to produce an adaptive and cognitive framework for explaining substance use. We contend that this framework provides new insights into the nature of substance use that may be valuable for both clinicians and researchers.

  8. Complete hazard ranking to analyze right-censored data: An ALS survival study.

    Directory of Open Access Journals (Sweden)

    Zhengnan Huang

    2017-12-01

    Full Text Available Survival analysis represents an important outcome measure in clinical research and clinical trials; further, survival ranking may offer additional advantages in clinical trials. In this study, we developed GuanRank, a non-parametric ranking-based technique to transform patients' survival data into a linear space of hazard ranks. The transformation enables the utilization of machine learning base-learners including Gaussian process regression, Lasso, and random forest on survival data. The method was submitted to the DREAM Amyotrophic Lateral Sclerosis (ALS Stratification Challenge. Ranked first place, the model gave more accurate ranking predictions on the PRO-ACT ALS dataset in comparison to Cox proportional hazard model. By utilizing right-censored data in its training process, the method demonstrated its state-of-the-art predictive power in ALS survival ranking. Its feature selection identified multiple important factors, some of which conflicts with previous studies.

  9. Biological Effectiveness and Application of Heavy Ions in Radiation Therapy Described by a Physical and Biological Model

    DEFF Research Database (Denmark)

    Olsen, Kjeld J.; Hansen, Johnny W.

    is inadequately described by an RBE-factor, whereas the complete formulation of the probability of survival must be used, as survival depends on both radiation quality and dose. The theoretical model of track structure can be used in dose-effect calculations for neutron-, high-LET, and low-LET radiation applied...... simultaneously in therapy....

  10. A cytogenetic model predicts relapse risk and survival in patients with acute myeloid leukemia undergoing hematopoietic stem cell transplantation in morphologic complete remission.

    Science.gov (United States)

    Rashidi, Armin; Cashen, Amanda F

    2015-01-01

    Up to 30% of patients with acute myeloid leukemia (AML) and abnormal cytogenetics have persistent cytogenetic abnormalities (pCytAbnl) at morphologic complete remission (mCR). We hypothesized that the prognostic significance of pCytAbnl in patients undergoing allogeneic hematopoietic stem cell transplantation (HSCT) in mCR varies with cytogenetic risk group. We analyzed the data on 118 patients with AML and abnormal cytogenetics who underwent HSCT in mCR, and developed a risk stratification model based on pCytAbnl and cytogenetic risk group. The model distinguished three groups of patients (Pcytogenetics (n=25) had the shortest median time to relapse (TTR; 5 months), relapse-free survival (RFS; 3 months), and overall survival (OS; 7 months). The group with favorable/intermediate risk cytogenetics and without pCytAbnl (n=43) had the longest median TTR (not reached), RFS (57 months), and OS (57 months). The group with pCytAbnl and favorable/intermediate risk cytogenetics, or, without pCytAbnl but with unfavorable risk cytogenetics (n=50) experienced intermediate TTR (18 months), RFS (9 months), and OS (18 months). In conclusion, a cytogenetic risk model identifies patients with AML in mCR with distinct rates of relapse and survival following HSCT. Copyright © 2014 Elsevier Ltd. All rights reserved.

  11. Clinical variables serve as prognostic factors in a model for survival from glioblastoma multiforme

    DEFF Research Database (Denmark)

    Michaelsen, Signe Regner; Christensen, Ib Jarle; Grunnet, Kirsten

    2013-01-01

    Although implementation of temozolomide (TMZ) as a part of primary therapy for glioblastoma multiforme (GBM) has resulted in improved patient survival, the disease is still incurable. Previous studies have correlated various parameters to survival, although no single parameter has yet been...

