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.

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...

Linking age, survival, and transit time distributions

Science.gov (United States)

Calabrese, Salvatore; Porporato, Amilcare

2015-10-01

Although the concepts of age, survival, and transit time have been widely used in many fields, including population dynamics, chemical engineering, and hydrology, a comprehensive mathematical framework is still missing. Here we discuss several relationships among these quantities by starting from the evolution equation for the joint distribution of age and survival, from which the equations for age and survival time readily follow. It also becomes apparent how the statistical dependence between age and survival is directly related to either the age dependence of the loss function or the survival-time dependence of the input function. The solution of the joint distribution equation also allows us to obtain the relationships between the age at exit (or death) and the survival time at input (or birth), as well as to stress the symmetries of the various distributions under time reversal. The transit time is then obtained as a sum of the age and survival time, and its properties are discussed along with the general relationships between their mean values. The special case of steady state case is analyzed in detail. Some examples, inspired by hydrologic applications, are presented to illustrate the theory with the specific results. This article was corrected on 11 Nov 2015. See the end of the full text for details.

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

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.

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

Fledgling survival increases with development time and adult survival across north and south temperate zones

Science.gov (United States)

Lloyd, Penn; Martin, Thomas E.

2016-01-01

Slow life histories are characterized by high adult survival and few offspring, which are thought to allow increased investment per offspring to increase juvenile survival. Consistent with this pattern, south temperate zone birds are commonly longer-lived and have fewer young than north temperate zone species. However, comparative analyses of juvenile survival, including during the first few weeks of the post-fledging period when most juvenile mortality occurs, are largely lacking. We combined our measurements of fledgling survival for eight passerines in South Africa with estimates from published studies of 57 north and south temperate zone songbird species to test three predictions: (1) fledgling survival increases with length of development time in the nest; (2) fledgling survival increases with adult survival and reduced brood size controlled for development time; and (3) south temperate zone species, with their higher adult survival and smaller brood sizes, exhibit higher fledgling survival than north temperate zone species controlled for development time. We found that fledgling survival was higher among south temperate zone species and generally increased with development time and adult survival within and between latitudinal regions. Clutch size did not explain additional variation, but was confounded with adult survival. Given the importance of age-specific mortality to life history evolution, understanding the causes of these geographical patterns of mortality is important.

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.

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions.

Science.gov (United States)

Kaneko, Gen; Yoshinaga, Tatsuki; Gribble, Kristin E; Welch, David M; Ushio, Hideki

2016-07-22

Rotifers are microscopic cosmopolitan zooplankton used as models in ecotoxicological and aging studies due to their several advantages such as short lifespan, ease of culture, and parthenogenesis that enables clonal culture. However, caution is required when measuring their survival time as it is affected by maternal age and maternal feeding conditions. Here we provide a protocol for powerful and reproducible measurement of the survival time in Brachionus rotifers following a careful synchronization of culture conditions over several generations. Empirically, poor synchronization results in early mortality and a gradual decrease in survival rate, thus resulting in weak statistical power. Indeed, under such conditions, calorie restriction (CR) failed to significantly extend the lifespan of B. plicatilis although CR-induced longevity has been demonstrated with well-synchronized rotifer samples in past and present studies. This protocol is probably useful for other invertebrate models, including the fruitfly Drosophila melanogaster and the nematode Caenorhabditis elegans, because maternal age effects have also been reported in these species.

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...

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...

Increasing survival time decreases the cost-effectiveness of using "test & treat'' to eliminate HIV epidemics.

Science.gov (United States)

Wagner, Bradley G; Coburn, Brian J; Blower, Sally

2013-01-01

Treating HIV-infected individuals reduces their viral load, consequently increasing their survival time and decreasing their infectivity. It has been proposed that universal testing and treatment (i.e., universal "test & treat'') could lead to HIV elimination and would be extremely cost-effective. It is now being debated whether to use a universal "test & treat'' approach in the "real-world'' as a prevention strategy to control HIV epidemics. However current modeling predictions of the impact, and cost-effectiveness, of universal `"est & treat'' strategies are based on an unrealistically short survival time for treated individuals. Here we use mathematical modeling and a longer, more realistic, survival time. We model the potential impact of a universal "test & treat'' strategy in South Africa. Our results show that increasing the length of the survival time on treatment, although beneficial to individuals, reduces the probability of eliminating HIV and decreases the cost-effectiveness of using universal "test & treat'' strategies. Therefore our results show that individual-level benefits and public health benefits will conflict when using "test &treat'' strategies to reduce HIV transmission.

Integral Time and the Varieties of Post-Mortem Survival

Directory of Open Access Journals (Sweden)

Sean M. Kelly

2008-06-01

Full Text Available While the question of survival of bodily death is usually approached byfocusing on the mind/body relation (and often with the idea of the soul as a special kindof substance, this paper explores the issue in the context of our understanding of time.The argument of the paper is woven around the central intuition of time as an “everlivingpresent.” The development of this intuition allows for a more integral or “complexholistic”theory of time, the soul, and the question of survival. Following the introductorymatter, the first section proposes a re-interpretation of Nietzsche’s doctrine of eternalrecurrence in terms of moments and lives as “eternally occurring.” The next section is atreatment of Julian Barbour’s neo-Machian model of instants of time as configurations inthe n-dimensional phase-space he calls “Platonia.” While rejecting his claim to have doneaway with time, I do find his model suggestive of the idea of moments and lives aseternally occurring. The following section begins with Fechner’s visionary ideas of thenature of the soul and its survival of bodily death, with particular attention to the notionof holonic inclusion and the central analogy of the transition from perception to memory.I turn next to Whitehead’s equally holonic notions of prehension and the concrescence ofactual occasions. From his epochal theory of time and certain ambiguities in hisreflections on the “divine antinomies,” we are brought to the threshold of a potentiallymore integral or “complex-holistic” theory of time and survival, which is treated in thelast section. This section draws from my earlier work on Hegel, Jung, and Edgar Morin,as well as from key insights of Jean Gebser, for an interpretation of Sri Aurobindo’sinspired but cryptic description of the “Supramental Time Vision.” This interpretationleads to an alternative understanding of reincarnation—and to the possibility of itsreconciliation with the once-only view

Sex-related time-dependent variations in post-stroke survival-evidence of a female stroke survival advantage

DEFF Research Database (Denmark)

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

2007-01-01

the influence of gender on post-stroke mortality, from the time of admission through the subsequent years until death or censoring ( mean follow-up time: 538 days). All patients underwent an evaluation including stroke severity, computed tomography and cardiovascular risk factors. Independent predictors......Background: Women live longer than men, yet most studies show that gender has no influence on survival after stroke. Methods: A registry was started in 2001, with the aim of registering all hospitalized stroke patients in Denmark, and it now holds 39,484 patients of which 48% are female. We studied...... of death were identified by means of a survival model based on 22,222 individuals with a complete data set. Results: Females were older and had severer stroke. Interestingly, the risk of death between genders was time dependent. The female/male stroke mortality rate favoured women from the first day...

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

[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.

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

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...

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.

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.)

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

Integral Time and the Varieties of Post-Mortem Survival

Directory of Open Access Journals (Sweden)

Sean M. Kelly

2008-06-01

Full Text Available While the question of survival of bodily death is usually approached by focusing on the mind/body relation (and often with the idea of the soul as a special kind of substance, this paper explores the issue in the context of our understanding of time. The argument of the paper is woven around the central intuition of time as an “ever-living present.” The development of this intuition allows for a more integral or “complex-holistic” theory of time, the soul, and the question of survival. Following the introductory matter, the first section proposes a re-interpretation of Nietzsche’s doctrine of eternal recurrence in terms of moments and lives as “eternally occurring.” The next section is a treatment of Julian Barbour’s neo-Machian model of instants of time as configurations in the n-dimensional phase-space he calls “Platonia.” While rejecting his claim to have done away with time, I do find his model suggestive of the idea of moments and lives as eternally occurring. The following section begins with Fechner’s visionary ideas of the nature of the soul and its survival of bodily death, with particular attention to the notion of holonic inclusion and the central analogy of the transition from perception to memory. I turn next to Whitehead’s equally holonic notions of prehension and the concrescence of actual occasions. From his epochal theory of time and certain ambiguities in his reflections on the “divine antinomies,” we are brought to the threshold of a potentially more integral or “complex-holistic” theory of time and survival, which is treated in the last section. This section draws from my earlier work on Hegel, Jung, and Edgar Morin, as well as from key insights of Jean Gebser, for an interpretation of Sri Aurobindo’s inspired but cryptic description of the “Supramental Time Vision.” This interpretation leads to an alternative understanding of reincarnation—and to the possibility of its reconciliation

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 ...

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.

The role of donor age and ischemic time on survival following orthotopic heart transplantation.

Science.gov (United States)

Del Rizzo, D F; Menkis, A H; Pflugfelder, P W; Novick, R J; McKenzie, F N; Boyd, W D; Kostuk, W J

1999-04-01

The advances in immunotherapy, along with a liberalization of eligibility criteria have contributed significantly to the ever increasing demand for donor organs. In an attempt to expand the donor pool, transplant programs are now accepting older donors as well as donors from more remote areas. The purpose of this study is to determine the effect of donor age and organ ischemic time on survival following orthotopic heart transplantation (OHT). From April 1981 to December 1996 372 adult patients underwent OHT at the University of Western Ontario. Cox proportional hazards models were used to identify predictors of outcome. Variables affecting survival were then entered into a stepwise logistic regression model to develop probability models for 30-day- and 1-year-mortality. The mean age of the recipient population was 45.6 +/- 12.3 years (range 18-64 years: 54 56 years). The majority (329 patients, 86.1%) were male and the most common indications for OHT were ischemic (n = 180) and idiopathic (n = 171) cardiomyopathy. Total ischemic time (TIT) was 202.4 +/- 84.5 minutes (range 47-457 minutes). In 86 donors TIT was under 2 hours while it was between 2 and 4 hours in 168, and more than 4 hours in 128 donors. Actuarial survival was 80%, 73%, and 55% at 1, 5, and 10 years respectively. By Cox proportional hazards models, recipient status (Status I-II vs III-IV; risk ratio 1.75; p = 0.003) and donor age, examined as either a continuous or categorical variable ([age or = 35; risk ratio 1.98; p or = 50; risk ratio 2.20; p or = 50; risk ratio 1.83; p 50 years (p = 0.009). By stepwise logistic regression analysis, a probability model for survival was then developed based on donor age, the interaction between donor age and ischemic time, and patient status. Improvements in myocardial preservation and peri-operative management may allow for the safe utilization of donor organs with prolonged ischemic times. Older donors are associated with decreased peri-operative and long

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.

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.

Association of bystander cardiopulmonary resuscitation and survival according to ambulance response-times after out-of-hospital cardiac arrest

DEFF Research Database (Denmark)

Rajan, Shahzleen; Wissenberg, Mads; Folke, Fredrik

2016-01-01

30-day survival chances decreased for both patients with bystander CPR and those without. However, the contrast between the survival chances of patients with versus without bystander CPR increased over time: within 5 minutes, 30-day survival was 14.5% (95% confidence interval [CI]: 12.8-16.4) versus...... 6.3% (95% CI: 5.1-7.6), corresponding to 2.3 times higher chances of survival associated with bystander CPR; within 10 minutes, 30-day survival chances were 6.7% (95% CI: 5.4-8.1) versus 2.2% (95% CI: 1.5-3.1), corresponding to 3.0 times higher chances of 30-day survival associated with bystander...... CPR. The contrast in 30-day survival became statistically insignificant when response time was >13 minutes (bystander CPR vs no bystander CPR: 3.7% [95% CI: 2.2-5.4] vs 1.5% [95% CI: 0.6-2.7]), but 30-day survival was still 2.5 times higher associated with bystander CPR. Based on the model and Danish...

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.

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...

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.)

Association of Bystander Cardiopulmonary Resuscitation and Survival According to Ambulance Response Times After Out-of-Hospital Cardiac Arrest.

Science.gov (United States)

Rajan, Shahzleen; Wissenberg, Mads; Folke, Fredrik; Hansen, Steen Møller; Gerds, Thomas A; Kragholm, Kristian; Hansen, Carolina Malta; Karlsson, Lena; Lippert, Freddy K; Køber, Lars; Gislason, Gunnar H; Torp-Pedersen, Christian

2016-12-20

Bystander-initiated cardiopulmonary resuscitation (CPR) increases patient survival after out-of-hospital cardiac arrest, but it is unknown to what degree bystander CPR remains positively associated with survival with increasing time to potential defibrillation. The main objective was to examine the association of bystander CPR with survival as time to advanced treatment increases. We studied 7623 out-of-hospital cardiac arrest patients between 2005 and 2011, identified through the nationwide Danish Cardiac Arrest Registry. Multiple logistic regression analysis was used to examine the association between time from 911 call to emergency medical service arrival (response time) and survival according to whether bystander CPR was provided (yes or no). Reported are 30-day survival chances with 95% bootstrap confidence intervals. With increasing response times, adjusted 30-day survival chances decreased for both patients with bystander CPR and those without. However, the contrast between the survival chances of patients with versus without bystander CPR increased over time: within 5 minutes, 30-day survival was 14.5% (95% confidence interval [CI]: 12.8-16.4) versus 6.3% (95% CI: 5.1-7.6), corresponding to 2.3 times higher chances of survival associated with bystander CPR; within 10 minutes, 30-day survival chances were 6.7% (95% CI: 5.4-8.1) versus 2.2% (95% CI: 1.5-3.1), corresponding to 3.0 times higher chances of 30-day survival associated with bystander CPR. The contrast in 30-day survival became statistically insignificant when response time was >13 minutes (bystander CPR vs no bystander CPR: 3.7% [95% CI: 2.2-5.4] vs 1.5% [95% CI: 0.6-2.7]), but 30-day survival was still 2.5 times higher associated with bystander CPR. Based on the model and Danish out-of-hospital cardiac arrest statistics, an additional 233 patients could potentially be saved annually if response time was reduced from 10 to 5 minutes and 119 patients if response time was reduced from 7 (the median

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.

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.

Methodology for lognormal modelling of malignant pleural mesothelioma survival time distributions: a study of 5580 case histories from Europe and USA

Energy Technology Data Exchange (ETDEWEB)

Mould, Richard F [41 Ewhurst Avenue, South Croydon, Surrey CR2 0DH (United Kingdom); Lahanas, Michael [Klinikum Offenbach, Strahlenklinik, 66 Starkenburgring, 63069 Offenbach am Main (Germany); Asselain, Bernard [Institut Curie, Biostatistiques, 26 rue d' Ulm, 75231 Paris Cedex 05 (France); Brewster, David [Director, Scottish Cancer Registry, Information Services (NHS National Services Scotland) Area 155, Gyle Square, 1 South Gyle Crescent, Edinburgh EH12 9EB (United Kingdom); Burgers, Sjaak A [Department of Thoracic Oncology, Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, The (Netherlands); Damhuis, Ronald A M [Rotterdam Cancer Registry, Rochussenstraat 125, PO Box 289, 3000 AG Rotterdam, The (Netherlands); Rycke, Yann De [Institut Curie, Biostatistiques, 26 rue d' Ulm, 75231 Paris Cedex 05 (France); Gennaro, Valerio [Liguria Mesothelioma Cancer Registry, Etiology and Epidemiology Department, National Cancer Research Institute, Pad. Maragliano, Largo R Benzi, 10-16132 Genoa (Italy); Szeszenia-Dabrowska, Neonila [Department of Occupational and Environmental Epidemiology, National Institute of Occupational Medicine, PO Box 199, Swietej Teresy od Dzieciatka Jezus 8, 91-348 Lodz (Poland)

2004-09-07

A truncated left-censored and right-censored lognormal model has been validated for representing pleural mesothelioma survival times in the range 5-200 weeks for data subsets grouped by age for males, 40-49, 50-59, 60-69, 70-79 and 80+ years and for all ages combined for females. The cases available for study were from Europe and USA and totalled 5580. This is larger than any other pleural mesothelioma cohort accrued for study. The methodology describes the computation of reference baseline probabilities, 5-200 weeks, which can be used in clinical trials to assess results of future promising treatment methods. This study is an extension of previous lognormal modelling by Mould et al (2002 Phys. Med. Biol. 47 3893-924) to predict long-term cancer survival from short-term data where the proportion cured is denoted by C and the uncured proportion, which can be represented by a lognormal, by (1 - C). Pleural mesothelioma is a special case when C = 0.

Methodology for lognormal modelling of malignant pleural mesothelioma survival time distributions: a study of 5580 case histories from Europe and USA

International Nuclear Information System (INIS)

Mould, Richard F; Lahanas, Michael; Asselain, Bernard; Brewster, David; Burgers, Sjaak A; Damhuis, Ronald A M; Rycke, Yann De; Gennaro, Valerio; Szeszenia-Dabrowska, Neonila

2004-01-01

A truncated left-censored and right-censored lognormal model has been validated for representing pleural mesothelioma survival times in the range 5-200 weeks for data subsets grouped by age for males, 40-49, 50-59, 60-69, 70-79 and 80+ years and for all ages combined for females. The cases available for study were from Europe and USA and totalled 5580. This is larger than any other pleural mesothelioma cohort accrued for study. The methodology describes the computation of reference baseline probabilities, 5-200 weeks, which can be used in clinical trials to assess results of future promising treatment methods. This study is an extension of previous lognormal modelling by Mould et al (2002 Phys. Med. Biol. 47 3893-924) to predict long-term cancer survival from short-term data where the proportion cured is denoted by C and the uncured proportion, which can be represented by a lognormal, by (1 - C). Pleural mesothelioma is a special case when C = 0

[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

Time dependent ethnic convergence in colorectal cancer survival in hawaii

Directory of Open Access Journals (Sweden)

Hundahl Scott A

2003-02-01

Full Text Available Abstract Background Although colorectal cancer death rates have been declining, this trend is not consistent across all ethnic groups. Biological, environmental, behavioral and socioeconomic explanations exist, but the reason for this discrepancy remains inconclusive. We examined the hypothesis that improved cancer screening across all ethnic groups will reduce ethnic differences in colorectal cancer survival. Methods Through the Hawaii Tumor Registry 16,424 patients diagnosed with colorectal cancer were identified during the years 1960–2000. Cox regression analyses were performed for each of three cohorts stratified by ethnicity (Caucasian, Japanese, Hawaiian, Filipino, and Chinese. The models included stage of diagnosis, year of diagnosis, age, and sex as predictors of survival. Results Mortality rates improved significantly for all ethnic groups. Moreover, with the exception of Hawaiians, rates for all ethnic groups converged over time. Persistently lower survival for Hawaiians appeared linked with more cancer treatment. Conclusion Ethnic disparities in colorectal cancer mortality rates appear primarily the result of differential utilization of health care. If modern screening procedures can be provided equally to all ethnic groups, ethnic outcome differences can be virtually eliminated.

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

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.

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.

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.

Survival Times of Meter-Sized Rock Boulders on the Surface of Airless Bodies

Science.gov (United States)

Basilevsky, A. T.; Head, J. W.; Horz, F.; Ramsley, K.

2015-01-01

This study considers the survival times of meter-sized rock boulders on the surfaces of several airless bodies. As the starting point, we employ estimates of the survival times of such boulders on the surface of the Moon by[1], then discuss the role of destruction due to day-night temperature cycling, consider the meteorite bombardment environment on the considered bodies in terms of projectile flux and velocities and finally estimate the survival times. Survival times of meter-sized rocks on lunar surface: The survival times of hand specimen-sized rocks exposed to the lunar surface environment were estimated based on experiments modeling the destruction of rocks by meteorite impacts, combined with measurements of the lunar surface meteorite flux, (e.g.,[2]). For estimations of the survival times of meter-sized lunar boulders, [1] suggested a different approach based on analysis of the spatial density of boulders on the rims of small lunar craters of known absolute age. It was found that for a few million years, only a small fraction of the boulders ejected by cratering process are destroyed, for several tens of million years approx.50% are destroyed, and for 200-300 Ma, 90 to 99% are destroyed. Following [2] and other works, [1] considered that the rocks are mostly destroyed by meteorite impacts. Destruction of rocks by thermal-stress. However, high diurnal temperature variations on the surface of the Moon and other airless bodies imply that thermal stresses may also be a cause of surface rock destruction. Delbo et al. [3] interpreted the observed presence of fine debris on the surface of small asteroids as due to thermal surface cycling. They stated that because of the very low gravity on the surface of these bodies, ejecta from meteorite impacts should leave the body, so formation there of fine debris has to be due to thermal cycling. Based on experiments on heating-cooling of cm-scale pieces of ordinary and carbonaceous chondrites and theoretical modeling of

The time dependent association of adrenaline administration and survival from out-of-hospital cardiac arrest.

Science.gov (United States)

Ewy, Gordon A; Bobrow, Bentley J; Chikani, Vatsal; Sanders, Arthur B; Otto, Charles W; Spaite, Daniel W; Kern, Karl B

2015-11-01

Recommended for decades, the therapeutic value of adrenaline (epinephrine) in the resuscitation of patients with out-of-hospital cardiac arrest (OHCA) is controversial. To investigate the possible time-dependent outcomes associated with adrenaline administration by Emergency Medical Services personnel (EMS). A retrospective analysis of prospectively collected data from a near statewide cardiac resuscitation database between 1 January 2005 and 30 November 2013. Multivariable logistic regression was used to analyze the effect of the time interval between EMS dispatch and the initial dose of adrenaline on survival. The primary endpoints were survival to hospital discharge and favourable neurologic outcome. Data from 3469 patients with witnessed OHCA were analyzed. Their mean age was 66.3 years and 69% were male. An initially shockable rhythm was present in 41.8% of patients. Based on a multivariable logistic regression model with initial adrenaline administration time interval (AATI) from EMS dispatch as the covariate, survival was greatest when adrenaline was administered very early but decreased rapidly with increasing (AATI); odds ratio 0.94 (95% Confidence Interval (CI) 0.92-0.97). The AATI had no significant effect on good neurological outcome (OR=0.96, 95% CI=0.90-1.02). In patients with OHCA, survival to hospital discharge was greater in those treated early with adrenaline by EMS especially in the subset of patients with a shockable rhythm. However survival rapidly decreased with increasing adrenaline administration time intervals (AATI). Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Measuring survival time: a probability-based approach useful in healthcare decision-making.

Science.gov (United States)

2011-01-01

In some clinical situations, the choice between treatment options takes into account their impact on patient survival time. Due to practical constraints (such as loss to follow-up), survival time is usually estimated using a probability calculation based on data obtained in clinical studies or trials. The two techniques most commonly used to estimate survival times are the Kaplan-Meier method and the actuarial method. Despite their limitations, they provide useful information when choosing between treatment options.

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

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...

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

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.

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

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.

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.

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.