  12. Lovaas Model of Applied Behavior Analysis. What Works Clearinghouse Intervention Report

    Science.gov (United States)

    What Works Clearinghouse, 2010

    2010-01-01

    The "Lovaas Model of Applied Behavior Analysis" is a type of behavioral therapy that initially focuses on discrete trials: brief periods of one-on-one instruction, during which a teacher cues a behavior, prompts the appropriate response, and provides reinforcement to the child. Children in the program receive an average of 35 to 40 hours…

  13. Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae

    Directory of Open Access Journals (Sweden)

    Terezinha Aparecida Guedes

    2014-09-01

    Full Text Available The objective of this analysis was to fit germination data of Rhipsalis cereuscula Haw seeds to the Weibull model with three parameters using Frequentist and Bayesian methods. Five parameterizations were compared using the Bayesian analysis to fit a prior distribution. The parameter estimates from the Frequentist method were similar to the Bayesian responses considering the following non-informative a priori distribution for the parameter vectors: gamma (10³, 10³ in the model M1, normal (0, 106 in the model M2, uniform (0, Lsup in the model M3, exp (μ in the model M4 and Lnormal (μ, 106 in the model M5. However, to achieve the convergence in the models M4 and M5, we applied the μ from the estimates of the Frequentist approach. The best models fitted by the Bayesian method were the M1 and M3. The adequacy of these models was based on the advantages over the Frequentist method such as the reduced computational efforts and the possibility of comparison.

  14. Support vector methods for survival analysis: a comparison between ranking and regression approaches.

    Science.gov (United States)

    Van Belle, Vanya; Pelckmans, Kristiaan; Van Huffel, Sabine; Suykens, Johan A K

    2011-10-01

    To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods

  15. Prognostic and survival analysis of 837 Chinese colorectal cancer patients.

    Science.gov (United States)

    Yuan, Ying; Li, Mo-Dan; Hu, Han-Guang; Dong, Cai-Xia; Chen, Jia-Qi; Li, Xiao-Fen; Li, Jing-Jing; Shen, Hong

    2013-05-07

    To develop a prognostic model to predict survival of patients with colorectal cancer (CRC). Survival data of 837 CRC patients undergoing surgery between 1996 and 2006 were collected and analyzed by univariate analysis and Cox proportional hazard regression model to reveal the prognostic factors for CRC. All data were recorded using a standard data form and analyzed using SPSS version 18.0 (SPSS, Chicago, IL, United States). Survival curves were calculated by the Kaplan-Meier method. The log rank test was used to assess differences in survival. Univariate hazard ratios and significant and independent predictors of disease-specific survival and were identified by Cox proportional hazard analysis. The stepwise procedure was set to a threshold of 0.05. Statistical significance was defined as P analysis suggested age, preoperative obstruction, serum carcinoembryonic antigen level at diagnosis, status of resection, tumor size, histological grade, pathological type, lymphovascular invasion, invasion of adjacent organs, and tumor node metastasis (TNM) staging were positive prognostic factors (P analysis showed a significant statistical difference in 3-year survival among these groups: LNR1, 73%; LNR2, 55%; and LNR3, 42% (P analysis results showed that histological grade, depth of bowel wall invasion, and number of metastatic lymph nodes were the most important prognostic factors for CRC if we did not consider the interaction of the TNM staging system (P < 0.05). When the TNM staging was taken into account, histological grade lost its statistical significance, while the specific TNM staging system showed a statistically significant difference (P < 0.0001). The overall survival of CRC patients has improved between 1996 and 2006. LNR is a powerful factor for estimating the survival of stage III CRC patients.

  16. Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores.

    Science.gov (United States)

    Houthooft, Rein; Ruyssinck, Joeri; van der Herten, Joachim; Stijven, Sean; Couckuyt, Ivo; Gadeyne, Bram; Ongenae, Femke; Colpaert, Kirsten; Decruyenaere, Johan; Dhaene, Tom; De Turck, Filip

    2015-03-01

    The length of stay of critically ill patients in the intensive care unit (ICU) is an indication of patient ICU resource usage and varies considerably. Planning of postoperative ICU admissions is important as ICUs often have no nonoccupied beds available. Estimation of the ICU bed availability for the next coming days is entirely based on clinical judgement by intensivists and therefore too inaccurate. For this reason, predictive models have much potential for improving planning for ICU patient admission. Our goal is to develop and optimize models for patient survival and ICU length of stay (LOS) based on monitored ICU patient data. Furthermore, these models are compared on their use of sequential organ failure (SOFA) scores as well as underlying raw data as input features. Different machine learning techniques are trained, using a 14,480 patient dataset, both on SOFA scores as well as their underlying raw data values from the first five days after admission, in order to predict (i) the patient LOS, and (ii) the patient mortality. Furthermore, to help physicians in assessing the prediction credibility, a probabilistic model is tailored to the output of our best-performing model, assigning a belief to each patient status prediction. A two-by-two grid is built, using the classification outputs of the mortality and prolonged stay predictors to improve the patient LOS regression models. For predicting patient mortality and a prolonged stay, the best performing model is a support vector machine (SVM) with GA,D=65.9% (area under the curve (AUC) of 0.77) and GS,L=73.2% (AUC of 0.82). In terms of LOS regression, the best performing model is support vector regression, achieving a mean absolute error of 1.79 days and a median absolute error of 1.22 days for those patients surviving a nonprolonged stay. Using a classification grid based on the predicted patient mortality and prolonged stay, allows more accurate modeling of the patient LOS. The detailed models allow to support

  17. EGFR inhibitor erlotinib delays disease progression but does not extend survival in the SOD1 mouse model of ALS.

    Directory of Open Access Journals (Sweden)

    Claire E Le Pichon

    Full Text Available Amyotrophic lateral sclerosis (ALS is a fatal neurodegenerative disease that causes progressive paralysis due to motor neuron death. Several lines of published evidence suggested that inhibition of epidermal growth factor receptor (EGFR signaling might protect neurons from degeneration. To test this hypothesis in vivo, we treated the SOD1 transgenic mouse model of ALS with erlotinib, an EGFR inhibitor clinically approved for oncology indications. Although erlotinib failed to extend ALS mouse survival it did provide a modest but significant delay in the onset of multiple behavioral measures of disease progression. However, given the lack of protection of motor neuron synapses and the lack of survival extension, the small benefits observed after erlotinib treatment appear purely symptomatic, with no modification of disease course.

  18. Recipient bone marrow-derived stromal cells prolong graft survival in a rat hind limb allotransplantation model.

    Science.gov (United States)

    Ikeguchi, Ryosuke; Kakinoki, Ryosuke; Ohta, Souichi; Oda, Hiroki; Yurie, Hirofumi; Kaizawa, Yukitoshi; Mitsui, Hiroto; Aoyama, Tomoki; Toguchida, Junya; Matsuda, Shuichi

    2017-09-01

    Recent studies have indicated that bone marrow-derived stromal cells (BMSCs) have immunomodulatory properties that suppress the T cell responses that cause graft rejection. The purpose of this study is to evaluate the effect of recipient BMSCs intravenous infusion for immunomodulation in a rat vascularized composite allotransplantation model. A total of nine Wistar (WIS) rats and thirty Lewis (LEW) rats were used. BMSCs were harvested from three LEW rats. Twenty-four LEW rats were used as recipients and divided randomly into four groups: BMSC group, FK group, UT group, and Iso group. In the BMSC group, orthotopic rat hind limb transplantation was performed between WIS donor and LEW recipient rats. Recipient rats were injected intravenously with 2 × 10 6 recipient BMSCs on day 6, and with 0.2 mg/kg/day tacrolimus administered over 7 days (n = 6). In the FK group, recipient rats were treated with tacrolimus alone (n = 6). Rats in the UT group received no immunosuppressive treatment (n = 6). In the Iso group, transplantation was performed from three LEW donor rats to six LEW recipient rats without any immunosuppressive treatment (n = 6). Graft survival was assessed by daily inspection and histology. The immunological reactions of recipients were also evaluated. The graft survival of recipient rats in the BMSC group (24.5 days) was significantly prolonged in comparison with that of the FK group (18 days) (P Recipient rats in the BMSC group had significantly reduced serum IFN-γ cytokine levels (1.571 ± 0.779 pg/ml) in comparison with that of the FK group (7.059 ± 1.522 pg/ml) (P = .001). In in vitro study, BMSCs induce T cell hyporesponsiveness in a mixed lymphocyte reaction. BMSCs induce T cell hyporesponsiveness and prolong graft survival in the rat vascularized composite allotransplantation model. BMSCs exhibit immunomodulatory properties against acute rejection that can be realized without the need for significant recipient