Improving Gastric Cancer Outcome Prediction Using Single Time-Point Artificial Neural Network Models

Science.gov (United States)

Nilsaz-Dezfouli, Hamid; Abu-Bakar, Mohd Rizam; Arasan, Jayanthi; Adam, Mohd Bakri; Pourhoseingholi, Mohamad Amin

2017-01-01

In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time distribution or proportionality of hazard. Therefore, attention must be paid in developing nonlinear models with less restrictive assumptions. Artificial neural network (ANN) models are primarily useful in prediction when nonlinear approaches are required to sift through the plethora of available information. The applications of ANN models for prognostic and diagnostic classification in medicine have attracted a lot of interest. The applications of ANN models in modelling the survival of patients with gastric cancer have been discussed in some studies without completely considering the censored data. This study proposes an ANN model for predicting gastric cancer survivability, considering the censored data. Five separate single time-point ANN models were developed to predict the outcome of patients after 1, 2, 3, 4, and 5 years. The performance of ANN model in predicting the probabilities of death is consistently high for all time points according to the accuracy and the area under the receiver operating characteristic curve. PMID:28469384

Median survival time of patients after transcatheter chemo-embolization for hepatocellular carcinoma

International Nuclear Information System (INIS)

Haider, Z.; Haq, T.; Munir, K.; Usman, M.U.; Azeemuddin, M.

2006-01-01

Objective: To determine the effect on survival after trans arterial chemo embolization (TACE) in patients with unresectable hepatocellular carcinoma (HCC). Design: Longitudinal cohort study. Place and Duration of Study: Radiology Department, The Aga Khan University Hospital, Stadium Road, Karachi, from December 1997 to September 2005. Patients and Methods: Patients undergoing TACE procedure for HCC were prospectively followed. Forty three patients were enrolled from December 1997 to March 2003 in the study and subjected to chemo embolization therapy. Eight out of 43 patients were excluded from the study, who lost to follow-up. All the patients were followed till their death. Median and mean survival were calculated. Results: The median survival of these 35 patients was 410 days (13.6 months), with 95% confidence interval (236 days lower bound and 536 days upper bound). Mean survival time was 603 days (20.1 months) with 95% confidence interval (394 days lower bound and 812 days upper bound). There was significant difference in mean survival time (in days) by Child's Pugh class (X2 = 12.384; df=2, p-value=0.002). Conclusion: The study showed that TACE is an effective palliative treatment. TACE increases the median survival time. (author)

Effects of Oral Administration of Fucoidan Extracted from Cladosiphon okamuranus on Tumor Growth and Survival Time in a Tumor-Bearing Mouse Model

Directory of Open Access Journals (Sweden)

Yoshiharu Okamoto

2012-10-01

Full Text Available We evaluated the anti-tumor activities of the oral administration of fucoidan extracted from Cladosiphon okamuranus using a tumor (colon 26-bearing mouse model. The materials used included low-molecular-weight fucoidan (LMWF: 6.5–40 kDa, intermediate-molecular-weight fucoidan (IMWF: 110–138 kDa and high-molecular-weight fucoidan (HMWF: 300–330 kDa. The IMWF group showed significantly suppressed tumor growth. The LMWF and HMWF groups showed significantly increased survival times compared with that observed in the control group (mice fed a fucoidan-free diet. The median survival times in the control, LMWF, IMWF and HMWF groups were 23, 46, 40 and 43 days, respectively. It was also found that oral administration of fucoidan increased the population of natural killer cells in the spleen. Furthermore, from the results of the experiment using Myd-88 knockout mice, it was found that these effects are related to gut immunity. These results suggest that fucoidan is a candidate anti-tumor functional food.

Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction

International Nuclear Information System (INIS)

Garzon, Benjamin; Emblem, Kyrre E.; Mouridsen, Kim; Nedregaard, Baard; Due-Toennessen, Paulina; Nome, Terje; Hald, John K.; Bjoernerud, Atle; Haaberg, Asta K.; Kvinnsland, Yngve

2011-01-01

Background. A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose. To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods. T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results. Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). Conclusion. Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients

Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction

Energy Technology Data Exchange (ETDEWEB)

Garzon, Benjamin (Dept. of Circulation and Medical Imaging, NTNU, Trondheim (Norway)), email: benjamin.garzon@ntnu.no; Emblem, Kyrre E. (The Interventional Center, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway); Dept. of Radiology, MGH-HST AA Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston (United States)); Mouridsen, Kim (Center of Functionally Integrative Neuroscience, Aarhus Univ., Aarhus (Denmark)); Nedregaard, Baard; Due-Toennessen, Paulina; Nome, Terje; Hald, John K. (Dept. of Radiology and Nuclear Medicine, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway)); Bjoernerud, Atle (The Interventional Center, Rikshospitalet, Oslo Univ. Hospital, Oslo (Norway)); Haaberg, Asta K. (Dept. of Circulation and Medical Imaging, NTNU, Trondheim (Norway); Dept. of Medical Imaging, St Olav' s Hospital, Trondheim (Norway)); Kvinnsland, Yngve (NordicImagingLab, Bergen (Norway))

2011-11-15

Background. A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose. To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods. T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results. Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). Conclusion. Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients

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.

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...

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.

Association between time to disease progression end points and overall survival in patients with neuroendocrine tumors

Directory of Open Access Journals (Sweden)

Singh S

2014-08-01

Full Text Available Simron Singh,1 Xufang Wang,2 Calvin HL Law1 1Sunnybrook Odette Cancer Center, University of Toronto, Toronto, ON, Canada; 2Novartis Oncology, Florham Park, NJ, USA Abstract: Overall survival can be difficult to determine for slowly progressing malignancies, such as neuroendocrine tumors. We investigated whether time to disease progression is positively associated with overall survival in patients with such tumors. A literature review identified 22 clinical trials in patients with neuroendocrine tumors that reported survival probabilities for both time to disease progression (progression-free survival and time to progression and overall survival. Associations between median time to disease progression and median overall survival and between treatment effects on time to disease progression and treatment effects on overall survival were analyzed using weighted least-squares regression. Median time to disease progression was significantly associated with median overall survival (coefficient 0.595; P=0.022. In the seven randomized studies identified, the risk reduction for time to disease progression was positively associated with the risk reduction for overall survival (coefficient on −ln[HR] 0.151; 95% confidence interval −0.843, 1.145; P=0.713. The significant association between median time to disease progression and median overall survival supports the assertion that time to disease progression is an alternative end point to overall survival in patients with neuroendocrine tumors. An apparent albeit not significant trend correlates treatment effects on time to disease progression and treatment effects on overall survival. Informal surveys of physicians’ perceptions are consistent with these concepts, although additional randomized trials are needed. Keywords: neuroendocrine tumors, progression-free survival, disease progression, mortality

A sequential threshold cure model for genetic analysis of time-to-event data

DEFF Research Database (Denmark)

Ødegård, J; Madsen, Per; Labouriau, Rodrigo S.

2011-01-01

In analysis of time-to-event data, classical survival models ignore the presence of potential nonsusceptible (cured) individuals, which, if present, will invalidate the inference procedures. Existence of nonsusceptible individuals is particularly relevant under challenge testing with specific...... pathogens, which is a common procedure in aquaculture breeding schemes. A cure model is a survival model accounting for a fraction of nonsusceptible individuals in the population. This study proposes a mixed cure model for time-to-event data, measured as sequential binary records. In a simulation study...... survival data were generated through 2 underlying traits: susceptibility and endurance (risk of dying per time-unit), associated with 2 sets of underlying liabilities. Despite considerable phenotypic confounding, the proposed model was largely able to distinguish the 2 traits. Furthermore, if selection...

The accuracy of survival time prediction for patients with glioma is improved by measuring mitotic spindle checkpoint gene expression.

Directory of Open Access Journals (Sweden)

Li Bie

Full Text Available Identification of gene expression changes that improve prediction of survival time across all glioma grades would be clinically useful. Four Affymetrix GeneChip datasets from the literature, containing data from 771 glioma samples representing all WHO grades and eight normal brain samples, were used in an ANOVA model to screen for transcript changes that correlated with grade. Observations were confirmed and extended using qPCR assays on RNA derived from 38 additional glioma samples and eight normal samples for which survival data were available. RNA levels of eight major mitotic spindle assembly checkpoint (SAC genes (BUB1, BUB1B, BUB3, CENPE, MAD1L1, MAD2L1, CDC20, TTK significantly correlated with glioma grade and six also significantly correlated with survival time. In particular, the level of BUB1B expression was highly correlated with survival time (p<0.0001, and significantly outperformed all other measured parameters, including two standards; WHO grade and MIB-1 (Ki-67 labeling index. Measurement of the expression levels of a small set of SAC genes may complement histological grade and other clinical parameters for predicting survival time.

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.)

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

TH-E-BRF-05: Comparison of Survival-Time Prediction Models After Radiotherapy for High-Grade Glioma Patients Based On Clinical and DVH Features

International Nuclear Information System (INIS)

Magome, T; Haga, A; Igaki, H; Sekiya, N; Masutani, Y; Sakumi, A; Mukasa, A; Nakagawa, K

2014-01-01

Purpose: Although many outcome prediction models based on dose-volume information have been proposed, it is well known that the prognosis may be affected also by multiple clinical factors. The purpose of this study is to predict the survival time after radiotherapy for high-grade glioma patients based on features including clinical and dose-volume histogram (DVH) information. Methods: A total of 35 patients with high-grade glioma (oligodendroglioma: 2, anaplastic astrocytoma: 3, glioblastoma: 30) were selected in this study. All patients were treated with prescribed dose of 30–80 Gy after surgical resection or biopsy from 2006 to 2013 at The University of Tokyo Hospital. All cases were randomly separated into training dataset (30 cases) and test dataset (5 cases). The survival time after radiotherapy was predicted based on a multiple linear regression analysis and artificial neural network (ANN) by using 204 candidate features. The candidate features included the 12 clinical features (tumor location, extent of surgical resection, treatment duration of radiotherapy, etc.), and the 192 DVH features (maximum dose, minimum dose, D95, V60, etc.). The effective features for the prediction were selected according to a step-wise method by using 30 training cases. The prediction accuracy was evaluated by a coefficient of determination (R 2 ) between the predicted and actual survival time for the training and test dataset. Results: In the multiple regression analysis, the value of R 2 between the predicted and actual survival time was 0.460 for the training dataset and 0.375 for the test dataset. On the other hand, in the ANN analysis, the value of R 2 was 0.806 for the training dataset and 0.811 for the test dataset. Conclusion: Although a large number of patients would be needed for more accurate and robust prediction, our preliminary Result showed the potential to predict the outcome in the patients with high-grade glioma. This work was partly supported by the JSPS Core

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.

Helicopter crashes into water: warning time, final position, and other factors affecting survival.

Science.gov (United States)

Brooks, Christopher J; MacDonald, Conor V; Baker, Susan P; Shanahan, Dennis F; Haaland, Wren L

2014-04-01

According to 40 yr of data, the fatality rate for a helicopter crash into water is approximately 25%. Does warning time and the final position of the helicopter in the water influence the survival rate? The National Transportation Safety Board (NTSB) database was queried to identify helicopter crashes into water between 1981 and 2011 in the Gulf of Mexico and Hawaii. Fatality rate, amount of warning time prior to the crash, and final position of the helicopter were identified. There were 133 helicopters that crashed into water with 456 crew and passengers. Of these, 119 occupants (26%) did not survive; of those who did survive, 38% were injured. Twelve died after making a successful escape from the helicopter. Crashes with 1 min. However, more than half of fatalities (57%) came from crashes for which the warning time could not be determined. Lack of warning time and how to survive in the water after the crash should be a topic for study in all marine survival/aircraft ditching courses. Investigators should be trained to provide estimates of warning time when investigating helicopter crashes into water.

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.

On the Survival Time of a Duplex System: A Sokhotski-Plemelj Problem

Directory of Open Access Journals (Sweden)

Edmond J. Vanderperre

2008-12-01

Full Text Available We analyze the survival time of a renewable duplex system characterized by warm standby and subjected to a priority rule. In order to obtain the Laplace transform of the survival function, we employ a stochastic process endowed with time-dependent transition measures satisfying coupled partial differential equations. The solution procedure is based on the theory of sectionally holomorphic functions combined with the notion of dual transforms. Finally, we introduce a security interval related to a prescribed security level and a suitable risk criterion based on the survival function of the system. As an example, we consider the particular case of deterministic repair. A computer-plotted graph displays the survival function together with the security interval corresponding to a security level of 90%.

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.

Survival with Three-Times Weekly In-Center Nocturnal Versus Conventional Hemodialysis

Science.gov (United States)

Xu, Jianglin; Suri, Rita S.; Nesrallah, Gihad; Lindsay, Robert; Garg, Amit X.; Lester, Keith; Ofsthun, Norma; Lazarus, Michael; Hakim, Raymond M.

2012-01-01

Whether the duration of hemodialysis treatments improves outcomes remains controversial. Here, we evaluated survival and clinical changes associated with converting from conventional hemodialysis (mean=3.75 h/treatment) to in-center nocturnal hemodialysis (mean=7.85 h/treatment). All 959 consecutive patients who initiated nocturnal hemodialysis for the first time in 77 Fresenius Medical Care facilities during 2006 and 2007 were eligible. We used Cox models to compare risk for mortality during 2 years of follow-up in a 1:3 propensity score–matched cohort of 746 nocturnal and 2062 control patients on conventional hemodialysis. Two-year mortality was 19% among nocturnal hemodialysis patients compared with 27% among conventional patients. Nocturnal hemodialysis associated with a 25% reduction in the risk for death after adjustment for age, body mass index, and dialysis vintage (hazard ratio=0.75, 95% confidence interval=0.61–0.91, P=0.004). With respect to clinical features, interdialytic weight gain, albumin, hemoglobin, dialysis dose, and calcium increased on nocturnal therapy, whereas postdialysis weight, predialysis systolic blood pressure, ultrafiltration rate, phosphorus, and white blood cell count declined (all P<0.001). In summary, notwithstanding the possibility of residual selection bias, conversion to treatment with nocturnal hemodialysis associates with favorable clinical features, laboratory biomarkers, and improved survival compared with propensity score–matched controls. The potential impact of extended treatment time on clinical outcomes while maintaining a three times per week hemodialysis schedule requires evaluation in future clinical trials. PMID:22362905

Biologically-equivalent dose and long-term survival time in radiation treatments

International Nuclear Information System (INIS)

Zaider, Marco; Hanin, Leonid

2007-01-01

Within the linear-quadratic model the biologically-effective dose (BED)-taken to represent treatments with an equal tumor control probability (TCP)-is commonly (and plausibly) calculated according to BED(D) = -log[S(D)]/α. We ask whether in the presence of cellular proliferation this claim is justified and examine, as a related question, the extent to which BED approximates an isoeffective dose (IED) defined, more sensibly, in terms of an equal long-term survival probability, rather than TCP. We derive, under the assumption that cellular birth and death rates are time homogeneous, exact equations for the isoeffective dose, IED. As well, we give a rigorous definition of effective long-term survival time, T eff . By using several sets of radiobiological parameters, we illustrate potential differences between BED and IED on the one hand and, on the other, between T eff calculated as suggested here or by an earlier recipe. In summary: (a) the equations currently in use for calculating the effective treatment time may underestimate the isoeffective dose and should be avoided. The same is the case for the tumor control probability (TCP), only more so; (b) for permanent implants BED may be a poor substitute for IED; (c) for a fractionated treatment schedule, interpreting the observed probability of cure in terms of a TCP formalism that refers to the end of the treatment (rather than T eff ) may result in a miscalculation (underestimation) of the initial number of clonogens

Joint modelling of repeated measurement and time-to-event data: an introductory tutorial.

Science.gov (United States)

Asar, Özgür; Ritchie, James; Kalra, Philip A; Diggle, Peter J

2015-02-01

The term 'joint modelling' is used in the statistical literature to refer to methods for simultaneously analysing longitudinal measurement outcomes, also called repeated measurement data, and time-to-event outcomes, also called survival data. A typical example from nephrology is a study in which the data from each participant consist of repeated estimated glomerular filtration rate (eGFR) measurements and time to initiation of renal replacement therapy (RRT). Joint models typically combine linear mixed effects models for repeated measurements and Cox models for censored survival outcomes. Our aim in this paper is to present an introductory tutorial on joint modelling methods, with a case study in nephrology. We describe the development of the joint modelling framework and compare the results with those obtained by the more widely used approaches of conducting separate analyses of the repeated measurements and survival times based on a linear mixed effects model and a Cox model, respectively. Our case study concerns a data set from the Chronic Renal Insufficiency Standards Implementation Study (CRISIS). We also provide details of our open-source software implementation to allow others to replicate and/or modify our analysis. The results for the conventional linear mixed effects model and the longitudinal component of the joint models were found to be similar. However, there were considerable differences between the results for the Cox model with time-varying covariate and the time-to-event component of the joint model. For example, the relationship between kidney function as measured by eGFR and the hazard for initiation of RRT was significantly underestimated by the Cox model that treats eGFR as a time-varying covariate, because the Cox model does not take measurement error in eGFR into account. Joint models should be preferred for simultaneous analyses of repeated measurement and survival data, especially when the former is measured with error and the association

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.

Clinical findings and survival time in dogs with advanced heart failure.

Science.gov (United States)

Beaumier, Amelie; Rush, John E; Yang, Vicky K; Freeman, Lisa M

2018-04-10

Dogs with advanced heart failure are a clinical challenge for veterinarians but there are no studies reporting clinical features and outcome of this population. To describe clinical findings and outcome of dogs with advanced heart failure caused by degenerative mitral valve disease (DMVD). Fifty-four dogs with advanced heart failure because of DMVD. For study purposes, advanced heart failure was defined as recurrence of congestive heart failure signs despite receiving the initially prescribed dose of pimobendan, angiotensin-converting-enzyme inhibitor (ACEI), and furosemide >4 mg/kg/day. Data were collected for the time of diagnosis of Stage C heart failure and time of diagnosis of advanced heart failure. Date of death was recorded. At the diagnosis of advanced heart failure, doses of pimobendan (n = 30), furosemide (n = 28), ACEI (n = 13), and spironolactone (n = 4) were increased, with ≥1 new medications added in most dogs. After initial diagnosis of advanced heart failure, 38 (70%) dogs had additional medications adjustments (median = 2 [range, 0-27]), with the final total medication number ranging from 2-10 (median = 5). Median survival time after diagnosis of advanced heart failure was 281 days (range, 3-885 days). Dogs receiving a furosemide dose >6.70 mg/kg/day had significantly longer median survival times (402 days [range, 3-885 days] versus 129 days [range 9-853 days]; P = .017). Dogs with advanced heart failure can have relatively long survival times. Higher furosemide dose and non-hospitalization were associated with longer survival. Copyright © 2018 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

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

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.

Pre-hospital transport times and survival for Hypotensive patients with penetrating thoracic trauma

Directory of Open Access Journals (Sweden)

Mamta Swaroop

2013-01-01

Full Text Available Background: Achieving definitive care within the "Golden Hour" by minimizing response times is a consistent goal of regional trauma systems . This study hypothesizes that in urban Level I Trauma Centers, shorter pre-hospital times would predict outcomes in penetrating thoracic injuries. Materials and Methods: A retrospective cohort study was performed using a statewide trauma registry for the years 1999-2003 . Total pre-hospital times were measured for urban victims of penetrating thoracic trauma. Crude and adjusted mortality rates were compared by pre-hospital time using STATA statistical software. Results: During the study period, 908 patients presented to the hospital after penetrating thoracic trauma, with 79% surviving . Patients with higher injury severity scores (ISS were transported more quickly. Injury severity scores (ISS ≥16 and emergency department (ED hypotension (systolic blood pressure, SBP <90 strongly predicted mortality (P < 0.05 for each . In a logistic regression model including age, race, and ISS, longer transport times for hypotensive patients were associated with higher mortality rates (all P values <0.05. This was seen most significantly when comparing patient transport times 0-15 min and 46-60 min (P < 0.001. Conclusion: In victims of penetrating thoracic trauma, more severely injured patients arrive at urban trauma centers sooner . Mortality is strongly predicted by injury severity, although shorter pre-hospital times are associated with improved survival . These results suggest that careful planning to optimize transport time-encompassing hospital capacity and existing resources, traffic patterns, and trauma incident densities may be beneficial in areas with a high burden of penetrating trauma.

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.

Mean exit time and survival probability within the CTRW formalism

Science.gov (United States)

Montero, M.; Masoliver, J.

2007-05-01

An intense research on financial market microstructure is presently in progress. Continuous time random walks (CTRWs) are general models capable to capture the small-scale properties that high frequency data series show. The use of CTRW models in the analysis of financial problems is quite recent and their potentials have not been fully developed. Here we present two (closely related) applications of great interest in risk control. In the first place, we will review the problem of modelling the behaviour of the mean exit time (MET) of a process out of a given region of fixed size. The surveyed stochastic processes are the cumulative returns of asset prices. The link between the value of the MET and the timescale of the market fluctuations of a certain degree is crystal clear. In this sense, MET value may help, for instance, in deciding the optimal time horizon for the investment. The MET is, however, one among the statistics of a distribution of bigger interest: the survival probability (SP), the likelihood that after some lapse of time a process remains inside the given region without having crossed its boundaries. The final part of the manuscript is devoted to the study of this quantity. Note that the use of SPs may outperform the standard “Value at Risk" (VaR) method for two reasons: we can consider other market dynamics than the limited Wiener process and, even in this case, a risk level derived from the SP will ensure (within the desired quintile) that the quoted value of the portfolio will not leave the safety zone. We present some preliminary theoretical and applied results concerning this topic.

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.)

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...

Adjuvant Radiation Therapy Treatment Time Impacts Overall Survival in Gastric Cancer

International Nuclear Information System (INIS)

McMillan, Matthew T.; Ojerholm, Eric; Roses, Robert E.; Plastaras, John P.; Metz, James M.; Mamtani, Ronac; Karakousis, Giorgos C.; Fraker, Douglas L.; Drebin, Jeffrey A.; Stripp, Diana; Ben-Josef, Edgar; Datta, Jashodeep

2015-01-01

Purpose: Prolonged radiation therapy treatment time (RTT) is associated with worse survival in several tumor types. This study investigated whether delays during adjuvant radiation therapy impact overall survival (OS) in gastric cancer. Methods and Materials: The National Cancer Data Base was queried for patients with resected gastric cancer who received adjuvant radiation therapy with National Comprehensive Cancer Network–recommended doses (45 or 50.4 Gy) between 1998 and 2006. RTT was classified as standard (45 Gy: 33-36 days, 50.4 Gy: 38-41 days) or prolonged (45 Gy: >36 days, 50.4 Gy: >41 days). Cox proportional hazards models evaluated the association between the following factors and OS: RTT, interval from surgery to radiation therapy initiation, interval from surgery to radiation therapy completion, radiation therapy dose, demographic/pathologic and operative factors, and other elements of adjuvant multimodality therapy. Results: Of 1591 patients, RTT was delayed in 732 (46%). Factors associated with prolonged RTT were non-private health insurance (OR 1.3, P=.005) and treatment at non-academic facilities (OR 1.2, P=.045). Median OS and 5-year actuarial survival were significantly worse in patients with prolonged RTT compared with standard RTT (36 vs 51 months, P=.001; 39 vs 47%, P=.005); OS worsened with each cumulative week of delay (P<.0004). On multivariable analysis, prolonged RTT was associated with inferior OS (hazard ratio 1.2, P=.002); the intervals from surgery to radiation therapy initiation or completion were not. Prolonged RTT was particularly detrimental in patients with node positivity, inadequate nodal staging (<15 nodes examined), and those undergoing a cycle of chemotherapy before chemoradiation therapy. Conclusions: Delays during adjuvant radiation therapy appear to negatively impact survival in gastric cancer. Efforts to minimize cumulative interruptions to <7 days should be considered

Adjuvant Radiation Therapy Treatment Time Impacts Overall Survival in Gastric Cancer

Energy Technology Data Exchange (ETDEWEB)

McMillan, Matthew T. [Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania (United States); Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania (United States); Ojerholm, Eric [Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania (United States); Roses, Robert E., E-mail: Robert.Roses@uphs.upenn.edu [Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania (United States); Plastaras, John P.; Metz, James M. [Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania (United States); Mamtani, Ronac [Department of Hematology/Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania (United States); Karakousis, Giorgos C.; Fraker, Douglas L.; Drebin, Jeffrey A. [Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania (United States); Stripp, Diana; Ben-Josef, Edgar [Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania (United States); Datta, Jashodeep [Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania (United States)

2015-10-01

Purpose: Prolonged radiation therapy treatment time (RTT) is associated with worse survival in several tumor types. This study investigated whether delays during adjuvant radiation therapy impact overall survival (OS) in gastric cancer. Methods and Materials: The National Cancer Data Base was queried for patients with resected gastric cancer who received adjuvant radiation therapy with National Comprehensive Cancer Network–recommended doses (45 or 50.4 Gy) between 1998 and 2006. RTT was classified as standard (45 Gy: 33-36 days, 50.4 Gy: 38-41 days) or prolonged (45 Gy: >36 days, 50.4 Gy: >41 days). Cox proportional hazards models evaluated the association between the following factors and OS: RTT, interval from surgery to radiation therapy initiation, interval from surgery to radiation therapy completion, radiation therapy dose, demographic/pathologic and operative factors, and other elements of adjuvant multimodality therapy. Results: Of 1591 patients, RTT was delayed in 732 (46%). Factors associated with prolonged RTT were non-private health insurance (OR 1.3, P=.005) and treatment at non-academic facilities (OR 1.2, P=.045). Median OS and 5-year actuarial survival were significantly worse in patients with prolonged RTT compared with standard RTT (36 vs 51 months, P=.001; 39 vs 47%, P=.005); OS worsened with each cumulative week of delay (P<.0004). On multivariable analysis, prolonged RTT was associated with inferior OS (hazard ratio 1.2, P=.002); the intervals from surgery to radiation therapy initiation or completion were not. Prolonged RTT was particularly detrimental in patients with node positivity, inadequate nodal staging (<15 nodes examined), and those undergoing a cycle of chemotherapy before chemoradiation therapy. Conclusions: Delays during adjuvant radiation therapy appear to negatively impact survival in gastric cancer. Efforts to minimize cumulative interruptions to <7 days should be considered.

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.

Modeling discrete time-to-event data

CERN Document Server

Tutz, Gerhard

2016-01-01

This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are expla...

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.

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

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).

An exploratory discrete-time multilevel analysis of the effect of social support on the survival of elderly people in China

Science.gov (United States)

Feng, Zhixin; Jones, Kelvyn; Wang, Wenfei Winnie

2015-01-01

This study undertakes a survival analysis of elderly persons in China using Chinese Longitudinal Healthy Longevity Survey 2002–2008. Employing discrete-time multilevel models, we explored the effect of social support on the survival of elderly people in China. This study focuses on objective (living arrangements and received support) and subjective activities (perceived support) of social support, finding that the effect of different activities of social support on the survival of elderly people varies according to the availability of different support resources. Specifically, living with a spouse, financial independence, perceiving care support from any resource is associated with higher survival rates for elderly people. Separate analysis focusing on urban elderly and rural elderly revealed broadly similar results. There is a larger difference between those perceiving care support from family or social service and not perceiving care support in urban areas comparing to those in rural areas. Those who cannot pay medical expenses are the least likely to survive. The higher level of economic development in province has no significant effect on the survival of elderly people for the whole sample model and the elderly people in urban areas; however, there is a negative influence on the survival of the rural elderly people. PMID:25703671

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.

Markov chains and semi-Markov models in time-to-event analysis.

Science.gov (United States)

Abner, Erin L; Charnigo, Richard J; Kryscio, Richard J

2013-10-25

A variety of statistical methods are available to investigators for analysis of time-to-event data, often referred to as survival analysis. Kaplan-Meier estimation and Cox proportional hazards regression are commonly employed tools but are not appropriate for all studies, particularly in the presence of competing risks and when multiple or recurrent outcomes are of interest. Markov chain models can accommodate censored data, competing risks (informative censoring), multiple outcomes, recurrent outcomes, frailty, and non-constant survival probabilities. Markov chain models, though often overlooked by investigators in time-to-event analysis, have long been used in clinical studies and have widespread application in other fields.

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.

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

Parent-child communication and marijuana initiation: evidence using discrete-time survival analysis.

Science.gov (United States)

Nonnemaker, James M; Silber-Ashley, Olivia; Farrelly, Matthew C; Dench, Daniel

2012-12-01

This study supplements existing literature on the relationship between parent-child communication and adolescent drug use by exploring whether parental and/or adolescent recall of specific drug-related conversations differentially impact youth's likelihood of initiating marijuana use. Using discrete-time survival analysis, we estimated the hazard of marijuana initiation using a logit model to obtain an estimate of the relative risk of initiation. Our results suggest that parent-child communication about drug use is either not protective (no effect) or - in the case of youth reports of communication - potentially harmful (leading to increased likelihood of marijuana initiation). Copyright © 2012 Elsevier Ltd. All rights reserved.

Rigorous bounds on survival times in circular accelerators and efficient computation of fringe-field transfer maps

International Nuclear Information System (INIS)

Hoffstaetter, G.H.

1994-12-01

Analyzing stability of particle motion in storage rings contributes to the general field of stability analysis in weakly nonlinear motion. A method which we call pseudo invariant estimation (PIE) is used to compute lower bounds on the survival time in circular accelerators. The pseudeo invariants needed for this approach are computed via nonlinear perturbative normal form theory and the required global maxima of the highly complicated multivariate functions could only be rigorously bound with an extension of interval arithmetic. The bounds on the survival times are large enough to the relevant; the same is true for the lower bounds on dynamical aperatures, which can be computed. The PIE method can lead to novel design criteria with the objective of maximizing the survival time. A major effort in the direction of rigourous predictions only makes sense if accurate models of accelerators are available. Fringe fields often have a significant influence on optical properties, but the computation of fringe-field maps by DA based integration is slower by several orders of magnitude than DA evaluation of the propagator for main-field maps. A novel computation of fringe-field effects called symplectic scaling (SYSCA) is introduced. It exploits the advantages of Lie transformations, generating functions, and scaling properties and is extremely accurate. The computation of fringe-field maps is typically made nearly two orders of magnitude faster. (orig.)

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.

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.

Survival and prognostic factors at time of diagnosis in high-grade appendicular osteosarcoma

DEFF Research Database (Denmark)

Colding-Rasmussen, Thomas; Thorn, Andrea Pohly; Horstmann, Peter

2018-01-01

BACKGROUND: Survival of patients with high-grade osteosarcoma (HOS), the most common primary bone cancer, has not improved significantly the last 30 years and the disease remains a major challenge. The purpose of this study is to evaluate survival in relation to prognostic factors at time of diag...

Childhood cancer survival in Switzerland (1976-2013): Time-trends and predictors.

Science.gov (United States)

Schindler, Matthias; Belle, Fabiën N; Grotzer, Michael A; von der Weid, Nicolas X; Kuehni, Claudia E

2017-01-01

Population-based studies on childhood cancer survival are key to monitor progress against cancer and to detect potential differences between regions and other subgroups in the population. We investigated time trends and factors associated with childhood cancer survival on a national level in Switzerland, from 1976 to 2013. We extracted data from the population-based Swiss Childhood Cancer Registry of 5,776 children (age 0-14 years) diagnosed with cancer from 1985 to 2014 in Switzerland. We calculated age-adjusted 5-year survival, defined the annual reduction in risk of death (ARR), and explored associations of survival with clinical and demographic factors. Overall, 5-year survival improved significantly, from 64% in 1976-1983 to 88% in 2004-2013. ARR over the whole period was 4% for all diagnostic groups, greatest for Hodgkin lymphomas (8%), ependymomas (6%), Burkitt's lymphomas (6%) and germ cell tumours (6%). Children treated in hospitals without specialised paediatric cancer centre for leukaemia (HR 12.9), lymphoma (HR 5.0) and neuroblastoma (HR 3.7) were at higher risk of death. In French-speaking Switzerland, risk of death was lower for lymphoma (HR 0.6), CNS tumours (HR 0.7) and neuroblastoma (HR 0.5). Children with migration background had a higher risk of death from all tumours except bone tumours. Childhood cancer survival significantly improved from 1976 to 2013, but there is room for further improvement. Survival rates varied by type of clinical treatment, language region and nationality. All paediatric cancer patients should be referred to a specialised paediatric cancer centre. Further research is needed to intervene and completely eliminate inequalities in survival. © 2016 UICC.

Effect of fibrinolysis inhibitors on survival time of irradiated rats

International Nuclear Information System (INIS)

Smok, W.

1988-01-01

The possibilities of alleviation of the gastrointestinal syndrome of acute radiation sickness by modification of haemorrhagic diathesis using EACA and traskolan were studied. A significant prolongation of the mean survival time was obtained in the irradiated rats treated with EACA. 7 tabs., 10 refs. (author)

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.

Providing Survivable Real-Time Communication Service for Distributed Mission Critical Systems

National Research Council Canada - National Science Library

Zhao, Wei; Bettati, Riccardo; Vaidya, Nitin

2005-01-01

This document is the final report for Providing Survivable Real-Time Communication Service for Distributed Mission Critical Systems, a Texas A AND M project funded through the DARPA Fault Tolerant Networks Program...

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.

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

Analysis of time to event outcomes in randomized controlled trials by generalized additive models.

Directory of Open Access Journals (Sweden)

Christos Argyropoulos

Full Text Available Randomized Controlled Trials almost invariably utilize the hazard ratio calculated with a Cox proportional hazard model as a treatment efficacy measure. Despite the widespread adoption of HRs, these provide a limited understanding of the treatment effect and may even provide a biased estimate when the assumption of proportional hazards in the Cox model is not verified by the trial data. Additional treatment effect measures on the survival probability or the time scale may be used to supplement HRs but a framework for the simultaneous generation of these measures is lacking.By splitting follow-up time at the nodes of a Gauss Lobatto numerical quadrature rule, techniques for Poisson Generalized Additive Models (PGAM can be adopted for flexible hazard modeling. Straightforward simulation post-estimation transforms PGAM estimates for the log hazard into estimates of the survival function. These in turn were used to calculate relative and absolute risks or even differences in restricted mean survival time between treatment arms. We illustrate our approach with extensive simulations and in two trials: IPASS (in which the proportionality of hazards was violated and HEMO a long duration study conducted under evolving standards of care on a heterogeneous patient population.PGAM can generate estimates of the survival function and the hazard ratio that are essentially identical to those obtained by Kaplan Meier curve analysis and the Cox model. PGAMs can simultaneously provide multiple measures of treatment efficacy after a single data pass. Furthermore, supported unadjusted (overall treatment effect but also subgroup and adjusted analyses, while incorporating multiple time scales and accounting for non-proportional hazards in survival data.By augmenting the HR conventionally reported, PGAMs have the potential to support the inferential goals of multiple stakeholders involved in the evaluation and appraisal of clinical trial results under proportional and

Survival times of pre-1950 US women radium dial workers

International Nuclear Information System (INIS)

Stehney, A.F.

1994-01-01

Survival times of US women radium dial workers to the end of 1989 were examined by life table methods. Included were 1301 women rust employed before 1930 and 1242 first employed in 1930-1949. Expected numbers of deaths were estimated from age- and time-specific death rates for US white females. In the early group, 85 deaths from the well-known radium-induced cancers - bone sarcomas and head carcinomas - were observed, but only 724 deaths from aH other causes were observed vs 755 expected. Life shortening (±S.E.) of 1.8 ±0.5 y compared to the general population of US white females was calculated from the time distribution of all deaths in the pre-1930 group. In the 1930--1949 group, 350 deaths were observed vs 343 expected and no bone sarcomas or head carcinomas occurred. Among women who survived at least 2 y after rust measurement of body radium, a significant excess of observed vs expected deaths was found only for radium intakes greater than 1.85 MBq of 226 Ra + 228 Ra, and no trend of deaths or reduction of life expectancy was found with length of employment

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

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....

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.

Survival time and effect of selected predictor variables on survival in owned pet cats seropositive for feline immunodeficiency and leukemia virus attending a referral clinic in northern Italy.

Science.gov (United States)

Spada, Eva; Perego, Roberta; Sgamma, Elena Assunta; Proverbio, Daniela

2018-02-01

Feline immunodeficiency virus (FIV) and feline leukemia virus (FeLV) are among the most important feline infectious diseases worldwide. This retrospective study investigated survival times and effects of selected predictor factors on survival time in a population of owned pet cats in Northern Italy testing positive for the presence of FIV antibodies and FeLV antigen. One hundred and three retrovirus-seropositive cats, 53 FIV-seropositive cats, 40 FeLV-seropositive cats, and 10 FIV+FeLV-seropositive cats were included in the study. A population of 103 retrovirus-seronegative age and sex-matched cats was selected. Survival time was calculated and compared between retrovirus-seronegative, FIV, FeLV and FIV+FeLV-seropositive cats using Kaplan-Meier survival analysis. Cox proportional-hazards regression analysis was used to study the effect of selected predictor factors (male gender, peripheral blood cytopenia as reduced red blood cells - RBC- count, leukopenia, neutropenia and lymphopenia, hypercreatininemia and reduced albumin to globulin ratio) on survival time in retrovirus-seropositive populations. Median survival times for seronegative cats, FIV, FeLV and FIV+FeLV-seropositive cats were 3960, 2040, 714 and 77days, respectively. Compared to retrovirus-seronegative cats median survival time was significantly lower (P<0.000) in FeLV and FIV+FeLV-seropositive cats. Median survival time in FeLV and FIV+FeLV-seropositive cats was also significant lower (P<0.000) when compared to FIV-seropositive cats. Hazard ratio of death in FeLV and FIV+FeLV-seropositive cats being respectively 3.4 and 7.4 times higher, in comparison to seronegative cats and 2.3 and 4.8 times higher in FeLV and FIV+FeLV-seropositive cats as compared to FIV-seropositive cats. A Cox proportional-hazards regression analysis showed that FIV and FeLV-seropositive cats with reduced RBC counts at time of diagnosis of seropositivity had significantly shorter survival times when compared to FIV and Fe

Analyzing survival curves at a fixed point in time for paired and clustered right-censored data

Science.gov (United States)

Su, Pei-Fang; Chi, Yunchan; Lee, Chun-Yi; Shyr, Yu; Liao, Yi-De

2018-01-01

In clinical trials, information about certain time points may be of interest in making decisions about treatment effectiveness. Rather than comparing entire survival curves, researchers can focus on the comparison at fixed time points that may have a clinical utility for patients. For two independent samples of right-censored data, Klein et al. (2007) compared survival probabilities at a fixed time point by studying a number of tests based on some transformations of the Kaplan-Meier estimators of the survival function. However, to compare the survival probabilities at a fixed time point for paired right-censored data or clustered right-censored data, their approach would need to be modified. In this paper, we extend the statistics to accommodate the possible within-paired correlation and within-clustered correlation, respectively. We use simulation studies to present comparative results. Finally, we illustrate the implementation of these methods using two real data sets. PMID:29456280

Gene expression profiling of canine osteosarcoma reveals genes associated with short and long survival times

Directory of Open Access Journals (Sweden)

Rao Nagesha AS

2009-09-01

Full Text Available Abstract Background Gene expression profiling of spontaneous tumors in the dog offers a unique translational opportunity to identify prognostic biomarkers and signaling pathways that are common to both canine and human. Osteosarcoma (OS accounts for approximately 80% of all malignant bone tumors in the dog. Canine OS are highly comparable with their human counterpart with respect to histology, high metastatic rate and poor long-term survival. This study investigates the prognostic gene profile among thirty-two primary canine OS using canine specific cDNA microarrays representing 20,313 genes to identify genes and cellular signaling pathways associated with survival. This, the first report of its kind in dogs with OS, also demonstrates the advantages of cross-species comparison with human OS. Results The 32 tumors were classified into two prognostic groups based on survival time (ST. They were defined as short survivors (dogs with poor prognosis: surviving fewer than 6 months and long survivors (dogs with better prognosis: surviving 6 months or longer. Fifty-one transcripts were found to be differentially expressed, with common upregulation of these genes in the short survivors. The overexpressed genes in short survivors are associated with possible roles in proliferation, drug resistance or metastasis. Several deregulated pathways identified in the present study, including Wnt signaling, Integrin signaling and Chemokine/cytokine signaling are comparable to the pathway analysis conducted on human OS gene profiles, emphasizing the value of the dog as an excellent model for humans. Conclusion A molecular-based method for discrimination of outcome for short and long survivors is useful for future prognostic stratification at initial diagnosis, where genes and pathways associated with cell cycle/proliferation, drug resistance and metastasis could be potential targets for diagnosis and therapy. The similarities between human and canine OS makes the

Using Concurrent Cardiovascular Information to Augment Survival Time Data for Evaluating Orthostatic Tilt Test Performance

Science.gov (United States)

Feiveson, Alan H.; Fiedler, James; Lee, Stuart M. C.; Koslovsky, Matthew D.; Stenger, Michael B.; Platts, Steven H.

2018-01-01

Head-up tilt (HUT) tests often are used in research to measure orthostatic intolerance (OI) (inability to appropriately control blood pressure while upright) in clinical populations and otherwise healthy individuals after interventions. Post-space flight orthostatic intolerance is a well-known phenomenon, and countermeasures to its development has been an active area of research at NASA. In the NASA HUT protocol, subjects lie horizontally on an automatic tilt table for baseline measurements before being raised to 80deg head-up tilt for a defined period of time or until signs or symptoms of presyncope ensues (light-headedness, nausea, dizziness, sweating, weakness or fainting). Multiple measures are collected to evaluate the cardiovascular system's ability to respond appropriately to the orthostatic challenge. However if the intended duration of the HUT is short, the ability to detect changes in OI due to an intervention or its prevention by a countermeasure may be limited by a small number of failures to permit comparisons based on survival time alone. Thus, the time-trajectory of the cardiovascular data becomes an important additional source of information. In particular, we will show how various measures of trajectory variability can effectively augment survival analysis for the assessment of OI in a joint model when high censoring rates are present.

Lessons from Cacti: How to Survive the Prickles of Life during Tough Times

Science.gov (United States)

Bigger, Alan S.; Bigger, Linda B.

2009-01-01

The saguaro cactus looked a little like humans, in different shapes and sizes. How on earth do they survive in a climate that seems so inhospitable? It is possible to learn lessons for life from a cactus, if one can only get beyond the thorns, and that these lessons will assist one to survive during tough or prickly times. These plants survive…

Effect of liquid nitrogen storage time on the survival and ...

African Journals Online (AJOL)

Investigations were undertaken on the effect of liquid nitrogen (LN) storage time on survival and regeneration of somatic embryos of cocoa (Theobroma cacao l.). Somatic embryos from different cocoa genotypes (AMAZ 3-2, AMAZ 10-1, AMAZ 12, SIAL 93, and IMC 14) at 15.45% moisture content were cryopreserved in LN ...

What affects local community hospitals' survival in turbulent times?

Science.gov (United States)

Chiang, Hung-Che; Wang, Shiow-Ing

2015-06-01

Hospital closures became a prevalent phenomenon in Taiwan after the implementation of a national health insurance program. A wide range of causes contributes to the viability of hospitals, but little is known about the situation under universal coverage health systems. The purpose of present study is to recognize the factors that may contribute to hospital survival under the universal coverage health system. This is a retrospective case-control study. Local community hospitals that contracted with the Bureau of National Health Insurance in 1998 and remained open during the period 1998-2011 are the designated cases. Controls are local community hospitals that closed during the same period. Using longitudinal representative health claim data, 209 local community hospitals that closed during 1998-2011 were compared with 165 that remained open. Variables related to institutional characteristics, degree of competition, characteristics of patients and financial performance were analyzed by logistic regression models. Hospitals' survival was positively related to specialty hospital, the number of respiratory care beds, the physician to population ratio, the number of clinics in the same region, a highly competitive market and the occupancy rate of elderly patients in the hospital. Teaching hospitals, investor-owned hospitals, the provision of obstetrics services or home care, and the number of medical centers or other local community hospitals may jeopardize the chance of survival. Factors-enhanced local hospitals to survive under the universal coverage health system have been identified. Hospital managers could manipulate these findings and adapt strategies for subsistence. © The Author 2015. Published by Oxford University Press in association with the International Society for Quality in Health Care; all rights reserved.

Expectation-maximization algorithms for learning a finite mixture of univariate survival time distributions from partially specified class values

Energy Technology Data Exchange (ETDEWEB)

Lee, Youngrok [Iowa State Univ., Ames, IA (United States)

2013-05-15

Heterogeneity exists on a data set when samples from di erent classes are merged into the data set. Finite mixture models can be used to represent a survival time distribution on heterogeneous patient group by the proportions of each class and by the survival time distribution within each class as well. The heterogeneous data set cannot be explicitly decomposed to homogeneous subgroups unless all the samples are precisely labeled by their origin classes; such impossibility of decomposition is a barrier to overcome for estimating nite mixture models. The expectation-maximization (EM) algorithm has been used to obtain maximum likelihood estimates of nite mixture models by soft-decomposition of heterogeneous samples without labels for a subset or the entire set of data. In medical surveillance databases we can find partially labeled data, that is, while not completely unlabeled there is only imprecise information about class values. In this study we propose new EM algorithms that take advantages of using such partial labels, and thus incorporate more information than traditional EM algorithms. We particularly propose four variants of the EM algorithm named EM-OCML, EM-PCML, EM-HCML and EM-CPCML, each of which assumes a specific mechanism of missing class values. We conducted a simulation study on exponential survival trees with five classes and showed that the advantages of incorporating substantial amount of partially labeled data can be highly signi cant. We also showed model selection based on AIC values fairly works to select the best proposed algorithm on each specific data set. A case study on a real-world data set of gastric cancer provided by Surveillance, Epidemiology and End Results (SEER) program showed a superiority of EM-CPCML to not only the other proposed EM algorithms but also conventional supervised, unsupervised and semi-supervised learning algorithms.

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

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...

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.

Child mortality inequalities across Rwanda districts: a geoadditive continuous-time survival analysis

Directory of Open Access Journals (Sweden)

François Niragire

2017-05-01

Full Text Available Child survival programmes are efficient when they target the most significant and area-specific factors. This study aimed to assess the key determinants and spatial variation of child mortality at the district level in Rwanda. Data from the 2010 Rwanda Demographic and Health Survey were analysed for 8817 live births that occurred during five years preceding the survey. Out of the children born, 433 had died before survey interviews were carried out. A full Bayesian geo-additive continuous-time hazard model enabled us to maximise data utilisation and hence improve the accuracy of our estimates. The results showed substantial district- level spatial variation in childhood mortality in Rwanda. District-specific spatial characteristics were particularly associated with higher death hazards in two districts: Musanze and Nyabihu. The model estimates showed that there were lower death rates among children from households of medium and high economic status compared to those from low-economic status households. Factors, such as four antenatal care visits, delivery at a health facility, prolonged breastfeeding and mothers younger than 31 years were associated with lower child death rates. Long preceding birth intervals were also associated with fewer hazards. For these reasons, programmes aimed at reducing child mortality gaps between districts in Rwanda should target maternal factors and take into consideration district-specific spatial characteristics. Further, child survival gains require strengthening or scaling-up of existing programmes pertaining to access to, and utilisation of maternal and child health care services as well as reduction of the household gap in the economic status.