  19. Effect of Electroacupuncture at The Zusanli Point (Stomach-36) on Dorsal Random Pattern Skin Flap Survival in a Rat Model.

    Science.gov (United States)

    Wang, Li-Ren; Cai, Le-Yi; Lin, Ding-Sheng; Cao, Bin; Li, Zhi-Jie

    2017-10-01

    Random skin flaps are commonly used for wound repair and reconstruction. Electroacupuncture at The Zusanli point could enhance microcirculation and blood perfusion in random skin flaps. To determine whether electroacupuncture at The Zusanli point can improve the survival of random skin flaps in a rat model. Thirty-six male Sprague Dawley rats were randomly divided into 3 groups: control group (no electroacupuncture), Group A (electroacupuncture at a nonacupoint near The Zusanli point), and Group B (electroacupuncture at The Zusanli point). McFarlane flaps were established. On postoperative Day 2, malondialdehyde (MDA) and superoxide dismutase were detected. The flap survival rate was evaluated, inflammation was examined in hematoxylin and eosin-stained slices, and the expression of vascular endothelial growth factor (VEGF) was measured immunohistochemically on Day 7. The mean survival area of the flaps in Group B was significantly larger than that in the control group and Group A. Superoxide dismutase activity and VEGF expression level were significantly higher in Group B than those in the control group and Group A, whereas MDA and inflammation levels in Group B were significantly lower than those in the other 2 groups. Electroacupuncture at The Zusanli point can effectively improve the random flap survival.

  20. Probiotics improve survival of septic rats by suppressing conditioned pathogens in ascites

    Science.gov (United States)

    Liu, Da-Quan; Gao, Qiao-Ying; Liu, Hong-Bin; Li, Dong-Hua; Wu, Shang-Wei

    2013-01-01

    AIM: To investigate the benefits of probiotics treatment in septic rats. METHODS: The septic rats were induced by cecal ligation and puncture. The animals of control, septic model and probiotics treated groups were treated with vehicle and mixed probiotics, respectively. The mixture of probiotics included Bifidobacterium longum, Lactobacillus bulgaricus and Streptococcus thermophilus. We observed the survival of septic rats using different amounts of mixed probiotics. We also detected the bacterial population in ascites and blood of experimental sepsis using cultivation and real-time polymerase chain reaction. The severity of mucosal inflammation in colonic tissues was determined. RESULTS: Probiotics treatment improved survival of the rats significantly and this effect was dose dependent. The survival rate was 30% for vehicle-treated septic model group. However, 1 and 1/4 doses of probiotics treatment increased survival rate significantly compared with septic model group (80% and 55% vs 30%, P probiotics treated group compared with septic model group (5.20 ± 0.57 vs 9.81 ± 0.67, P probiotics treated group compared with septic model group (33.3% vs 100.0%, P probiotics treated group were decreased significantly compared with that of septic model group (3.93 ± 0.73 vs 8.80 ± 0.83, P probiotics treatment, there was a decrease in the scores of inflammatory cell infiltration into the intestinal mucosa in septic animals (1.50 ± 0.25 vs 2.88 ± 0.14, P Probiotics improve survival of septic rats by suppressing these conditioned pathogens. PMID:23840152