Child mortality inequalities across Rwanda districts: a geoadditive continuous-time survival analysis.

Science.gov (United States)

Niragire, François; Achia, Thomas N O; Lyambabaje, Alexandre; Ntaganira, Joseph

2017-05-11

Child survival programmes are efficient when they target the most significant and area-specific factors. This study aimed to assess the key determinants and spatial variation of child mortality at the district level in Rwanda. Data from the 2010 Rwanda Demographic and Health Survey were analysed for 8817 live births that occurred during five years preceding the survey. Out of the children born, 433 had died before survey interviews were carried out. A full Bayesian geo-additive continuous-time hazard model enabled us to maximise data utilisation and hence improve the accuracy of our estimates. The results showed substantial district- level spatial variation in childhood mortality in Rwanda. District-specific spatial characteristics were particularly associated with higher death hazards in two districts: Musanze and Nyabihu. The model estimates showed that there were lower death rates among children from households of medium and high economic status compared to those from low-economic status households. Factors, such as four antenatal care visits, delivery at a health facility, prolonged breastfeeding and mothers younger than 31 years were associated with lower child death rates. Long preceding birth intervals were also associated with fewer hazards. For these reasons, programmes aimed at reducing child mortality gaps between districts in Rwanda should target maternal factors and take into consideration district-specific spatial characteristics. Further, child survival gains require strengthening or scaling-up of existing programmes pertaining to access to, and utilisation of maternal and child health care services as well as reduction of the household gap in the economic status.

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)

Median Survival Time of Endometrial Cancer Patients with Lymphovascular Invasion at the Hospital Universiti Sains Malaysia.

Science.gov (United States)

Asyikeen, Wan Adnan Wan Nor; Siti-Azrin, Ab Hamid; Jalil, Nur Asyilla Che; Zin, Anani Aila Mat; Othman, Nor Hayati

2016-11-01

Endometrial cancer is the most common gynaecologic malignancy among females worldwide. The purpose of this study was to determine the median survival time of endometrial cancer patients at the Hospital Universiti Sains Malaysia (USM). A list of 121 endometrial cancer cases registered at Hospital USM between 2000 until 2011 was retrospectively reviewed. The survival time of the endometrial cancer patients was estimated by Kaplan-Meier survival analysis. Log-rank tests were performed to compare the survival of the patients based on socio-demographics and clinical presentation. Only 108 patients, 87.0%, were included who were of Malay ethnicity. Previous history included menopause in 67.6% of patients and diabetes mellitus in 39.8% of patients; additionally, 63.4% of patients were nulliparous. Tumour staging was as follows: 24.5% stage I, 10.8% stage II, 26.5% stage III and 38.2% stage IV. The overall median survival time of the endometrial cancer patients was 70.20 months (95% confidence interval (CI): 51.79, 88.61). The significant factors were age, the presence of lymphovascular invasion and treatment received. The overall survival of endometrial cancer was low. A prospective study needs to be carried out to discover more effective and accurate tests for the early detection of endometrial cancer.

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

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...

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.

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.

Polygenic Score × Intervention Moderation: an Application of Discrete-Time Survival Analysis to Model the Timing of First Marijuana Use Among Urban Youth.

Science.gov (United States)

Musci, Rashelle J; Fairman, Brian; Masyn, Katherine E; Uhl, George; Maher, Brion; Sisto, Danielle Y; Kellam, Sheppard G; Ialongo, Nicholas S

2018-01-01

The present study examines the interaction between a polygenic score and an elementary school-based universal preventive intervention trial and its effects on a discrete-time survival analysis of time to first smoking marijuana. Research has suggested that initiation of substances is both genetically and environmentally driven (Rhee et al., Archives of general psychiatry 60:1256-1264, 2003; Verweij et al., Addiction 105:417-430, 2010). A previous work has found a significant interaction between the polygenic score and the same elementary school-based intervention with tobacco smoking (Musci et al., in press). The polygenic score reflects the contribution of multiple genes and has been shown in prior research to be predictive of smoking cessation, tobacco use, and marijuana use (Uhl et al., Molecular Psychiatry 19:50-54, 2014). Using data from a longitudinal preventive intervention study (N = 678), we examined age of first marijuana use from sixth grade to age 18. Genetic data were collected during emerging adulthood and were genotyped using the Affymetrix 6.0 microarray (N = 545). The polygenic score was computed using these data. Discrete-time survival analysis was employed to test for intervention main and interaction effects with the polygenic score. We found main effect of the polygenic score approaching significance, with the participants with higher polygenic scores reporting their first smoking marijuana at an age significantly later than controls (p = .050). We also found a significant intervention × polygenic score interaction effect at p = .003, with participants at the higher end of the polygenic score benefiting the most from the intervention in terms of delayed age of first use. These results suggest that genetics may play an important role in the age of first use of marijuana and that differences in genetics may account for the differential effectiveness of classroom-based interventions in delaying substance use experimentation.

33 CFR 150.503 - What are the time interval requirements for maintenance on survival craft falls?

Science.gov (United States)

2010-07-01

... 33 Navigation and Navigable Waters 2 2010-07-01 2010-07-01 false What are the time interval requirements for maintenance on survival craft falls? 150.503 Section 150.503 Navigation and Navigable Waters... maintenance on survival craft falls? (a) Each fall used in a launching device for survival craft or rescue...

Timely disclosure of progress in long-term cancer survival: the boomerang method substantially improved estimates in a comparative study.

Science.gov (United States)

Brenner, Hermann; Jansen, Lina

2016-02-01

Monitoring cancer survival is a key task of cancer registries, but timely disclosure of progress in long-term survival remains a challenge. We introduce and evaluate a novel method, denoted "boomerang method," for deriving more up-to-date estimates of long-term survival. We applied three established methods (cohort, complete, and period analysis) and the boomerang method to derive up-to-date 10-year relative survival of patients diagnosed with common solid cancers and hematological malignancies in the United States. Using the Surveillance, Epidemiology and End Results 9 database, we compared the most up-to-date age-specific estimates that might have been obtained with the database including patients diagnosed up to 2001 with 10-year survival later observed for patients diagnosed in 1997-2001. For cancers with little or no increase in survival over time, the various estimates of 10-year relative survival potentially available by the end of 2001 were generally rather similar. For malignancies with strongly increasing survival over time, including breast and prostate cancer and all hematological malignancies, the boomerang method provided estimates that were closest to later observed 10-year relative survival in 23 of the 34 groups assessed. The boomerang method can substantially improve up-to-dateness of long-term cancer survival estimates in times of ongoing improvement in prognosis. Copyright © 2016 Elsevier Inc. All rights reserved.

Single nucleotide polymorphisms in the D-loop region of mitochondrial DNA is associated with the kidney survival time in chronic kidney disease patients.

Science.gov (United States)

Xu, Jinsheng; Guo, Zhanjun; Bai, Yaling; Zhang, Junxia; Cui, Liwen; Zhang, Huiran; Zhang, Shenglei; Ai, Xiaolu

2015-02-01

The mitochondrial displacement loop (D-loop) is known to accumulate mutations and SNPs at a higher frequency than other regions of mitochondrial DNA (mtDNA). We had identified chronic kidney disease (CKD) risk-associated SNPs in the D-loop of CKD patients previously. In this study, we investigated the association of SNPs in the D-loop of mtDNA with the kidney survival of CKD. The D-loop region of mtDNA was sequenced for 119 CKD patients from the inpatient of the Fourth Hospital of Hebei Medical University. The Kaplan-Meier method was used to identify disease outcome-associated SNPs in the D-loop of CKD patients. The Cox proportional hazards model was used to identify risk factors for the kidney survival of CKD. In the present study, we identified 20 SNPs with a frequency higher than 5% and assessed the relationship of these SNPs with kidney survival time in CKD patients, a SNP of 146 was identified by log-rank test for statistically significant prediction of the kidney survival time. In an overall multivariate analysis, allele 146 was identified as an independent predictor of kidney survival time in CKD patients. The survival time of kidney in the CKD patients with 146C was significantly shorter than that of kidney in CKD patients with 146T (relative risk, 2.336; 95% CI, 1.319-3.923; p = 0.001). SNPs in the D-loop can predict the kidney survival of CKD patients. Analysis of genetic polymorphisms in the mitochondrial D-loop can help to identify CKD patient subgroup at high risk of a poor disease outcome.

Palliative Sedation in Advanced Cancer Patients: Does it Shorten Survival Time? - A Systematic Review.

Science.gov (United States)

Barathi, B; Chandra, Prabha S

2013-01-01

Patients with advanced cancer often suffer from multiple refractory symptoms in the terminal phase of their life. Palliative sedation is one of the few ways to relieve this refractory suffering. This systematic review investigated the effect of palliative sedation on survival time in terminally ill cancer patients. Six electronic databases were searched for both prospective and retrospective studies which evaluated the effect of palliative sedation on survival time. Only those studies which had a comparison group that did not receive palliative sedation were selected for the review. Abstracts of all retrieved studies were screened to include the most relevant studies and only studies which met inclusion criteria were selected. References of all retrieved studies were also screened for relevant studies. Selected studies were assessed for quality and data extraction was done using the structured data extraction form. Eleven studies including four prospective and seven retrospective studies were identified. Mean survival time (MST) was measured as the time from last admission until death. A careful analysis of the results of all the 11 studies indicated that MST of sedated and non-sedated group was not statistically different in any of the studies. This systematic review supports the fact that palliative sedation does not shorten survival in terminally ill cancer patients. However, this conclusion needs to be taken with consideration of the methodology, study design, and the population studied of the included studies in this review.

Predicting survival time in noncurative patients with advanced cancer: a prospective study in China.

Science.gov (United States)

Cui, Jing; Zhou, Lingjun; Wee, B; Shen, Fengping; Ma, Xiuqiang; Zhao, Jijun

2014-05-01

Accurate prediction of prognosis for cancer patients is important for good clinical decision making in therapeutic and care strategies. The application of prognostic tools and indicators could improve prediction accuracy. This study aimed to develop a new prognostic scale to predict survival time of advanced cancer patients in China. We prospectively collected items that we anticipated might influence survival time of advanced cancer patients. Participants were recruited from 12 hospitals in Shanghai, China. We collected data including demographic information, clinical symptoms and signs, and biochemical test results. Log-rank tests, Cox regression, and linear regression were performed to develop a prognostic scale. Three hundred twenty patients with advanced cancer were recruited. Fourteen prognostic factors were included in the prognostic scale: Karnofsky Performance Scale (KPS) score, pain, ascites, hydrothorax, edema, delirium, cachexia, white blood cell (WBC) count, hemoglobin, sodium, total bilirubin, direct bilirubin, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) values. The score was calculated by summing the partial scores, ranging from 0 to 30. When using the cutoff points of 7-day, 30-day, 90-day, and 180-day survival time, the scores were calculated as 12, 10, 8, and 6, respectively. We propose a new prognostic scale including KPS, pain, ascites, hydrothorax, edema, delirium, cachexia, WBC count, hemoglobin, sodium, total bilirubin, direct bilirubin, AST, and ALP values, which may help guide physicians in predicting the likely survival time of cancer patients more accurately. More studies are needed to validate this scale in the future.

Palliative sedation in advanced cancer patients: Does it shorten survival time? - A systematic review

Directory of Open Access Journals (Sweden)

B Barathi

2013-01-01

Full Text Available Background: Patients with advanced cancer often suffer from multiple refractory symptoms in the terminal phase of their life. Palliative sedation is one of the few ways to relieve this refractory suffering. Objectives: This systematic review investigated the effect of palliative sedation on survival time in terminally ill cancer patients. Materials and Methods: Six electronic databases were searched for both prospective and retrospective studies which evaluated the effect of palliative sedation on survival time. Only those studies which had a comparison group that did not receive palliative sedation were selected for the review. Abstracts of all retrieved studies were screened to include the most relevant studies and only studies which met inclusion criteria were selected. References of all retrieved studies were also screened for relevant studies. Selected studies were assessed for quality and data extraction was done using the structured data extraction form. Results: Eleven studies including four prospective and seven retrospective studies were identified. Mean survival time (MST was measured as the time from last admission until death. A careful analysis of the results of all the 11 studies indicated that MST of sedated and non-sedated group was not statistically different in any of the studies. Conclusion: This systematic review supports the fact that palliative sedation does not shorten survival in terminally ill cancer patients. However, this conclusion needs to be taken with consideration of the methodology, study design, and the population studied of the included studies in this review.

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....

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.

Survivable architectures for time and wavelength division multiplexed passive optical networks

Science.gov (United States)

Wong, Elaine

2014-08-01

The increased network reach and customer base of next-generation time and wavelength division multiplexed PON (TWDM-PONs) have necessitated rapid fault detection and subsequent restoration of services to its users. However, direct application of existing solutions for conventional PONs to TWDM-PONs is unsuitable as these schemes rely on the loss of signal (LOS) of upstream transmissions to trigger protection switching. As TWDM-PONs are required to potentially use sleep/doze mode optical network units (ONU), the loss of upstream transmission from a sleeping or dozing ONU could erroneously trigger protection switching. Further, TWDM-PONs require its monitoring modules for fiber/device fault detection to be more sensitive than those typically deployed in conventional PONs. To address the above issues, three survivable architectures that are compliant with TWDM-PON specifications are presented in this work. These architectures combine rapid detection and protection switching against multipoint failure, and most importantly do not rely on upstream transmissions for LOS activation. Survivability analyses as well as evaluations of the additional costs incurred to achieve survivability are performed and compared to the unprotected TWDM-PON. Network parameters that impact the maximum achievable network reach, maximum split ratio, connection availability, fault impact, and the incremental reliability costs for each proposed survivable architecture are highlighted.

Accelerated failure time regression for backward recurrence times and current durations

DEFF Research Database (Denmark)

Keiding, N; Fine, J P; Hansen, O H

2011-01-01

Backward recurrence times in stationary renewal processes and current durations in dynamic populations observed at a cross-section may yield estimates of underlying interarrival times or survival distributions under suitable stationarity assumptions. Regression models have been proposed for these......Backward recurrence times in stationary renewal processes and current durations in dynamic populations observed at a cross-section may yield estimates of underlying interarrival times or survival distributions under suitable stationarity assumptions. Regression models have been proposed...... for these situations, but accelerated failure time models have the particularly attractive feature that they are preserved when going from the backward recurrence times to the underlying survival distribution of interest. This simple fact has recently been noticed in a sociological context and is here illustrated...... by a study of current duration of time to pregnancy...

PSO-Based Robot Path Planning for Multisurvivor Rescue in Limited Survival Time

Directory of Open Access Journals (Sweden)

N. Geng

2014-01-01

Full Text Available Since the strength of a trapped person often declines with time in urgent and dangerous circumstances, adopting a robot to rescue as many survivors as possible in limited time is of considerable significance. However, as one key issue in robot navigation, how to plan an optimal rescue path of a robot has not yet been fully solved. This paper studies robot path planning for multisurvivor rescue in limited survival time using a representative heuristic, particle swarm optimization (PSO. First, the robot path planning problem including multiple survivors is formulated as a discrete optimization one with high constraint, where the number of rescued persons is taken as the unique objective function, and the strength of a trapped person is used to constrain the feasibility of a path. Then, a new integer PSO algorithm is presented to solve the mathematical model, and several new operations, such as the update of a particle, the insertion and inversion operators, and the rapidly local search method, are incorporated into the proposed algorithm to improve its effectiveness. Finally, the simulation results demonstrate the capacity of our method in generating optimal paths with high quality.

Enhancement of radiation effect on mouse intestinal crypt survival by timing of 5-fluorouracil administration

International Nuclear Information System (INIS)

Ho, E.; Coffey, C.; Maruyama, Y.

1977-01-01

There is a marked dependence of mouse crypt survival on the sequence of combined drug-radiation treatment and on the time lapse between irradiation and drug administration. When 5-fluorouracil is administered 6 hours after irradiation or later (up to 18 hours postirradiation), crypt survival drops significantly

Times of analgesic efficacy of two drugs in the treatment of patients with renal-ureteral colic compared by survival models

Directory of Open Access Journals (Sweden)

Luis Reyes Velázquez

2015-01-01

Full Text Available Renourethral colic is a very painful clinical situation that requires a quick diagnosis and treatment. A study was done with patients who were administered two types of analgesics, and whose pain was measured through a visual analogue scale. Censored data results were obtained, considering the time when the pain disappeared as the random variable. Maximum likelihood and survival analysis give useful methods to estimate the distribution and parametric functions for this variable. This study will allow a more effective, timely, lower cost and suitable medical treatment for patients.

KMWin--a convenient tool for graphical presentation of results from Kaplan-Meier survival time analysis.

Science.gov (United States)

Gross, Arnd; Ziepert, Marita; Scholz, Markus

2012-01-01

Analysis of clinical studies often necessitates multiple graphical representations of the results. Many professional software packages are available for this purpose. Most packages are either only commercially available or hard to use especially if one aims to generate or customize a huge number of similar graphical outputs. We developed a new, freely available software tool called KMWin (Kaplan-Meier for Windows) facilitating Kaplan-Meier survival time analysis. KMWin is based on the statistical software environment R and provides an easy to use graphical interface. Survival time data can be supplied as SPSS (sav), SAS export (xpt) or text file (dat), which is also a common export format of other applications such as Excel. Figures can directly be exported in any graphical file format supported by R. On the basis of a working example, we demonstrate how to use KMWin and present its main functions. We show how to control the interface, customize the graphical output, and analyse survival time data. A number of comparisons are performed between KMWin and SPSS regarding graphical output, statistical output, data management and development. Although the general functionality of SPSS is larger, KMWin comprises a number of features useful for survival time analysis in clinical trials and other applications. These are for example number of cases and number of cases under risk within the figure or provision of a queue system for repetitive analyses of updated data sets. Moreover, major adjustments of graphical settings can be performed easily on a single window. We conclude that our tool is well suited and convenient for repetitive analyses of survival time data. It can be used by non-statisticians and provides often used functions as well as functions which are not supplied by standard software packages. The software is routinely applied in several clinical study groups.

KMWin--a convenient tool for graphical presentation of results from Kaplan-Meier survival time analysis.

Directory of Open Access Journals (Sweden)

Arnd Gross

Full Text Available BACKGROUND: Analysis of clinical studies often necessitates multiple graphical representations of the results. Many professional software packages are available for this purpose. Most packages are either only commercially available or hard to use especially if one aims to generate or customize a huge number of similar graphical outputs. We developed a new, freely available software tool called KMWin (Kaplan-Meier for Windows facilitating Kaplan-Meier survival time analysis. KMWin is based on the statistical software environment R and provides an easy to use graphical interface. Survival time data can be supplied as SPSS (sav, SAS export (xpt or text file (dat, which is also a common export format of other applications such as Excel. Figures can directly be exported in any graphical file format supported by R. RESULTS: On the basis of a working example, we demonstrate how to use KMWin and present its main functions. We show how to control the interface, customize the graphical output, and analyse survival time data. A number of comparisons are performed between KMWin and SPSS regarding graphical output, statistical output, data management and development. Although the general functionality of SPSS is larger, KMWin comprises a number of features useful for survival time analysis in clinical trials and other applications. These are for example number of cases and number of cases under risk within the figure or provision of a queue system for repetitive analyses of updated data sets. Moreover, major adjustments of graphical settings can be performed easily on a single window. CONCLUSIONS: We conclude that our tool is well suited and convenient for repetitive analyses of survival time data. It can be used by non-statisticians and provides often used functions as well as functions which are not supplied by standard software packages. The software is routinely applied in several clinical study groups.

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...

Integration of RNA-Seq and RPPA data for survival time prediction in cancer patients.

Science.gov (United States)

Isik, Zerrin; Ercan, Muserref Ece

2017-10-01

Integration of several types of patient data in a computational framework can accelerate the identification of more reliable biomarkers, especially for prognostic purposes. This study aims to identify biomarkers that can successfully predict the potential survival time of a cancer patient by integrating the transcriptomic (RNA-Seq), proteomic (RPPA), and protein-protein interaction (PPI) data. The proposed method -RPBioNet- employs a random walk-based algorithm that works on a PPI network to identify a limited number of protein biomarkers. Later, the method uses gene expression measurements of the selected biomarkers to train a classifier for the survival time prediction of patients. RPBioNet was applied to classify kidney renal clear cell carcinoma (KIRC), glioblastoma multiforme (GBM), and lung squamous cell carcinoma (LUSC) patients based on their survival time classes (long- or short-term). The RPBioNet method correctly identified the survival time classes of patients with between 66% and 78% average accuracy for three data sets. RPBioNet operates with only 20 to 50 biomarkers and can achieve on average 6% higher accuracy compared to the closest alternative method, which uses only RNA-Seq data in the biomarker selection. Further analysis of the most predictive biomarkers highlighted genes that are common for both cancer types, as they may be driver proteins responsible for cancer progression. The novelty of this study is the integration of a PPI network with mRNA and protein expression data to identify more accurate prognostic biomarkers that can be used for clinical purposes in the future. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

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

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.

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.

Flow cytometric and radioisotopic determinations of platelet survival time in normal cats and feline leukemia virus-infected cats

International Nuclear Information System (INIS)

Jacobs, R.M.; Boyce, J.T.; Kociba, G.J.

1986-01-01

This study demonstrates the potential usefulness of a flow cytometric technique to measure platelet survival time in cats utilizing autologous platelets labeled in vitro with fluorescein isothiocyanate (FITC). When compared with a 51Cr method, no significant differences in estimated survival times were found. Both the 51Cr and FITC-labeling procedures induced similar changes in platelet shape and collagen-induced aggregation. Platelets labeled with FITC had significantly greater volumes compared with those of glutaraldehyde-fixed platelets. These changes were primarily related to the platelet centrifugation and washing procedures rather than the labels themselves. This novel technique potentially has wide applicability to cell circulation time studies as flow cytometry equipment becomes more readily available. Problems with the technique are discussed. In a preliminary study of the platelet survival time in feline leukemia virus (FeLV)-infected cats, two of three cats had significantly reduced survival times using both flow cytometric and radioisotopic methods. These data suggest increased platelet turnover in FeLV-infected cats

Flow cytometric and radioisotopic determinations of platelet survival time in normal cats and feline leukemia virus-infected cats

Energy Technology Data Exchange (ETDEWEB)

Jacobs, R.M.; Boyce, J.T.; Kociba, G.J.

1986-01-01

This study demonstrates the potential usefulness of a flow cytometric technique to measure platelet survival time in cats utilizing autologous platelets labeled in vitro with fluorescein isothiocyanate (FITC). When compared with a 51Cr method, no significant differences in estimated survival times were found. Both the 51Cr and FITC-labeling procedures induced similar changes in platelet shape and collagen-induced aggregation. Platelets labeled with FITC had significantly greater volumes compared with those of glutaraldehyde-fixed platelets. These changes were primarily related to the platelet centrifugation and washing procedures rather than the labels themselves. This novel technique potentially has wide applicability to cell circulation time studies as flow cytometry equipment becomes more readily available. Problems with the technique are discussed. In a preliminary study of the platelet survival time in feline leukemia virus (FeLV)-infected cats, two of three cats had significantly reduced survival times using both flow cytometric and radioisotopic methods. These data suggest increased platelet turnover in FeLV-infected cats.

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.

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.

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

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.

Examining the Influence of Campus Climate on Students' Time to Degree: A Multilevel Discrete-Time Survival Analysis

Science.gov (United States)

Zhou, Ji; Castellanos, Michelle

2013-01-01

Utilizing longitudinal data of 3477 students from 28 institutions, we examine the effects of structural diversity and quality of interracial relation on students' persistence towards graduation within six years. We utilize multilevel discrete-time survival analysis to account for the longitudinal persistence patterns as well as the nested…

Time to treatment as a quality metric in lung cancer: Staging studies, time to treatment, and patient survival

International Nuclear Information System (INIS)

Gomez, Daniel R.; Liao, Kai-Ping; Swisher, Stephen G.; Blumenschein, George R.; Erasmus, Jeremy J.; Buchholz, Thomas A.; Giordano, Sharon H.; Smith, Benjamin D.

2015-01-01

Purpose: Prompt staging and treatment are crucial for non-small cell lung cancer (NSCLC). We determined if predictors of treatment delay after diagnosis were associated with prognosis. Materials and methods: Medicare claims from 28,732 patients diagnosed with NSCLC in 2004–2007 were used to establish the diagnosis-to-treatment interval (ideally ⩽35 days) and identify staging studies during that interval. Factors associated with delay were identified with multivariate logistic regression, and associations between delay and survival by stage were tested with Cox proportional hazard regression. Results: Median diagnosis-to-treatment interval was 27 days. Receipt of PET was associated with delays (57.4% of patients with PET delayed [n = 6646/11,583] versus 22.8% of those without [n = 3908/17,149]; adjusted OR = 4.48, 95% CI 4.23–4.74, p < 0.001). Median diagnosis-to-PET interval was 15 days; PET-to-clinic, 5 days; and clinic-to-treatment, 12 days. Diagnosis-to-treatment intervals <35 days were associated with improved survival for patients with localized disease and those with distant disease surviving ⩾1 year but not for patients with distant disease surviving <1 year. Conclusion: Delays between diagnosing and treating NSCLC are common and associated with use of PET for staging. Reducing time to treatment may improve survival for patients with manageable disease at diagnosis

Evaluation of serum biochemical marker concentrations and survival time in dogs with protein-losing enteropathy.

Science.gov (United States)

Equilino, Mirjam; Théodoloz, Vincent; Gorgas, Daniela; Doherr, Marcus G; Heilmann, Romy M; Suchodolski, Jan S; Steiner, Jörg M; Burgener Dvm, Iwan A

2015-01-01

To evaluate serum concentrations of biochemical markers and survival time in dogs with protein-losing enteropathy (PLE). Prospective study. 29 dogs with PLE and 18 dogs with food-responsive diarrhea (FRD). Data regarding serum concentrations of various biochemical markers at the initial evaluation were available for 18 of the 29 dogs with PLE and compared with findings for dogs with FRD. Correlations between biochemical marker concentrations and survival time (interval between time of initial evaluation and death or euthanasia) for dogs with PLE were evaluated. Serum C-reactive protein concentration was high in 13 of 18 dogs with PLE and in 2 of 18 dogs with FRD. Serum concentration of canine pancreatic lipase immunoreactivity was high in 3 dogs with PLE but within the reference interval in all dogs with FRD. Serum α1-proteinase inhibitor concentration was less than the lower reference limit in 9 dogs with PLE and 1 dog with FRD. Compared with findings in dogs with FRD, values of those 3 variables in dogs with PLE were significantly different. Serum calprotectin (measured by radioimmunoassay and ELISA) and S100A12 concentrations were high but did not differ significantly between groups. Seventeen of the 29 dogs with PLE were euthanized owing to this disease; median survival time was 67 days (range, 2 to 2,551 days). Serum C-reactive protein, canine pancreatic lipase immunoreactivity, and α1-proteinase inhibitor concentrations differed significantly between dogs with PLE and FRD. Most initial biomarker concentrations were not predictive of survival time in dogs with PLE.

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.

Progression-free survival/time to progression as a potential surrogate for overall survival in HR+, HER2– metastatic breast cancer

Directory of Open Access Journals (Sweden)

Forsythe A

2018-05-01

Full Text Available Anna Forsythe,1 David Chandiwana,2 Janina Barth,3 Marroon Thabane,4 Johan Baeck,2 Gabriel Tremblay1 1Purple Squirrel Economics, New York, NY, 2Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA; 3Novartis Pharma GmbH, Nuremberg, Germany; 4Novartis Pharmaceuticals Incorporated, Dorval, QC, Canada Background: Several recent randomized controlled trials (RCTs in hormone receptor-positive (HR+, human epidermal growth factor receptor 2-negative (HER2– metastatic breast cancer (MBC have demonstrated significant improvements in progression-free survival (PFS; however, few have reported improvement in overall survival (OS. The surrogacy of PFS or time to progression (TTP for OS has not been formally investigated in HR+, HER2– MBC.Methods: A systematic literature review of RCTs in HR+, HER2– MBC was conducted to identify studies that reported both median PFS/TTP and OS. The correlation between PFS/TTP and OS was evaluated using Pearson’s product–moment correlation and Spearman’s rank correlation. Subgroup analyses were performed to explore possible reasons for heterogeneity. Errors-in-variables weighted least squares regression (LSR was used to model incremental OS months as a function of incremental PFS/TTP months. An exploratory analysis investigated the impact of three covariates (chemotherapy vs hormonal/targeted therapy, PFS vs TTP, and first-line therapy vs second-line therapy or greater on OS prediction. The lower 95% prediction band was used to determine the minimum incremental PFS/TTP months required to predict OS benefit (surrogate threshold effect [STE].Results: Forty studies were identified. There was a statistically significant correlation between median PFS/TTP and OS (Pearson =0.741, P=0.000; Spearman =0.650, P=0.000. These results proved consistent for chemotherapy and hormonal/targeted therapy. Univariate LSR analysis yielded an R2 of 0.354 with 1 incremental PFS/TTP month corresponding to 1.13 incremental OS months

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.

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...

Association of the Timing of Pregnancy With Survival in Women With Breast Cancer

Science.gov (United States)

Iqbal, Javaid; Amir, Eitan; Rochon, Paula A.; Giannakeas, Vasily; Sun, Ping

2017-01-01

Importance Increasing numbers of women experience pregnancy around the time of, or after, a diagnosis of breast cancer. Understanding the effect of pregnancy on survival in women with breast cancer will help in the counseling and treatment of these women. Objective To compare the overall survival of women diagnosed with breast cancer during pregnancy or in the postpartum period with that of women who had breast cancer but did not become pregnant. Design, Setting, and Participants This population-based, retrospective cohort study linked health administrative databases in Ontario, Canada, comprising 7553 women aged 20 to 45 years at the time of diagnosis with invasive breast cancer, from January 1, 2003, to December 31, 2014. Exposures Any pregnancy in the period from 5 years before, until 5 years after, the index date of the diagnosis of breast cancer. Women were classified into the following 4 exposure groups: no pregnancy (the referent), pregnancy before breast cancer, pregnancy-associated breast cancer, and pregnancy following breast cancer. Main Outcomes and Measures Five-year actuarial survival rates for all exposure groups, age-adjusted and multivariable hazard ratios [HRs] of pregnancy for overall survival for all exposure groups, and time-dependent hazard ratios for women with pregnancy following breast cancer. Results Among the 7553 women in the study (mean age at diagnosis, 39.1 years; median, 40 years; range, 20-44 years) the 5-year actuarial survival rate was 87.5% (95% CI, 86.5%-88.4%) for women with no pregnancy, 85.3% (95% CI, 82.8%-87.8%) for women with pregnancy before breast cancer (age-adjusted hazard ratio, 1.03; 95% CI, 0.85-1.27; P = .73), and 82.1% (95% CI, 78.3%-85.9%) for women with pregnancy-associated breast cancer (age-adjusted hazard ratio, 1.18; 95% CI, 0.91-1.53; P = .20). The 5-year actuarial survival rate was 96.7% (95% CI, 94.1%-99.3%) for women who had pregnancy 6 months or more after diagnosis of breast cancer, vs 87

The M1 form of tumor-associated macrophages in non-small cell lung cancer is positively associated with survival time

International Nuclear Information System (INIS)

Ma, Junliang; Liu, Lunxu; Che, Guowei; Yu, Nanbin; Dai, Fuqiang; You, Zongbing

2010-01-01

Tumor-associated macrophages (TAMs) play an important role in growth, progression and metastasis of tumors. In non-small cell lung cancer (NSCLC), TAMs' anti-tumor or pro-tumor role is not determined. Macrophages are polarized into M1 (with anti-tumor function) and M2 (with pro-tumor function) forms. This study was conducted to determine whether the M1 and M2 macrophage densities in NSCLC are associated with patient's survival time. Fifty patients with an average of 1-year survival (short survival group) and 50 patients with an average of 5-year survival (long survival group) were included in this retrospective study. Paraffin-embedded NSCLC specimens and their clinicopathological data including up to 8-year follow-up information were used. Immunohistochemical double-staining of CD68/HLA-DR (markers for M1 macrophages) and CD68/CD163 (markers for M2 macrophages) was performed and evaluated in a blinded fashion. The M1 and M2 macrophage densities in the tumor islets, stroma, or islets and stroma were determined using computer-aided microscopy. Correlation of the macrophage densities and patient's survival time was analyzed using the Statistical Package for the Social Sciences. Approximately 70% of TAMs were M2 macrophages and the remaining 30% were M1 macrophages in NSCLC. The M2 macrophage densities (approximately 78 to 113 per mm 2 ) in the tumor islets, stroma, or islets and stroma were not significantly different between the long survival and short survival groups. The M1 macrophage densities in the tumor islets (approximately 70/mm 2 ) and stroma (approximately 34/mm 2 ) of the long survival group were significantly higher than the M1 macrophage densities in the tumor islets (approximately 7/mm 2 ) and stroma (13/mm 2 ) of the short survival group (P < 0.001 and P < 0.05, respectively). The M2 macrophage densities were not associated with patient's survival time. The M1 macrophage densities in the tumor islets, stroma, or islets and stroma

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.

KMWin – A Convenient Tool for Graphical Presentation of Results from Kaplan-Meier Survival Time Analysis

Science.gov (United States)

Gross, Arnd; Ziepert, Marita; Scholz, Markus

2012-01-01

Background Analysis of clinical studies often necessitates multiple graphical representations of the results. Many professional software packages are available for this purpose. Most packages are either only commercially available or hard to use especially if one aims to generate or customize a huge number of similar graphical outputs. We developed a new, freely available software tool called KMWin (Kaplan-Meier for Windows) facilitating Kaplan-Meier survival time analysis. KMWin is based on the statistical software environment R and provides an easy to use graphical interface. Survival time data can be supplied as SPSS (sav), SAS export (xpt) or text file (dat), which is also a common export format of other applications such as Excel. Figures can directly be exported in any graphical file format supported by R. Results On the basis of a working example, we demonstrate how to use KMWin and present its main functions. We show how to control the interface, customize the graphical output, and analyse survival time data. A number of comparisons are performed between KMWin and SPSS regarding graphical output, statistical output, data management and development. Although the general functionality of SPSS is larger, KMWin comprises a number of features useful for survival time analysis in clinical trials and other applications. These are for example number of cases and number of cases under risk within the figure or provision of a queue system for repetitive analyses of updated data sets. Moreover, major adjustments of graphical settings can be performed easily on a single window. Conclusions We conclude that our tool is well suited and convenient for repetitive analyses of survival time data. It can be used by non-statisticians and provides often used functions as well as functions which are not supplied by standard software packages. The software is routinely applied in several clinical study groups. PMID:22723912

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.

On avian influenza epidemic models with time delay.

Science.gov (United States)

Liu, Sanhong; Ruan, Shigui; Zhang, Xinan

2015-12-01

After the outbreak of the first avian influenza A virus (H5N1) in Hong Kong in 1997, another avian influenza A virus (H7N9) crossed the species barrier in mainland China in 2013 and 2014 and caused more than 400 human cases with a death rate of nearly 40%. In this paper, we take account of the incubation periods of avian influenza A virus and construct a bird-to-human transmission model with different time delays in the avian and human populations combining the survival probability of the infective avian and human populations at the latent time. By analyzing the dynamical behavior of the model, we obtain a threshold value for the prevalence of avian influenza and investigate local and global asymptotical stability of equilibria of the system.

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.

Survival probability of a local excitation in a non-Markovian environment: Survival collapse, Zeno and anti-Zeno effects

International Nuclear Information System (INIS)

Rufeil-Fiori, E.; Pastawski, H.M.

2009-01-01

The decay dynamics of a local excitation interacting with a non-Markovian environment, modeled by a semi-infinite tight-binding chain, is exactly evaluated. We identify distinctive regimes for the dynamics. Sequentially: (i) early quadratic decay of the initial-state survival probability, up to a spreading time t S , (ii) exponential decay described by a self-consistent Fermi Golden Rule, and (iii) asymptotic behavior governed by quantum diffusion through the return processes, leading to an inverse power law decay. At this last cross-over time t R a survival collapse becomes possible. This could reduce the survival probability by several orders of magnitude. The cross-over times t S and t R allow to assess the range of applicability of the Fermi Golden Rule and give the conditions for the observation of the Zeno and anti-Zeno effect.

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.

Effect of 153Sm-EDTMP on survival time in patients with nasopharyngeal carcinoma and multiple bone metastases

International Nuclear Information System (INIS)

Fan Wei; Zheng Zongyuan; Xu Guangpu

2004-01-01

Objective: To evaluate the effect on survival of Samarium-153-ethylene diamine tetramethylene phosphonate (153Sm-EDTMP) in patients with nasopharyngeal carcinoma (NPC) and multiple bone metastases. Methods: From 1993 to 1999, 160 patients (127 men, 33 women; median age 35 years) presented with NPC and multiple bone metastases. Of these, 40 patients had undergone chemotherapy, and 72 palliative radiotherapy. Patients were randomly divided into four groups: Group 1 (N = 20) received analgesics (control); Groups 2, 3 and 4 (N = 80, 40, and 20, respectively) received one, two or three courses, respectively, of 153Sm-EDTMP (77.7 MBq/kg/course; course interval, 4 wk). Results: Eight patients died of non-cancer-related causes, and 24 were lost to follow-up. The median survival time for Group 1 (7.8 months) was significantly less (p < 0.05) than that of Groups 2, 3 and 4 (11.6, 13.4 and 12.8 months, respectively). Patients given 153Sm-EDTMP who had had revious external radiation survived longer (p < 0.05) than those in the other treatment groups. Conclusions: Internal radiotherapy with 153Sm-EDTMP can extend survival time in patients with nasopharyngeal carcinoma and multiple bone metastases; when combined with external radiotherapy in appropriate patients, its effect on survival time is enhanced.. (authors)

Semiparametric accelerated failure time cure rate mixture models with competing risks.

Science.gov (United States)

Choi, Sangbum; Zhu, Liang; Huang, Xuelin

2018-01-15

Modern medical treatments have substantially improved survival rates for many chronic diseases and have generated considerable interest in developing cure fraction models for survival data with a non-ignorable cured proportion. Statistical analysis of such data may be further complicated by competing risks that involve multiple types of endpoints. Regression analysis of competing risks is typically undertaken via a proportional hazards model adapted on cause-specific hazard or subdistribution hazard. In this article, we propose an alternative approach that treats competing events as distinct outcomes in a mixture. We consider semiparametric accelerated failure time models for the cause-conditional survival function that are combined through a multinomial logistic model within the cure-mixture modeling framework. The cure-mixture approach to competing risks provides a means to determine the overall effect of a treatment and insights into how this treatment modifies the components of the mixture in the presence of a cure fraction. The regression and nonparametric parameters are estimated by a nonparametric kernel-based maximum likelihood estimation method. Variance estimation is achieved through resampling methods for the kernel-smoothed likelihood function. Simulation studies show that the procedures work well in practical settings. Application to a sarcoma study demonstrates the use of the proposed method for competing risk data with a cure fraction. Copyright © 2017 John Wiley & Sons, Ltd.

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.

Marginal Bayesian nonparametric model for time to disease arrival of threatened amphibian populations.

Science.gov (United States)

Zhou, Haiming; Hanson, Timothy; Knapp, Roland

2015-12-01

The global emergence of Batrachochytrium dendrobatidis (Bd) has caused the extinction of hundreds of amphibian species worldwide. It has become increasingly important to be able to precisely predict time to Bd arrival in a population. The data analyzed herein present a unique challenge in terms of modeling because there is a strong spatial component to Bd arrival time and the traditional proportional hazards assumption is grossly violated. To address these concerns, we develop a novel marginal Bayesian nonparametric survival model for spatially correlated right-censored data. This class of models assumes that the logarithm of survival times marginally follow a mixture of normal densities with a linear-dependent Dirichlet process prior as the random mixing measure, and their joint distribution is induced by a Gaussian copula model with a spatial correlation structure. To invert high-dimensional spatial correlation matrices, we adopt a full-scale approximation that can capture both large- and small-scale spatial dependence. An efficient Markov chain Monte Carlo algorithm with delayed rejection is proposed for posterior computation, and an R package spBayesSurv is provided to fit the model. This approach is first evaluated through simulations, then applied to threatened frog populations in Sequoia-Kings Canyon National Park. © 2015, The International Biometric Society.

Kernel based methods for accelerated failure time model with ultra-high dimensional data

Directory of Open Access Journals (Sweden)

Jiang Feng

2010-12-01

Full Text Available Abstract Background Most genomic data have ultra-high dimensions with more than 10,000 genes (probes. Regularization methods with L1 and Lp penalty have been extensively studied in survival analysis with high-dimensional genomic data. However, when the sample size n ≪ m (the number of genes, directly identifying a small subset of genes from ultra-high (m > 10, 000 dimensional data is time-consuming and not computationally efficient. In current microarray analysis, what people really do is select a couple of thousands (or hundreds of genes using univariate analysis or statistical tests, and then apply the LASSO-type penalty to further reduce the number of disease associated genes. This two-step procedure may introduce bias and inaccuracy and lead us to miss biologically important genes. Results The accelerated failure time (AFT model is a linear regression model and a useful alternative to the Cox model for survival analysis. In this paper, we propose a nonlinear kernel based AFT model and an efficient variable selection method with adaptive kernel ridge regression. Our proposed variable selection method is based on the kernel matrix and dual problem with a much smaller n × n matrix. It is very efficient when the number of unknown variables (genes is much larger than the number of samples. Moreover, the primal variables are explicitly updated and the sparsity in the solution is exploited. Conclusions Our proposed methods can simultaneously identify survival associated prognostic factors and predict survival outcomes with ultra-high dimensional genomic data. We have demonstrated the performance of our methods with both simulation and real data. The proposed method performs superbly with limited computational studies.

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

Survival times of patients with a first hip fracture with and without subsequent major long-bone fractures.

Science.gov (United States)

Angthong, Chayanin; Angthong, Wirana; Harnroongroj, Thos; Naito, Masatoshi; Harnroongroj, Thossart

2013-01-01

Survival rates are poorer after a second hip fracture than after a first hip fracture. Previous survival studies have included in-hospital mortality. Excluding in-hospital deaths from the analysis allows survival times to be evaluated in community-based patients. There is still a lack of data regarding the effects of subsequent fractures on survival times after hospital discharge following an initial hip fracture. This study compared the survival times of community-dwelling patients with hip fracture who had or did not have a subsequent major long-bone fracture. Hazard ratios and risk factors for subsequent fractures and mortality rates with and without subsequent fractures were calculated. Of 844 patients with hip fracture from 2000 through 2008, 71 had a subsequent major long-bone fracture and 773 did not. Patients who died of other causes, such as perioperative complications, during hospitalization were excluded. Such exclusion allowed us to determine the effect of subsequent fracture on the survival of community-dwelling individuals after hospital discharge or after the time of the fracture if they did not need hospitalization. Demographic data, causes of death, and mortality rates were recorded. Differences in mortality rates between the patient groups and hazard ratios were calculated. Mortality rates during the first year and from 1 to 5 years after the most recent fracture were 5.6% and 1.4%, respectively, in patients with subsequent fractures, and 4.7% and 1.4%, respectively, in patients without subsequent fractures. These rates did not differ significantly between the groups. Cox regression analysis and calculation of hazard ratios did not show significant differences between patients with subsequent fractures and those without. On univariate and multivariate analyses, age fracture. This study found that survival times did not differ significantly between patients with and without subsequent major long-bone fractures after hip fracture. Therefore, all

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

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

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.

It's Deja Vu All over Again: Using Multiple-Spell Discrete-Time Survival Analysis.

Science.gov (United States)

Willett, John B.; Singer, Judith D.

1995-01-01

The multiple-spell discrete-time survival analysis method is introduced and illustrated using longitudinal data on exit from and reentry into the teaching profession. The method is applicable to many educational problems involving the sequential occurrence of disparate events or episodes. (SLD)

Evaluation of Survival Time of Tooth Color Dental Materials in Primary Anterior Teeth

Directory of Open Access Journals (Sweden)

Behjat-Al-Molook Ajami

2013-01-01

Full Text Available Introduction: In restorative dentistry, selecting the proper material is an important factor for clinical success. The objective of this study was clinical evaluation of survival time of three tooth color materials in primary anterior teeth. Methods: In this interventional clinical trial study, 94 deciduous anterior teeth (36 teeth in boys, 58 teeth in girls belonging to 3-5 year old children in Pediatric Department of Mashhad Faculty of Dentistry, Iran were selected. Selective dental materials included compoglass, glass-ionomer Fuji II LC, and composite resin. The data were analyzed with Kaplan–Meyer and Log rank test. Results: compoglass had the highest survival time in comparison with composite and glass-ionomer. Nine months retention rate for teeth restored with compoglass, composite resin and glass-ionomer were estimated: 95%, 21%, and 12.5%, respectively. Conclusion: Compoglass can be a suitable material for anterior primary teeth restoration

Evaluation of Survival Time of Tooth Color Dental Materials in Primary Anterior Teeth

Directory of Open Access Journals (Sweden)

Taraneh Movahhed

2012-09-01

Full Text Available Introduction: In restorative dentistry, selecting the proper material is an important factor for clinical success. The objective of this study was clinical evaluation of survival time of three tooth color materials in primary anterior teeth. Methods: In this interventional clinical trial study, 94 deciduous anterior teeth (36 teeth in boys, 58 teeth in girls belonging to 3-5 year old children in Pediatric Department of Mashhad Faculty of Dentistry, Iran were selected. Selective dental materials included compoglass, glass-ionomer Fuji II LC, and composite resin. The data were analyzed with Kaplan–Meyer and Log rank test. Results: compoglass had the highest survival time in comparison with composite and glass-ionomer. Nine months retention rate for teeth restored with compoglass, composite resin and glass-ionomer were estimated: 95%, 21%, and 12.5%, respectively. Conclusion: Compoglass can be a suitable material for anterior primary teeth restoration.

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.)

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

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.

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

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

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.

Risk-adjusted survival for adults following in-hospital cardiac arrest by day of week and time of day: observational cohort study

Science.gov (United States)

Robinson, Emily J; Power, Geraldine S; Nolan, Jerry; Soar, Jasmeet; Spearpoint, Ken; Gwinnutt, Carl; Rowan, Kathryn M

2016-01-01

Background Internationally, hospital survival is lower for patients admitted at weekends and at night. Data from the UK National Cardiac Arrest Audit (NCAA) indicate that crude hospital survival was worse after in-hospital cardiac arrest (IHCA) at night versus day, and at weekends versus weekdays, despite similar frequency of events. Objective To describe IHCA demographics during three day/time periods—weekday daytime (Monday to Friday, 08:00 to 19:59), weekend daytime (Saturday and Sunday, 08:00 to 19:59) and night-time (Monday to Sunday, 20:00 to 07:59)—and to compare the associated rates of return of spontaneous circulation (ROSC) for >20 min (ROSC>20 min) and survival to hospital discharge, adjusted for risk using previously developed NCAA risk models. To consider whether any observed difference could be attributed to differences in the case mix of patients resident in hospital and/or the administered care. Methods We performed a prospectively defined analysis of NCAA data from 27 700 patients aged ≥16 years receiving chest compressions and/or defibrillation and attended by a hospital-based resuscitation team in response to a resuscitation (2222) call in 146 UK acute hospitals. Results Risk-adjusted outcomes (OR (95% CI)) were worse (p20 min 0.88 (0.81 to 0.95); hospital survival 0.72 (0.64 to 0.80)), and night-time (ROSC>20 min 0.72 (0.68 to 0.76); hospital survival 0.58 (0.54 to 0.63)) compared with weekday daytime. The effects were stronger for non-shockable than shockable rhythms, but there was no significant interaction between day/time of arrest and age, or day/time of arrest and arrest location. While many daytime IHCAs involved procedures, restricting the analyses to IHCAs in medical admissions with an arrest location of ward produced results that are broadly in line with the primary analyses. Conclusions IHCAs attended by the hospital-based resuscitation team during nights and weekends have substantially worse outcomes than during

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.

[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.

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

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.

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.

Effect of bifidobacteria implantation on the survival time of whole-body irradiated mice

International Nuclear Information System (INIS)

Yokokura, Teruo; Onoue, Masaharu; Mutai, Masahiko

1980-01-01

Letahl dose (2 KR) of gamma-ray was irradiated on the whole bodies of mice. Survival time after irradiation was significantly longer in mice with administration of both Bifidobacterium breve YIT 4008 and transgalactosyl oligosaccharide than in mice with administration of either of the two or nothing. (Tsunoda, M.)

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

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

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

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.

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.

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

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.

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

Determine the Influence of Time Held in “Knockdown” Anesthesia on Survival and Stress of Surgically Implanted Juvenile Salmonids

Energy Technology Data Exchange (ETDEWEB)

Woodley, Christa M.; Wagner, Katie A.; Knox, Kasey M.

2012-01-31

The Juvenile Salmon Acoustic Telemetry System (JSATS) was developed for the U.S. Army Corp of Engineers Portland District (USACE) to address questions related to survival and performance measures of juvenile salmonids as they pass through the Federal Columbia River Power System (FCRPS). Researchers using JSATS acoustic transmitters (ATs) were tasked with standardizing the surgical implantation procedure to ensure that the stressors of handling and surgery on salmonids were consistent and less likely to cause effects of tagging in survival studies. Researchers questioned whether the exposure time in 'knockdown' anesthesia (or induction) to prepare fish for surgery could influence the survival of study fish (CBSPSC 2011). Currently, fish are held in knockdown anesthesia after they reach Stage 4 anesthesia until the completion of the surgical implantation of a transmitter, varies from 5 to 15 minutes for studies conducted in the Columbia Basin. The Columbia Basin Surgical Protocol Steering Committee (CBSPSC ) expressed concern that its currently recommended 10-minute maximum time limit during which fish are held in anesthetic - tricaine methanesulfonate (MS-222, 80 mg L-1 water) - could increase behavioral and physiological costs, and/or decrease survival of outmigrating juvenile salmonids. In addition, the variability in the time fish are held at Stage 4 could affect the data intended for direct comparison of fish within or among survival studies. Under the current recommended protocol, if fish exceed the 10-minute time limit, they are to be released without surgical implantation, thereby increasing the number of fish handled and endangered species 'take' at the bypass systems for FCRPS survival studies.

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.

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

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

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

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.

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.

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.

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.

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....

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.

Impact of Sodium Chloride and Heat on Survival Time of Linguatula Serrata Nymphs in vitro: An Experimental Study

Directory of Open Access Journals (Sweden)

B. Hajimohammadi

2012-07-01

Full Text Available Introduction: Linguatula serrata is a zoonotic parasite, belonging to the class Pentastomida. The major aim of this study was to evaluate the impact of sodium chloride (NaCl and heat on survival time of Linguatula serrata nymphs. Materials & Methods: Thirty nymphs (10 in triplicate were separately transferred to plastic tubes, containing different concentrations of NaCl solution (2%, 5% and 10%. Meanwhile, 30 nymphs in tubes containing Phosphate Buffer Saline (PBS were separately treated by +50°C, +60°C and +72°C. As control group, thirty nymphs were stored in PBS at +4°C. The effects of different conditions on survival time of the nymphs were evaluated by observing their motility in different periods of time. Results: The survival time of the nymphs stored in 10% NaCl solution was too short and all of them were dead after 3 hours. But the other ones maintained in 2% NaCl solution were significantly more resistant (p<0.05 and were survived for 2 days. All the nymphs pertaining to each +60°C and +72°C treatments were found dead after first 5-minute storage interval; the nymphs stored at +50°C died totally after 20 minutes. The nymphs maintained in PBS at +4°C (control group showed the longest survival time (p<0.05; all of them were alive until day 4 and the last ones died on day 34. Conclusion: It is concluded that salting and heating have significant parasiticidal effects on L. serrata nymphs and could be used as disinfecting methods in processing of meat products especially liver. However, refrigeration at +4°C increases the resistance of the nymphs in meat products and therefore might endanger the food safety.

Frozen in Time? Microbial strategies for survival and carbon metabolism over geologic time in a Pleistocene permafrost chronosequence

Science.gov (United States)

Mackelprang, R.; Douglas, T. A.; Waldrop, M. P.

2014-12-01

Permafrost soils have received tremendous interest due to their importance as a global carbon store with the potential to be thawed over the coming centuries. Instead of being 'frozen in time,' permafrost contains active microbes. Most metagenomic studies have focused on Holocene aged permafrost. Here, we target Pleistocene aged ice and carbon rich permafrost (Yedoma), which can differ in carbon content and stage of decay. Our aim was to understand how microbes in the permafrost transform organic matter over geologic time and to identify physiological and biochemical adaptations that enable long-term survival. We used next-generation sequencing to characterize microbial communities along a permafrost age gradient. Samples were collected from the Cold Regions Research and Engineering Laboratory (CRREL) Permafrost Tunnel near Fox, AK, which penetrates a hillside providing access to permafrost ranging in age from 12 to 40 kyr. DNA was extracted directly from unthawed samples. 16S rRNA amplicon (16S) and shotgun metagenome sequencing revealed significant age-driven differences. First, microbial diversity declines with permafrost age, likely due to long-term exposure to environmental stresses and a reduction in metabolic resources. Second, we observed taxonomic differences among ages, with an increasing abundance of Firmicutes (endospore-formers) in older samples, suggesting that dormancy is a common survival strategy in older permafrost. Ordination of 16S and metagenome data revealed age-based clustering. Genes differing significantly between age categories included those involved in lipopolysaccharide assembly, cold-response, and carbon processing. These data point to the physiological adaptations to long-term frozen conditions and to the metabolic processes utilized in ancient permafrost. In fact, a gene common in older samples is involved in cadaverine production, which could potentially explain the putrefied smell of Pleistocene aged permafrost. Coupled with soil

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

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.

Risk-adjusted survival for adults following in-hospital cardiac arrest by day of week and time of day: observational cohort study.

Science.gov (United States)

Robinson, Emily J; Smith, Gary B; Power, Geraldine S; Harrison, David A; Nolan, Jerry; Soar, Jasmeet; Spearpoint, Ken; Gwinnutt, Carl; Rowan, Kathryn M

2016-11-01

Internationally, hospital survival is lower for patients admitted at weekends and at night. Data from the UK National Cardiac Arrest Audit (NCAA) indicate that crude hospital survival was worse after in-hospital cardiac arrest (IHCA) at night versus day, and at weekends versus weekdays, despite similar frequency of events. To describe IHCA demographics during three day/time periods-weekday daytime (Monday to Friday, 08:00 to 19:59), weekend daytime (Saturday and Sunday, 08:00 to 19:59) and night-time (Monday to Sunday, 20:00 to 07:59)-and to compare the associated rates of return of spontaneous circulation (ROSC) for >20 min (ROSC>20 min) and survival to hospital discharge, adjusted for risk using previously developed NCAA risk models. To consider whether any observed difference could be attributed to differences in the case mix of patients resident in hospital and/or the administered care. We performed a prospectively defined analysis of NCAA data from 27 700 patients aged ≥16 years receiving chest compressions and/or defibrillation and attended by a hospital-based resuscitation team in response to a resuscitation (2222) call in 146 UK acute hospitals. Risk-adjusted outcomes (OR (95% CI)) were worse (p20 min 0.88 (0.81 to 0.95); hospital survival 0.72 (0.64 to 0.80)), and night-time (ROSC>20 min 0.72 (0.68 to 0.76); hospital survival 0.58 (0.54 to 0.63)) compared with weekday daytime. The effects were stronger for non-shockable than shockable rhythms, but there was no significant interaction between day/time of arrest and age, or day/time of arrest and arrest location. While many daytime IHCAs involved procedures, restricting the analyses to IHCAs in medical admissions with an arrest location of ward produced results that are broadly in line with the primary analyses. IHCAs attended by the hospital-based resuscitation team during nights and weekends have substantially worse outcomes than during weekday daytimes. Organisational or care differences at

Promotion time cure rate model with nonparametric form of covariate effects.

Science.gov (United States)

Chen, Tianlei; Du, Pang

2018-05-10

Survival data with a cured portion are commonly seen in clinical trials. Motivated from a biological interpretation of cancer metastasis, promotion time cure model is a popular alternative to the mixture cure rate model for analyzing such data. The existing promotion cure models all assume a restrictive parametric form of covariate effects, which can be incorrectly specified especially at the exploratory stage. In this paper, we propose a nonparametric approach to modeling the covariate effects under the framework of promotion time cure model. The covariate effect function is estimated by smoothing splines via the optimization of a penalized profile likelihood. Point-wise interval estimates are also derived from the Bayesian interpretation of the penalized profile likelihood. Asymptotic convergence rates are established for the proposed estimates. Simulations show excellent performance of the proposed nonparametric method, which is then applied to a melanoma study. Copyright © 2018 John Wiley & Sons, Ltd.

Survival times with and without tube feeding in patients with dementia or psychiatric diseases in Japan.

Science.gov (United States)

Takayama, Keiko; Hirayama, Keisuke; Hirao, Akihiko; Kondo, Keiko; Hayashi, Hideki; Kadota, Koichi; Asaba, Hiroyuki; Ishizu, Hideki; Nakata, Kenji; Kurisu, Kairi; Oshima, Etsuko; Yokota, Osamu; Yamada, Norihito; Terada, Seishi

2017-11-01

It is widely supposed that there has been no evidence of increased survival in patients with advanced dementia receiving enteral tube feeding. However, more than a few studies have reported no harmful outcome from tube feeding in dementia patients compared to in patients without dementia. This was a retrospective study. Nine psychiatric hospitals in Okayama Prefecture participated in this survey. All inpatients fulfilling the entry criteria were evaluated. All subjects suffered from difficulty with oral intake. Attending physicians thought that the patients could not live without long-term artificial nutrition. The physicians decided whether to make use of long-term artificial nutrition between January 2012 and December 2014. We evaluated 185 patients. Their mean age was 76.6 ± 11.4 years. Of all subjects, patients with probable Alzheimer's disease (n = 78) formed the biggest group, schizophrenia patients (n = 44) the second, and those with vascular dementia (n = 30) the third. The median survival times were 711 days for patients with tube feeding and 61 days for patients without tube feeding. In a comparison different types of tube feeding, median survival times were 611 days for patients with a nasogastric tube and more than 1000 days for those with a percutaneous endoscopic gastrostomy tube. Patients with tube feeding survived longer than those without tube feeding, even among dementia patients. This study suggests that enteral nutrition for patients with dementia prolongs survival. Additionally, percutaneous endoscopic gastrostomy tube feeding may be safer than nasogastric tube feeding among patients in psychiatric hospitals. © 2017 Japanese Psychogeriatric Society.

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...

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

The impact of chemotherapy and its timing on survival in malignant peritoneal mesothelioma treated with complete debulking.

Science.gov (United States)

Naffouje, Samer A; Tulla, Kiara A; Salti, George I

2018-04-12

The current standard of treatment for malignant peritoneal mesothelioma (MPM) is cytoreductive surgery when the disease distribution is favorable. The role of chemotherapy, as an adjunct to surgery, remains unclear. The national database of mesothelioma was used to identify MPM patients who were treated with curative intent. Patients were divided into treatment groups: (1) chemotherapy only, (2) surgery only, (3) neoadjuvant chemotherapy + surgery, and (4) surgery + adjuvant chemotherapy. A negative control group of patients who did not receive any treatment was added (group 0). Totally, 1740 patients were included. Mean age was 63.04 ± 14.58 and 60.7% were males. The patients' distribution into the treatment groups was 604, 684, 169, 55, and 228 patients in groups 0-4, respectively, with a median survival of 3.61 ± 0.37, 11.10 ± 0.73, 57.41 ± 11.91, 52.30 ± 7.20, and 55.00 ± 9.19 months. The addition of chemotherapy, in any setting, to surgery provided an improved survival at 1 year (p = 0.006). This survival benefit ceased at the 2-, 3-, and 5-year checkpoints. The multivariate analysis identified age, sarcomatoid/biphasic histologies, nodal and distant metastasis, and offering no treatment or chemotherapy only as poor prognostic factors for overall survival. No difference in overall survival was noted with the addition of chemotherapy to complete debulking regardless of the timing. Complete debulking remains the standard treatment for MPM. The addition of systemic chemotherapy provides a short-term survival improvement at 1 year only and was similar whether given in the neoadjuvant or adjuvant setting. Nevertheless, it did not add a survival benefit beyond the 1-year time point.

End points for adjuvant therapy trials: has the time come to accept disease-free survival as a surrogate end point for overall survival?

Science.gov (United States)

Gill, Sharlene; Sargent, Daniel

2006-06-01

The intent of adjuvant therapy is to eradicate micro-metastatic residual disease following curative resection with the goal of preventing or delaying recurrence. The time-honored standard for demonstrating efficacy of new adjuvant therapies is an improvement in overall survival (OS). This typically requires phase III trials of large sample size with lengthy follow-up. With the intent of reducing the cost and time of completing such trials, there is considerable interest in developing alternative or surrogate end points. A surrogate end point may be employed as a substitute to directly assess the effects of an intervention on an already accepted clinical end point such as mortality. When used judiciously, surrogate end points can accelerate the evaluation of new therapies, resulting in the more timely dissemination of effective therapies to patients. The current review provides a perspective on the suitability and validity of disease-free survival (DFS) as an alternative end point for OS. Criteria for establishing surrogacy and the advantages and limitations associated with the use of DFS as a primary end point in adjuvant clinical trials and as the basis for approval of new adjuvant therapies are discussed.

Does prehospital time affect survival of major trauma patients where there is no prehospital care?

Directory of Open Access Journals (Sweden)

S B Dharap

2017-01-01

Full Text Available Background: Survival after major trauma is considered to be time dependent. Efficient prehospital care with rapid transport is the norm in developed countries, which is not available in many lower middle and low-income countries. The aim of this study was to assess the effect of prehospital time and primary treatment given on survival of major trauma patients in a setting without prehospital care. Materials and Methods: This prospective observational study was carried out in a university hospital in Mumbai, from January to December 2014. The hospital has a trauma service but no organized prehospital care or defined interhospital transfer protocols. All patients with life- and/or limb-threatening injuries were included in the study. Injury time and arrival time were noted and the interval was defined as “prehospital time” for the directly arriving patients and as “time to tertiary care” for those transferred. Primary outcome measure was in-hospital death (or discharge. Results: Of 1181 patients, 352 were admitted directly from the trauma scene and 829 were transferred from other hospitals. In-hospital mortality was associated with age, mechanism and mode of injury, shock, Glasgow Coma Score <9, Injury Severity Score ≥16, need for intubation, and ventilatory support on arrival; but neither with prehospital time nor with time to tertiary care. Transferred patients had a significantly higher mortality (odds ratio = 1.869, 95% confidence interval = 1.233–2.561, P = 0.005 despite fewer patients with severe injury. Two hundred and ninety-four (35% of these needed airway intervention while 108 (13% needed chest tube insertion on arrival to the trauma unit suggesting inadequate care at primary facility. Conclusion: Mortality is not associated with prehospital time but with transfers from primary care; probably due to deficient care. To improve survival after major trauma, enhancement of resources for resuscitation and capacity building of on

Modeling the effects of binary mixtures on survival in time.

NARCIS (Netherlands)

Baas, J.; van Houte, B.P.P.; van Gestel, C.A.M.; Kooijman, S.A.L.M.

2007-01-01

In general, effects of mixtures are difficult to describe, and most of the models in use are descriptive in nature and lack a strong mechanistic basis. The aim of this experiment was to develop a process-based model for the interpretation of mixture toxicity measurements, with effects of binary

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.

Patients with type 2 diabetes benefit from primary care-based disease management: a propensity score matched survival time analysis.

Science.gov (United States)

Drabik, Anna; Büscher, Guido; Thomas, Karsten; Graf, Christian; Müller, Dirk; Stock, Stephanie

2012-08-01

This study aimed to assess the impact of a nationwide German diabetes mellitus disease management program (DMP) on survival time and costs in comparison to routine care. The authors conducted a retrospective observational cohort study using routine administration data from Germany's largest sickness fund to identify insured suffering from diabetes in 2002. A total of 95,443 insured with type 2 diabetes mellitus who were born before January 1, 1962 met the defined inclusion criteria, resulting in 19,888 pairs of DMP participants and nonparticipants matched for socioeconomic and health status using propensity score matching methods. This is the first time propensity score matching has been used to evaluate a survival benefit of DMPs. In the time frame analyzed (3 years), mean survival time for the DMP group was 1045 days vs. 985 days for the routine care group (Ptime. They also incurred lower costs compared to propensity score matched insured in routine care.

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.

The survival rate of self-immolators in Kermanshah Province 2010- 2011

Directory of Open Access Journals (Sweden)

Farid Najafi

2013-12-01

Full Text Available Background: Self-immolation is one of the most violent methods of suicide, which is spreading in Iran. The highest rate of deaths due to committing suicide and self-immolation in Iran is observed in Kermanshah province. This research was conducted to study the survival rate and the factors that influence survival among the ones who commit self-immolation in Kermanshah province. Methods: In this study, all the cases who did not survive, as well as all the ones who were hospitalized due to self-immolation in Kermanshah province during 2010 and 2011 were examined. The Kaplan-Meier method was used to estimate the survival function, and in order to do the comparisons, Logrank test and Cox Regression were employed using Stata 12 software. Results: The results indicated that during 2010 and 2011, 343 individuals committed self-immolation in Kermanshah Province, while, 288 (84% were women. Also, it was found that 184 (53% did not survive, the mean and median of survival time in those who committed suicide deliberately, were 33±2.6 and 11±2 days respectively. Estimation of survival rate using Logrank test indicated that survival rate had a significant relationship with age, mental disorders, drug addiction, and TBSA (Total Body Surface Area, while it did not suggest a statistically significant relationship with gender, marital status and cause of injury. After multivariate analysis using Cox regression, only two variables of age and TBSA could remain in the model and the other variables were excluded from the model. Conclusion: The death toll due to self-immolation is very high and the mean and median of survival time among the people who committed self-immolation is very low. Therefore, it is recommended that remedial action be performed quickly without wasting time.

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

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

First recovery in anorexia nervosa patients in the long-term course: a discrete-time survival analysis.

Science.gov (United States)

Herzog, W; Schellberg, D; Deter, H C

1997-02-01

The results of a 12-year follow-up study of occurrence and timing of first recovery in 69 hospitalized patients with severe anorexia nervosa (AN) are presented. For the first time discrete-time survival analysis methods were used to determine the likelihood of recovery in AN patients. Furthermore, predictors gleaned from pretreatment-posttreatment studies of long-term outcome in AN could be evaluated as to their effect on a change in the time course structure of the likelihood of first recovery. Results show that AN condition did not improve until after 6 years after the first inpatient treatment in 50% of patients. However, a restricter-type AN and low serum creatinine levels were predictors for earlier recovery. One specific effect was that AN patients who show purging behavior in combination with additional social disturbances have a lower chance of recovering. The use of discrete-time survival analysis methodology in further prospective studies will contribute to the development of more tailored treatment of AN, which also takes the individual phase of illness and specific aspects of the symptomatology into account.

The number and microlocalization of tumor-associated immune cells are associated with patient's survival time in non-small cell lung cancer

International Nuclear Information System (INIS)

Dai, Fuqiang; Liu, Lunxu; Che, Guowei; Yu, Nanbin; Pu, Qiang; Zhang, Shangfu; Ma, Junliang; Ma, Lin; You, Zongbing

2010-01-01

Tumor microenvironment is composed of tumor cells, fibroblasts, endothelial cells, and infiltrating immune cells. Tumor-associated immune cells may inhibit or promote tumor growth and progression. This study was conducted to determine whether the number and microlocalization of macrophages, mature dendritic cells and cytotoxic T cells in non-small cell lung cancer are associated with patient's survival time. Ninety-nine patients with non-small cell lung cancer (NSCLC) were included in this retrospective study. Paraffin-embedded NSCLC specimens and their clinicopathological data including up to 8-year follow-up information were used. Immunohistochemical staining for CD68 (marker for macrophages), CD83 (marker for mature dendritic cells), and CD8 (marker for cytotoxic T cells) was performed and evaluated in a blinded fashion. The numbers of immune cells in tumor islets and stroma, tumor islets, or tumor stroma were counted under a microscope. Correlation of the cell numbers and patient's survival time was analyzed using the Statistical Package for the Social Sciences (version 13.0). The numbers of macrophages, mature dendritic cells and cytotoxic T cells were significantly more in the tumor stroma than in the tumor islets. The number of macrophages in the tumor islets was positively associated with patient's survival time, whereas the number of macrophages in the tumor stroma was negatively associated with patient's survival time in both univariate and multivariate analyses. The number of mature dendritic cells in the tumor islets and stroma, tumor islets only, or tumor stroma only was positively associated with patient's survival time in a univariate analysis but not in a multivariate analysis. The number of cytotoxic T cells in the tumor islets and stroma was positively associated with patient's survival time in a univariate analysis but not in a multivariate analysis. The number of cytotoxic T cells in the tumor islets only or stroma

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

Splenectomy increases the survival time of heart allograft via developing immune tolerance

Science.gov (United States)

2013-01-01

Background The spleen is an active lymphoid organ. The effect of splenectomy on the immune response remains unclear. This study investigated whether splenectomy can induce immune tolerance and has a beneficial role in cardiac allograft. Methods Wistar rats were used for heart donors. The Sprague–Dawley (SD) rats designated as the recipients of heart transplantation (HT) were randomly assigned into four groups: sham, splenectomy, HT, splenectomy + HT. The survival of transplanted hearts was assessed by daily checking of abdominal palpation. At various time points after transplantation, the transplanted hearts were collected and histologically examined; the level of CD4+CD25+ T regulatory lymphocytes (Tregs) and rate of lymphocyte apoptosis (annexin-v+ PI+ cells) in the blood were analyzed by using flow cytometric method. Results 1) Splenectomy significantly prolonged the mean survival time of heart allografts (7 ± 1.1 days and 27 ± 1.5 days for HT and splenectomy + HT, respectively; n = 12-14/group, HT vs. splenectomy + HT, p Splenectomy delayed pathological changes (inflammatory cell infiltration, myocardial damage) of the transplanted hearts in splenectomy + HT rats; 3) The level of CD4+CD25+ Tregs in the blood of splenectomized rats was significantly increased within 7 days (2.4 ± 0.5%, 4.9 ± 1.3% and 5.3 ± 1.0% for sham, splenectomy and splenectomy + HT, respectively; n = 15/group, sham vs. splenectomy or splenectomy + HT, p splenectomy surgery and gradually decreased to baseline level; 4) Splenectomy increased the rate of lymphocyte apoptosis (day 7: 0.3 ± 0.05%, 3.9 ± 0.9% and 4.1 ± 0.9% for sham, splenectomy and splenectomy + HT, respectively; n = 15/group, sham vs. splenectomy or splenectomy + HT, p Splenectomy inhibits the development of pathology and prolongs the survival time of cardiac allograft. The responsible mechanism is associated with induction of immune

Results of surgical excision and evaluation of factors associated with survival time in dogs with lingual neoplasia: 97 cases (1995-2008).

Science.gov (United States)

Culp, William T N; Ehrhart, Nicole; Withrow, Stephen J; Rebhun, Robert B; Boston, Sarah; Buracco, Paolo; Reiter, Alexander M; Schallberger, Sandra P; Aldridge, Charles F; Kent, Michael S; Mayhew, Philipp D; Brown, Dorothy C

2013-05-15

To describe the clinical characteristics, treatments, outcomes, and factors associated with survival time in a cohort of dogs with lingual neoplasia that underwent surgical excision. Retrospective case series. Animals-97 client-owned dogs. Medical records of dogs with a lingual tumor examined between 1995 and 2008 were reviewed. Records were included if a lingual tumor was confirmed by histologic examination and surgical excision of the mass was attempted. Data were recorded and analyzed to identify prognostic factors. Clinical signs were mostly related to the oral cavity. For 93 dogs, marginal excision, subtotal glossectomy, and near-total glossectomy were performed in 35 (38%), 55 (59%), and 3 (3%), respectively. Surgery-related complications were rare, but 27 (28%) dogs had tumor recurrence. The most common histopathologic diagnoses for the 97 dogs were squamous cell carcinoma (31 [32%]) and malignant melanoma (29 [30%]). Eighteen (19%) dogs developed metastatic disease, and the overall median survival time was 483 days. Median survival time was 216 days for dogs with squamous cell carcinoma and 241 days for dogs with malignant melanoma. Dogs with lingual tumors ≥ 2 cm in diameter at diagnosis had a significantly shorter survival time than did dogs with tumors melanoma predominate. A thorough physical examination to identify lingual tumors at an early stage and surgical treatment after tumor identification are recommended because tumor size significantly affected survival time.

Time-dependent perturbation theory for nonequilibrium lattice models

International Nuclear Information System (INIS)

Jensen, I.; Dickman, R.

1993-01-01

The authors develop a time-dependent perturbation theory for nonequilibrium interacting particle systems. They focus on models such as the contact process which evolve via destruction and autocatalytic creation of particles. At a critical value of the destruction rate there is a continuous phase transition between an active steady state and the vacuum state, which is absorbing. They present several methods for deriving series for the evolution starting from a single seed particle, including expansions for the ultimate survival probability in the super- and subcritical regions, expansions for the average number of particles in the subcritical region, and short-time expansions. Algorithms for computer generation of the various expansions are presented. Rather long series (24 terms or more) and precise estimates of critical parameters are presented. 45 refs., 4 figs., 9 tabs

Semiparametric Bayesian analysis of accelerated failure time models with cluster structures.

Science.gov (United States)

Li, Zhaonan; Xu, Xinyi; Shen, Junshan

2017-11-10

In this paper, we develop a Bayesian semiparametric accelerated failure time model for survival data with cluster structures. Our model allows distributional heterogeneity across clusters and accommodates their relationships through a density ratio approach. Moreover, a nonparametric mixture of Dirichlet processes prior is placed on the baseline distribution to yield full distributional flexibility. We illustrate through simulations that our model can greatly improve estimation accuracy by effectively pooling information from multiple clusters, while taking into account the heterogeneity in their random error distributions. We also demonstrate the implementation of our method using analysis of Mayo Clinic Trial in Primary Biliary Cirrhosis. Copyright © 2017 John Wiley & Sons, Ltd.

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.

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...

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)

A clinical tool for predicting survival in ALS.

Science.gov (United States)

Knibb, Jonathan A; Keren, Noa; Kulka, Anna; Leigh, P Nigel; Martin, Sarah; Shaw, Christopher E; Tsuda, Miho; Al-Chalabi, Ammar

2016-12-01

Amyotrophic lateral sclerosis (ALS) is a progressive and usually fatal neurodegenerative disease. Survival from diagnosis varies considerably. Several prognostic factors are known, including site of onset (bulbar or limb), age at symptom onset, delay from onset to diagnosis and the use of riluzole and non-invasive ventilation (NIV). Clinicians and patients would benefit from a practical way of using these factors to provide an individualised prognosis. 575 consecutive patients with incident ALS from a population-based registry in South-East England register for ALS (SEALS) were studied. Their survival was modelled as a two-step process: the time from diagnosis to respiratory muscle involvement, followed by the time from respiratory involvement to death. The effects of predictor variables were assessed separately for each time interval. Younger age at symptom onset, longer delay from onset to diagnosis and riluzole use were associated with slower progression to respiratory involvement, and NIV use was associated with lower mortality after respiratory involvement, each with a clinically significant effect size. Riluzole may have a greater effect in younger patients and those with longer delay to diagnosis. A patient's survival time has a roughly 50% chance of falling between half and twice the predicted median. A simple and clinically applicable graphical method of predicting an individual patient's survival from diagnosis is presented. The model should be validated in an independent cohort, and extended to include other important prognostic factors. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Chronic consequences of acute injuries: worse survival after discharge.

Science.gov (United States)

Shafi, Shahid; Renfro, Lindsay A; Barnes, Sunni; Rayan, Nadine; Gentilello, Larry M; Fleming, Neil; Ballard, David

2012-09-01

The Trauma Quality Improvement Program uses inhospital mortality to measure quality of care, which assumes patients who survive injury are not likely to suffer higher mortality after discharge. We hypothesized that survival rates in trauma patients who survive to discharge remain stable afterward. Patients treated at an urban Level I trauma center (2006-2008) were linked with the Social Security Administration Death Master File. Survival rates were measured at 30, 90, and 180 days and 1 and 2 years from injury among two groups of trauma patients who survived to discharge: major trauma (Abbreviated Injury Scale score ≥ 3 injuries, n = 2,238) and minor trauma (Abbreviated Injury Scale score ≤ 2 injuries, n = 1,171). Control groups matched to each trauma group by age and sex were simulated from the US general population using annual survival probabilities from census data. Kaplan-Meier and log-rank analyses conditional upon survival to each time point were used to determine changes in risk of mortality after discharge. Cox proportional hazards models with left truncation at the time of discharge were used to determine independent predictors of mortality after discharge. The survival rate in trauma patients with major injuries was 92% at 30 days posttrauma and declined to 84% by 3 years (p > 0.05 compared with general population). Minor trauma patients experienced a survival rate similar to the general population. Age and injury severity were the only independent predictors of long-term mortality given survival to discharge. Log-rank tests conditional on survival to each time point showed that mortality risk in patients with major injuries remained significantly higher than the general population for up to 6 months after injury. The survival rate of trauma patients with major injuries remains significantly lower than survival for minor trauma patients and the general population for several months postdischarge. Surveillance for early identification and treatment of

Predicting treatment effect from surrogate endpoints and historical trials: an extrapolation involving probabilities of a binary outcome or survival to a specific time.

Science.gov (United States)

Baker, Stuart G; Sargent, Daniel J; Buyse, Marc; Burzykowski, Tomasz

2012-03-01

Using multiple historical trials with surrogate and true endpoints, we consider various models to predict the effect of treatment on a true endpoint in a target trial in which only a surrogate endpoint is observed. This predicted result is computed using (1) a prediction model (mixture, linear, or principal stratification) estimated from historical trials and the surrogate endpoint of the target trial and (2) a random extrapolation error estimated from successively leaving out each trial among the historical trials. The method applies to either binary outcomes or survival to a particular time that is computed from censored survival data. We compute a 95% confidence interval for the predicted result and validate its coverage using simulation. To summarize the additional uncertainty from using a predicted instead of true result for the estimated treatment effect, we compute its multiplier of standard error. Software is available for download. © 2011, The International Biometric Society No claim to original US government works.

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.

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

Consequences of cold-ischemia time on primary nonfunction and patient and graft survival in liver transplantation: a meta-analysis.

Directory of Open Access Journals (Sweden)

James E Stahl

2008-06-01

Full Text Available The ability to preserve organs prior to transplant is essential to the organ allocation process.The purpose of this study is to describe the functional relationship between cold-ischemia time (CIT and primary nonfunction (PNF, patient and graft survival in liver transplant.To identify relevant articles Medline, EMBASE and the Cochrane database, including the non-English literature identified in these databases, was searched from 1966 to April 2008. Two independent reviewers screened and extracted the data. CIT was analyzed both as a continuous variable and stratified by clinically relevant intervals. Nondichotomous variables were weighted by sample size. Percent variables were weighted by the inverse of the binomial variance.Twenty-six studies met criteria. Functionally, PNF% = -6.678281+0.9134701*CIT Mean+0.1250879*(CIT Mean-9.895352-0.0067663*(CIT Mean-9.895353, r2 = .625, , p<.0001. Mean patient survival: 93% (1 month, 88% (3 months, 83% (6 months and 83% (12 months. Mean graft survival: 85.9% (1 month, 80.5% (3 months, 78.1% (6 months and 76.8% (12 months. Maximum patient and graft survival occurred with CITs between 7.5-12.5 hrs at each survival interval. PNF was also significantly correlated with ICU time, % first time grafts and % immunologic mismatches.The results of this work imply that CIT may be the most important pre-transplant information needed in the decision to accept an organ.

On the relationship between tumour growth rate and survival in non-small cell lung cancer

Directory of Open Access Journals (Sweden)

Hitesh B. Mistry

2017-11-01

Full Text Available A recurrent question within oncology drug development is predicting phase III outcome for a new treatment using early clinical data. One approach to tackle this problem has been to derive metrics from mathematical models that describe tumour size dynamics termed re-growth rate and time to tumour re-growth. They have shown to be strong predictors of overall survival in numerous studies but there is debate about how these metrics are derived and if they are more predictive than empirical end-points. This work explores the issues raised in using model-derived metric as predictors for survival analyses. Re-growth rate and time to tumour re-growth were calculated for three large clinical studies by forward and reverse alignment. The latter involves re-aligning patients to their time of progression. Hence, it accounts for the time taken to estimate re-growth rate and time to tumour re-growth but also assesses if these predictors correlate to survival from the time of progression. I found that neither re-growth rate nor time to tumour re-growth correlated to survival using reverse alignment. This suggests that the dynamics of tumours up until disease progression has no relationship to survival post progression. For prediction of a phase III trial I found the metrics performed no better than empirical end-points. These results highlight that care must be taken when relating dynamics of tumour imaging to survival and that bench-marking new approaches to existing ones is essential.

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 (w_i = 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

A neural computational model for animal's time-to-collision estimation.

Science.gov (United States)

Wang, Ling; Yao, Dezhong

2013-04-17

The time-to-collision (TTC) is the time elapsed before a looming object hits the subject. An accurate estimation of TTC plays a critical role in the survival of animals in nature and acts as an important factor in artificial intelligence systems that depend on judging and avoiding potential dangers. The theoretic formula for TTC is 1/τ≈θ'/sin θ, where θ and θ' are the visual angle and its variation, respectively, and the widely used approximation computational model is θ'/θ. However, both of these measures are too complex to be implemented by a biological neuronal model. We propose a new simple computational model: 1/τ≈Mθ-P/(θ+Q)+N, where M, P, Q, and N are constants that depend on a predefined visual angle. This model, weighted summation of visual angle model (WSVAM), can achieve perfect implementation through a widely accepted biological neuronal model. WSVAM has additional merits, including a natural minimum consumption and simplicity. Thus, it yields a precise and neuronal-implemented estimation for TTC, which provides a simple and convenient implementation for artificial vision, and represents a potential visual brain mechanism.

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.

Timing of adjuvant chemotherapy and its relation to survival among patients with stage III colon cancer.

Science.gov (United States)

Bos, A C R K; van Erning, F N; van Gestel, Y R B M; Creemers, G J M; Punt, C J A; van Oijen, M G H; Lemmens, V E P P

2015-11-01

Currently available data suggest that delaying the start of adjuvant chemotherapy in colon cancer patients has a detrimental effect on survival. We analysed which factors impact on the timing of adjuvant chemotherapy and evaluated the influence on overall survival (OS). Stage III colon cancer patients who underwent resection and received adjuvant chemotherapy between 2008 and 2013 were selected from the Netherlands Cancer Registry. Timing of adjuvant chemotherapy was subdivided into: ⩽ 4, 5-6, 7-8, 9-10, 11-12 and 13-16 weeks post-surgery. Multivariable regressions were performed to assess the influence of several factors on the probability of starting treatment within 8 weeks post-surgery and to evaluate the association of timing of adjuvant chemotherapy with 5-year OS. 6620 patients received adjuvant chemotherapy, 14% commenced after 8 weeks. Factors associated with starting treatment after 8 weeks were older age (Odds ratio (OR) 65-74 versus colon cancer patients within 8 weeks post-surgery. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

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

A generalised formulation of the 'incomplete-repair' model for cell survival and tissue response to fractionated low dose-rate irradiation

International Nuclear Information System (INIS)

Nilsson, P.; Joiner, M.C.

1990-01-01

A generalized equation for cell survival or tissue effects after fractionated low dose-rate irradiations, when there is incomplete repair between fractions and significant repair during fractions, is derived in terms of the h- and g-functions of the 'incomplete-repair' (IR) model. The model is critically dependent on α/β, repair half-time, treatment time and interfraction interval, and should therefore be regarded primarily as a tool for the analysis of fractionation and dose-rate effects in carefully designed radiobiological experiments, although it should also be useful in exploring, in a general way, the feasibility of clinical treatment protocols using fractionated low dose-rate treatments. (author)

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.

Effect of intestinal microflora on the survival time of mice exposed to lethal whole-body. gamma. irradiation

Energy Technology Data Exchange (ETDEWEB)

Onoue, M.; Uchida, K.; Yokokura, T.; Takahashi, T.; Mutai, M.

1981-11-01

The effect of intestinal microflora on the survival time of mice exposed to 2-kR whole-body ..gamma.. irradiation was studied using germfree, monoassociated, and conventionalized ICR mice. The germfree mice were monoassociated with 1 of 11 bacterial strains, which were isolated from the fresh feces of conventional mice, 2 weeks prior to irradiation. All mice died within 3 weeks after irradiation. Monoassociation with Fusobacterium sp., Streptococcus faecalis, Escherichia coli, or Pseudomonas sp. significantly reduced the mean survival time compared to that of germfree mice. In contrast, monoassociation with Clostridium sp., Bifidobacterium pseudolongum, or Lactobacillus acidophilus significantly prolonged the mean survival time compared to that of germfree mice. This suggests that the latter organisms may perform some activity to protect the mice from radiation injury. In this histopathological autopsy examination, the main lesions were hypocellularity in hematopoietic organs and hemorrhage in various organs. Neither karyorrhexis nor desquamation of intestinal mucosal cells was observed in any mice. From these observations, it is suggested that the death of these mice was related to hematopoietic damage. Bacterial invasion into various organs was observed in conventionalized and Pseudomonas-, E. coli-, or S. faecalis-monoassociated mice but not in Clostridium-, B. pseudolongum-, L. acidophilus-, or Fusobacterium-monoassociated mice.

Effect of intestinal microflora on the survival time of mice exposed to lethal whole-body γ irradiation

International Nuclear Information System (INIS)

Onoue, M.; Uchida, K.; Yokokura, T.; Takahashi, T.; Mutai, M.

1981-01-01

The effect of intestinal microflora on the survival time of mice exposed to 2-kR whole-body γ irradiation was studied using germfree, monoassociated, and conventionalized ICR mice. The germfree mice were monoassociated with 1 of 11 bacterial strains, which were isolated from the fresh feces of conventional mice, 2 weeks prior to irradiation. All mice died within 3 weeks after irradiation. Monoassociation with Fusobacterium sp., Streptococcus faecalis, Escherichia coli, or Pseudomonas sp. significantly reduced the mean survival time compared to that of germfree mice. In contrast, monoassociation with Clostridium sp., Bifidobacterium pseudolongum, or Lactobacillus acidophilus significantly prolonged the mean survival time compared to that of germfree mice. This suggests that the latter organisms may perform some activity to protect the mice from radiation injury. In this histopathological autopsy examination, the main lesions were hypocellularity in hematopoietic organs and hemorrhage in various organs. Neither karyorrhexis nor desquamation of intestinal mucosal cells was observed in any mice. From these observations, it is suggested that the death of these mice was related to hematopoietic damage. Bacterial invasion into various organs was observed in conventionalized and Pseudomonas-, E. coli-, or S. faecalis-monoassociated mice but not in Clostridium-, B. pseudolongum-, L. acidophilus-, or Fusobacterium-monoassociated mice

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.

rpsftm: An R package for rank preserving structural failure time models

OpenAIRE

Allison, A.; White, I. R.; Bond, S.

2017-01-01

Treatment switching in a randomised controlled trial occurs when participants change from their randomised treatment to the other trial treatment during the study. Failure to account for treatment switching in the analysis (i.e. by performing a standard intention-to-treat analysis) can lead to biased estimates of treatment efficacy. The rank preserving structural failure time model (RPSFTM) is a method used to adjust for treatment switching in trials with survival outcomes. The RPSFTM is due ...

Edema-induced increase in tumour cell survival for 125I and 103Pd prostate permanent seed implants - a bio-mathematical model

International Nuclear Information System (INIS)

Yue Ning; Chen Zhe; Nath, Ravinder

2002-01-01

Edema caused by the surgical procedure of prostate seed implantation expands the source-to-point distances within the prostate and hence decreases the dose coverage. The decrease of dose coverage results in an increase in tumour cell survival. To investigate the effects of edema on tumour cell survival, a bio-mathematical model of edema and the corresponding cell killing by continuous low dose rate irradiation (CLDRI) was developed so that tumour cell surviving fractions can be estimated in an edematous prostate for both 125 I and 103 Pd seed implants. The dynamic nature of edema and its resolution were modelled with an exponential function V(T)=V p (1+M exp(-0.693T/T e )) where V p is the prostate volume before implantation, M is the edema magnitude and T e is edema half-life (EHL). The dose rate of a radioactive seed was calculated according to AAPM TG43, i.e. D radical S k Δg(r) φ-bar an /r 2 , where r is the distance between a seed and a given point. The distance r is now a function of time because of edema. The g(r) was approximated as 1/r 0.4 and 1/r 0.8 for 125 I and 103 Pd, respectively. By expanding the mathematical expression of the resultant dose rate in a Taylor series of exponential functions of time, the dose rate was made equivalent to that produced from multiple fictitious radionuclides of different decay constants and strengths. The biologically effective dose (BED) for an edematous prostate implant was then calculated using a generalized Dale equation. The cell surviving fraction was computed as exp(-αBED), where α is the linear coefficient of the survival curve. The tumour cell survival was calculated for both 125 I and 103 Pd seed implants and for different tumour potential doubling time (TPDT) (from 5 days to 30 days) and for edemas of different magnitudes (from 0% to 95%) and edema half-lives (from 4 days to 30 days). Tumour cell survival increased with the increase of edema magnitude and EHL. For a typical edema of a half-life of 10 days

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.

Prognostic and survival analysis of presbyopia: The healthy twin study

Science.gov (United States)

Lira, Adiyani; Sung, Joohon

2015-12-01

Presbyopia, a vision condition in which the eye loses its flexibility to focus on near objects, is part of ageing process which mostly perceptible in the early or mid 40s. It is well known that age is its major risk factor, while sex, alcohol, poor nutrition, ocular and systemic diseases are known as common risk factors. However, many other variables might influence the prognosis. Therefore in this paper we developed a prognostic model to estimate survival from presbyopia. 1645 participants which part of the Healthy Twin Study, a prospective cohort study that has recruited Korean adult twins and their family members based on a nation-wide registry at public health agencies since 2005, were collected and analyzed by univariate analysis as well as Cox proportional hazard model to reveal the prognostic factors for presbyopia while survival curves were calculated by Kaplan-Meier method. Besides age, sex, diabetes, and myopia; the proposed model shows that education level (especially engineering program) also contribute to the occurrence of presbyopia as well. Generally, at 47 years old, the chance of getting presbyopia becomes higher with the survival probability is less than 50%. Furthermore, our study shows that by stratifying the survival curve, MZ has shorter survival with average onset time about 45.8 compare to DZ and siblings with 47.5 years old. By providing factors that have more effects and mainly associate with presbyopia, we expect that we could help to design an intervention to control or delay its onset time.

Long time to diagnosis of medulloblastoma in children is not associated with decreased survival or with worse neurological outcome.

Directory of Open Access Journals (Sweden)

Jean-Francois Brasme

Full Text Available BACKGROUND: The long time to diagnosis of medulloblastoma, one of the most frequent brain tumors in children, is the source of painful remorse and sometimes lawsuits. We analyzed its consequences for tumor stage, survival, and sequelae. PATIENTS AND METHODS: This retrospective population-based cohort study included all cases of pediatric medulloblastoma from a region of France between 1990 and 2005. We collected the demographic, clinical, and tumor data and analyzed the relations between the interval from symptom onset until diagnosis, initial disease stage, survival, and neuropsychological and neurological outcome. RESULTS: The median interval from symptom onset until diagnosis for the 166 cases was 65 days (interquartile range 31-121, range 3-457. A long interval (defined as longer than the median was associated with a lower frequency of metastasis in the univariate and multivariate analyses and with a larger tumor volume, desmoplastic histology, and longer survival in the univariate analysis, but not after adjustment for confounding factors. The time to diagnosis was significantly associated with IQ score among survivors. No significant relation was found between the time to diagnosis and neurological disability. In the 62 patients with metastases, a long prediagnosis interval was associated with a higher T stage, infiltration of the fourth ventricle floor, and incomplete surgical resection; it nonetheless did not influence survival significantly in this subgroup. CONCLUSIONS: We found complex and often inverse relations between time to diagnosis of medulloblastoma in children and initial severity factors, survival, and neuropsychological and neurological outcome. This interval appears due more to the nature of the tumor and its progression than to parental or medical factors. These conclusions should be taken into account in the information provided to parents and in expert assessments produced for malpractice claims.

Using Concurrent Cardiovascular Information to Augment Survival Time Data from Orthostatic Tilt Tests

Science.gov (United States)

Feiveson, Alan H.; Fiedler, James; Lee, Stuart M. M.; Westby, Christian M.; Stenger, Michael B.; Platts, Steven H.

2014-01-01

Orthostatic Intolerance (OI) is the propensity to develop symptoms of fainting during upright standing. OI is associated with changes in heart rate, blood pressure and other measures of cardiac function. Problem: NASA astronauts have shown increased susceptibility to OI on return from space missions. Current methods for counteracting OI in astronauts include fluid loading and the use of compression garments. Multivariate trajectory spread is greater as OI increases. Pairwise comparisons at the same time within subjects allows incorporation of pass/fail outcomes. Path length, convex hull area, and covariance matrix determinant do well as statistics to summarize this spread Missing data problems Time series analysis need many more time points per OTT session treatment of trend? how incorporate survival information?

Survival analysis with functional covariates for partial follow-up studies.

Science.gov (United States)

Fang, Hong-Bin; Wu, Tong Tong; Rapoport, Aaron P; Tan, Ming

2016-12-01

Predictive or prognostic analysis plays an increasingly important role in the era of personalized medicine to identify subsets of patients whom the treatment may benefit the most. Although various time-dependent covariate models are available, such models require that covariates be followed in the whole follow-up period. This article studies a new class of functional survival models where the covariates are only monitored in a time interval that is shorter than the whole follow-up period. This paper is motivated by the analysis of a longitudinal study on advanced myeloma patients who received stem cell transplants and T cell infusions after the transplants. The absolute lymphocyte cell counts were collected serially during hospitalization. Those patients are still followed up if they are alive after hospitalization, while their absolute lymphocyte cell counts cannot be measured after that. Another complication is that absolute lymphocyte cell counts are sparsely and irregularly measured. The conventional method using Cox model with time-varying covariates is not applicable because of the different lengths of observation periods. Analysis based on each single observation obviously underutilizes available information and, more seriously, may yield misleading results. This so-called partial follow-up study design represents increasingly common predictive modeling problem where we have serial multiple biomarkers up to a certain time point, which is shorter than the total length of follow-up. We therefore propose a solution to the partial follow-up design. The new method combines functional principal components analysis and survival analysis with selection of those functional covariates. It also has the advantage of handling sparse and irregularly measured longitudinal observations of covariates and measurement errors. Our analysis based on functional principal components reveals that it is the patterns of the trajectories of absolute lymphocyte cell counts, instead of

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.

Edaravone improves survival and neurological outcomes after CPR in a ventricular fibrillation model of rats.

Science.gov (United States)

Qin, Tao; Lei, Ling-Yan; Li, Nuo; Shi, Fangying Ruan; Chen, Meng-Hua; Xie, Lu

2016-10-01

Overproduction of free radicals is a main factor contributing to cerebral injury after cardiac arrest (CA)/cardiopulmonary resuscitation (CPR). We sought to evaluate the impact of edaravone on the survival and neurological outcomes after CA/CPR in rats. Rats were subjected to CA following CPR. For survival study, the rats with restoration of spontaneous circulation (ROSC) were randomly allocated to one of the two groups (edaravone and saline group, n=20/each group) to received Edaravone (3 mg/kg) or normal saline. Another 10 rats without experiencing CA and CPR served as the sham group. Survival was observed for 72 hours and the neurological deficit score (NDS) was calculated at 12, 24, 48, and 72 hours after ROSC. For the neurological biochemical analysis study, rats were subjected to the same experimental procedures. Then, edaravone group (n=24), saline group (n=24) and sham group (n=16) were further divided into 4 subgroups according to the different time intervals (12, 24, 48, and 72 hours following ROSC). Brain tissues were harvested at relative time intervals for evaluation of oxidative stress, TUNEL staining and apoptotic gene expression. Edaravone improved postresuscitative survival time and neurological deficit, decreased brain malonylaldehyde level, increased superoxide dismutase activities, decreased proapoptotic gene expression of capase-8, capase-3, and Bax, and increased antiapoptotic Bcl-2 expression at 12, 24, 48, and 72 hours after ROSC. Edaravone improves survival and neurological outcomes following CPR via antioxidative and antiapoptotic effects in rats. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

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

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

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

GOODNESS-OF-FIT TEST FOR THE ACCELERATED FAILURE TIME MODEL BASED ON MARTINGALE RESIDUALS

Czech Academy of Sciences Publication Activity Database

Novák, Petr

2013-01-01

Roč. 49, č. 1 (2013), s. 40-59 ISSN 0023-5954 R&D Projects: GA MŠk(CZ) 1M06047 Grant - others:GA MŠk(CZ) SVV 261315/2011 Keywords : accelerated failure time model * survival analysis * goodness-of-fit Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.563, year: 2013 http://library.utia.cas.cz/separaty/2013/SI/novak-goodness-of-fit test for the aft model based on martingale residuals.pdf

Analysis of real-time reservoir monitoring : reservoirs, strategies, & modeling.

Energy Technology Data Exchange (ETDEWEB)

Mani, Seethambal S.; van Bloemen Waanders, Bart Gustaaf; Cooper, Scott Patrick; Jakaboski, Blake Elaine; Normann, Randy Allen; Jennings, Jim (University of Texas at Austin, Austin, TX); Gilbert, Bob (University of Texas at Austin, Austin, TX); Lake, Larry W. (University of Texas at Austin, Austin, TX); Weiss, Chester Joseph; Lorenz, John Clay; Elbring, Gregory Jay; Wheeler, Mary Fanett (University of Texas at Austin, Austin, TX); Thomas, Sunil G. (University of Texas at Austin, Austin, TX); Rightley, Michael J.; Rodriguez, Adolfo (University of Texas at Austin, Austin, TX); Klie, Hector (University of Texas at Austin, Austin, TX); Banchs, Rafael (University of Texas at Austin, Austin, TX); Nunez, Emilio J. (University of Texas at Austin, Austin, TX); Jablonowski, Chris (University of Texas at Austin, Austin, TX)

2006-11-01

The project objective was to detail better ways to assess and exploit intelligent oil and gas field information through improved modeling, sensor technology, and process control to increase ultimate recovery of domestic hydrocarbons. To meet this objective we investigated the use of permanent downhole sensors systems (Smart Wells) whose data is fed real-time into computational reservoir models that are integrated with optimized production control systems. The project utilized a three-pronged approach (1) a value of information analysis to address the economic advantages, (2) reservoir simulation modeling and control optimization to prove the capability, and (3) evaluation of new generation sensor packaging to survive the borehole environment for long periods of time. The Value of Information (VOI) decision tree method was developed and used to assess the economic advantage of using the proposed technology; the VOI demonstrated the increased subsurface resolution through additional sensor data. Our findings show that the VOI studies are a practical means of ascertaining the value associated with a technology, in this case application of sensors to production. The procedure acknowledges the uncertainty in predictions but nevertheless assigns monetary value to the predictions. The best aspect of the procedure is that it builds consensus within interdisciplinary teams The reservoir simulation and modeling aspect of the project was developed to show the capability of exploiting sensor information both for reservoir characterization and to optimize control of the production system. Our findings indicate history matching is improved as more information is added to the objective function, clearly indicating that sensor information can help in reducing the uncertainty associated with reservoir characterization. Additional findings and approaches used are described in detail within the report. The next generation sensors aspect of the project evaluated sensors and packaging

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

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

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.

Conditional survival is greater than overall survival at diagnosis in patients with osteosarcoma and Ewing's sarcoma.

Science.gov (United States)

Miller, Benjamin J; Lynch, Charles F; Buckwalter, Joseph A

2013-11-01

Conditional survival is a measure of the risk of mortality given that a patient has survived a defined period of time. These estimates are clinically helpful, but have not been reported previously for osteosarcoma or Ewing's sarcoma. We determined the conditional survival of patients with osteosarcoma and Ewing's sarcoma given survival of 1 or more years. We used the Surveillance, Epidemiology, and End Results (SEER) Program database to investigate cases of osteosarcoma and Ewing's sarcoma in patients younger than 40 years from 1973 to 2009. The SEER Program is managed by the National Cancer Institute and provides survival data gathered from population-based cancer registries. We used an actuarial life table analysis to determine any cancer cause-specific 5-year survival estimates conditional on 1 to 5 years of survival after diagnosis. We performed a similar analysis to determine 20-year survival from the time of diagnosis. The estimated 5-year survival improved each year after diagnosis. For local/regional osteosarcoma, the 5-year survival improved from 74.8% at baseline to 91.4% at 5 years-meaning that if a patient with localized osteosarcoma lives for 5 years, the chance of living for another 5 years is 91.4%. Similarly, the 5-year survivals for local/regional Ewing's sarcoma improved from 72.9% at baseline to 92.5% at 5 years, for metastatic osteosarcoma 35.5% at baseline to 85.4% at 5 years, and for metastatic Ewing's sarcoma 31.7% at baseline to 83.6% at 5 years. The likelihood of 20-year cause-specific survival from the time of diagnosis in osteosarcoma and Ewing's sarcoma was almost 90% or greater after 10 years of survival, suggesting that while most patients will remain disease-free indefinitely, some experience cancer-related complications years after presumed eradication. The 5-year survival estimates of osteosarcoma and Ewing's sarcoma improve with each additional year of patient survival. Knowledge of a changing risk profile is useful in counseling

An Additive-Multiplicative Cox-Aalen Regression Model

DEFF Research Database (Denmark)

Scheike, Thomas H.; Zhang, Mei-Jie

2002-01-01

Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects...

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.

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.

SURVIVAL ANALYSIS AND LENGTH-BIASED SAMPLING

Directory of Open Access Journals (Sweden)

Masoud Asgharian

2010-12-01

Full Text Available When survival data are colleted as part of a prevalent cohort study, the recruited cases have already experienced their initiating event. These prevalent cases are then followed for a fixed period of time at the end of which the subjects will either have failed or have been censored. When interests lies in estimating the survival distribution, from onset, of subjects with the disease, one must take into account that the survival times of the cases in a prevalent cohort study are left truncated. When it is possible to assume that there has not been any epidemic of the disease over the past period of time that covers the onset times of the subjects, one may assume that the underlying incidence process that generates the initiating event times is a stationary Poisson process. Under such assumption, the survival times of the recruited subjects are called “lengthbiased”. I discuss the challenges one is faced with in analyzing these type of data. To address the theoretical aspects of the work, I present asymptotic results for the NPMLE of the length-biased as well as the unbiased survival distribution. I also discuss estimating the unbiased survival function using only the follow-up time. This addresses the case that the onset times are either unknown or known with uncertainty. Some of our most recent work and open questions will be presented. These include some aspects of analysis of covariates, strong approximation, functional LIL and density estimation under length-biased sampling with right censoring. The results will be illustrated with survival data from patients with dementia, collected as part of the Canadian Study of Health and Aging (CSHA.

Gap timing and the spectral timing model.

Science.gov (United States)

Hopson, J W

1999-04-01

A hypothesized mechanism underlying gap timing was implemented in the Spectral Timing Model [Grossberg, S., Schmajuk, N., 1989. Neural dynamics of adaptive timing and temporal discrimination during associative learning. Neural Netw. 2, 79-102] , a neural network timing model. The activation of the network nodes was made to decay in the absence of the timed signal, causing the model to shift its peak response time in a fashion similar to that shown in animal subjects. The model was then able to accurately simulate a parametric study of gap timing [Cabeza de Vaca, S., Brown, B., Hemmes, N., 1994. Internal clock and memory processes in aminal timing. J. Exp. Psychol.: Anim. Behav. Process. 20 (2), 184-198]. The addition of a memory decay process appears to produce the correct pattern of results in both Scalar Expectancy Theory models and in the Spectral Timing Model, and the fact that the same process should be effective in two such disparate models argues strongly that process reflects a true aspect of animal cognition.

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

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

Effect of antibiotic decontamination of the GI tract on survival time after neutron and gamma irradiation

International Nuclear Information System (INIS)

Geraci, J.P.; Jackson, K.L.; Mariano, M.S.

1984-01-01

Antibiotic decontaminated and conventional rats were whole-body irradiated with 8 MeV neutrons (1.5 to 13 Gy) or 137 Cs gamma radiation (9 to 20 Gy). The animals were checked for survival at four hour intervals from the second to the seventh day postirradiation and at eight hour intervals on other days. Decontamination of the GI tract increased median survival time 1 to 5 days in this range of dose dependency, whereas the effect of decontamination was negligible for doses that produced mostly intestinal death. These results suggest that sepsis and endotoxin produced by bacteria from the intestinal tract play little role in acute intestinal radiation death

Nivolumab versus Cabozantinib: Comparing Overall Survival in Metastatic Renal Cell Carcinoma.

Directory of Open Access Journals (Sweden)

Witold Wiecek

Full Text Available Renal-cell carcinoma (RCC affects over 330,000 new patients every year, of whom 1/3 present with metastatic RCC (mRCC at diagnosis. Most mRCC patients treated with a first-line agent relapse within 1 year and need second-line therapy. The present study aims to compare overall survival (OS between nivolumab and cabozantinib from two recent pivotal studies comparing, respectively, each one of the two emerging treatments against everolimus in patients who relapse following first-line treatment. Comparison is traditionally carried out using the Bucher method, which assumes proportional hazard. Since OS curves intersected in one of the pivotal studies, models not assuming proportional hazards were also considered to refine the comparison. Four Bayesian parametric survival network meta-analysis models were implemented on overall survival (OS data digitized from the Kaplan-Meier curves reported in the studies. Three models allowing hazard ratios (HR to vary over time were assessed against a fixed-HR model. The Bucher method favored cabozantinib, with a fixed HR for OS vs. nivolumab of 1.09 (95% confidence interval: [0.77, 1.54]. However, all models with time-varying HR showed better fits than the fixed-HR model. The log-logistic model fitted the data best, exhibiting a HR for OS initially favoring cabozantinib, the trend inverting to favor nivolumab after month 5 (95% credible interval <1 from 10 months. The initial probability of cabozantinib conferring superior OS was 54%, falling to 41.5% by month 24. Numerical differences in study-adjusted OS estimates between the two treatments remained small. This study evidences that HR for OS of nivolumab vs. cabozantinib varies over time, favoring cabozantinib in the first months of treatment but nivolumab afterwards, a possible indication that patients with poor prognosis benefit more from cabozantinib in terms of survival, nivolumab benefiting patients with better prognosis. More evidence, including real

Comparison of Effects of Separate and Combined Sugammadex and Lipid Emulsion Administration on Hemodynamic Parameters and Survival in a Rat Model of Verapamil Toxicity.

Science.gov (United States)

Tulgar, Serkan; Kose, Halil Cihan; Demir Piroglu, Isılay; Karakilic, Evvah; Ates, Nagihan Gozde; Demir, Ahmet; Gergerli, Ruken; Guven, Selin; Piroglu, Mustafa Devrim

2016-03-25

Toxicity of calcium channel blockers leads to high patient mortality and there is no effective antidote. The benefit of using 20% lipid emulsion and sugammadex has been reported. The present study measured the effect of sugammadex and 20% lipid emulsion on hemodynamics and survival in a rat model of verapamil toxicity. In this single-blinded randomized control study, rats were separated into 4 groups of 7 rats each: Sugammadex (S), Sugammadex plus 20% lipid emulsion (SL), 20% lipid emulsion (L), and control (C). Heart rates and mean arterial pressures were monitored and noted each minute until death. Average time to death was 21.0±9.57 minutes for group C, 35.57±10.61 minutes for group S, 37.14±16.6 minutes for group L and 49.86±27.56 minutes for group SL. Time to death was significantly longer in other groups than in the control group (psugammadex and lipid emulsion had a positive effect on survival in patients with calcium channel blocker toxicity. Sugammadex and intralipid increased survival in a rat model of verapamil toxicity. The combination of both drugs may decrease cardiotoxicity. Sugammadex alone or combined with 20% lipid emulsion reduce the need for inotropic agents. The mechanism requires clarification with larger studies.

Pretreatment oral hygiene habits and survival of head and neck squamous cell carcinoma (HNSCC) patients.

Science.gov (United States)

Friemel, Juliane; Foraita, Ronja; Günther, Kathrin; Heibeck, Mathias; Günther, Frauke; Pflueger, Maren; Pohlabeln, Hermann; Behrens, Thomas; Bullerdiek, Jörn; Nimzyk, Rolf; Ahrens, Wolfgang

2016-03-11

The survival time of patients with head and neck squamous cell carcinoma (HNSCC) is related to health behavior, such as tobacco smoking and alcohol consumption. Poor oral health (OH), dental care (DC) and the frequent use of mouthwash have been shown to represent independent risk factors for head and neck cancerogenesis, but their impact on the survival of HNSCC patients has not been systematically investigated. Two hundred seventy-six incident HNSCC cases recruited for the ARCAGE study were followed through a period of 6-10 years. Interview-based information on wearing of dentures, gum bleeding, teeth brushing, use of floss and dentist visits were grouped into weighted composite scores, i.e. oral health (OH) and dental care (DH). Use of mouthwash was assessed as frequency per day. Also obtained were other types of health behavior, such as smoking, alcohol drinking and diet, appreciated as both confounding and study variables. Endpoints were progression-free survival, overall survival and tumor-specific survival. Prognostic values were estimated using Kaplan-Meier analysis and Cox proportional hazards regression models. A good dental care score, summarizing annual dental visits, daily teeth cleaning and use of floss was associated with longer overall survival time (p = .001). The results of the Cox regression models similarly suggested a higher risk of tumor progression and shortened overall survival in patients with poor dental care, but the results lost their statistical significance after other types of health behavior had been controlled for. Frequent use of mouthwash (≥ 2 times/day) significantly increased the risk of tumor-specific death (HR = 2.26; CI = 1.19-4.32). Alcohol consumption and tobacco smoking were dose-dependently associated with tumor progression and shorter overall survival. Frequent mouthwash use of ≥ 2 times/day seems to elevate the risk of tumor-specific death in HNSCC patients. Good dental care scores are associated with longer overall

Understanding survival analysis: Kaplan-Meier estimate.

Science.gov (United States)

Goel, Manish Kumar; Khanna, Pardeep; Kishore, Jugal

2010-10-01

Kaplan-Meier estimate is one of the best options to be used to measure the fraction of subjects living for a certain amount of time after treatment. In clinical trials or community trials, the effect of an intervention is assessed by measuring the number of subjects survived or saved after that intervention over a period of time. The time starting from a defined point to the occurrence of a given event, for example death is called as survival time and the analysis of group data as survival analysis. This can be affected by subjects under study that are uncooperative and refused to be remained in the study or when some of the subjects may not experience the event or death before the end of the study, although they would have experienced or died if observation continued, or we lose touch with them midway in the study. We label these situations as censored observations. The Kaplan-Meier estimate is the simplest way of computing the survival over time in spite of all these difficulties associated with subjects or situations. The survival curve can be created assuming various situations. It involves computing of probabilities of occurrence of event at a certain point of time and multiplying these successive probabilities by any earlier computed probabilities to get the final estimate. This can be calculated for two groups of subjects and also their statistical difference in the survivals. This can be used in Ayurveda research when they are comparing two drugs and looking for survival of subjects.

Hospital-based colorectal cancer survival trend of different tumor locations from 1960s to 2000s.

Directory of Open Access Journals (Sweden)

Yu-Jing Fang

Full Text Available BACKGROUND: Our aim is to explore the trend of association between the survival rates of colorectal cancer (CRC and the different clinical characteristics in patients registered from 1960s to 2000s. We hypothesized that the survival rate of CRC increases over time and varies according to anatomic subsites. METHODS: Information from a total of 4558 stage T(1-4N(1-2M0 CRC patients registered from 1960s to 2008 were analyzed. The association of CRC overall survival with age, gender, tumor locations, time, histopathology types, pathology grades, no. of examined lymph nodes, the T stage, and the N stage was analyzed. The assessment of the influence of prognostic factors on patient survival was performed using Cox's proportional hazard regression models. RESULTS: From 1960 to 2008, the studied CRC patients included 2625 (57.6% and 1933 (42.4% males and females, respectively. These included 1896 (41.6% colon cancers, and 2662 (58.4% rectum cancers. The 5-year survival rate was 49%, 58%, 58%, 70%, and 77% for the time duration of 1960s, 1970s, 1980s, 1990s and 2000s, respectively. An increased 5-year survival rate was observed in the colon cancer and rectum cancer patients. Patients older than 60 years of age were more likely to develop colonic cancer (sigmoid than rectum cancer (49.2% vs. 39.9%. The Cox regression model showed that only rectum cancer survival was related to time duration. CONCLUSION: The overall survival and 5-year survival rates showed an increase from the 1960s to 2000s. There is a trend of rightward shift of tumor location in CRC patients.

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.

Trends in Testicular Cancer Survival: A Large Population-based Analysis.

Science.gov (United States)

Sui, Wilson; Morrow, David C; Bermejo, Carlos E; Hellenthal, Nicholas J

2015-06-01

To determine whether discrepancies in testicular cancer outcomes between Caucasians and non-Caucasians are changing over time. Although testicular cancer is more common in Caucasians, studies have shown that other races have worse outcomes. Using the Surveillance, Epidemiology, and End Results registry, we identified 29,803 patients diagnosed with histologically confirmed testicular cancer between 1983 and 2011. Of these, 12,650 patients (42%) had 10-year follow-up data. We stratified the patients by age group, stage, race, and year of diagnosis and assessed 10-year overall and cancer-specific survival in each cohort. Cox proportional hazard models were used to determine the relative contributions of each stratum to cancer-specific survival. Predicted overall 10-year survival of Caucasian patients with testicular cancer increased slightly from 88% to 89% over the period studied, whereas predicted cancer-specific 10-year survival dropped slightly from 94% to 93%. In contrast, non-Caucasian men demonstrated larger changes in 10-year overall (84%-86%) and cancer-specific (88%-91%) survival. On univariate analysis, race was significantly associated with testicular cancer death, with non-Caucasian men being 1.69 times more likely to die of testicular cancer than Caucasians (hazard ratio, 1.33-2.16; 95% confidence interval, testicular cancer. These data show a convergence in cancer-specific survival between racial groups over time, suggesting that diagnostic and treatment discrepancies may be improving for non-Caucasians. Copyright © 2015 Elsevier Inc. All rights reserved.

Survival analysis with covariates in combination with multinomial analysis to parametrize time to event for multi-state models

NARCIS (Netherlands)

Feenstra, T.L.; Postmus, D.; Quik, E.H.; Langendijk, H.; Krabbe, P.F.M.

Objectives: Recent ISPOR Good practice guidelines as well as literature encourage to use a single distribution rather than the latent failure approach to model time to event for patient level simulation models with multiple competing outcomes. Aim was to apply the preferred method of a single

Survival analysis with covariates in combination with multinomial analysis to parametrize time to event for multi-state models

NARCIS (Netherlands)

Feenstra, T.L.; Postmus, D.; Quik, E.H.; Langendijk, H.; Krabbe, P.F.M.

2013-01-01

Objectives: Recent ISPOR Good practice guidelines as well as literature encourage to use a single distribution rather than the latent failure approach to model time to event for patient level simulation models with multiple competing outcomes. Aim was to apply the preferred method of a single

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.

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...

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.

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.

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.

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.)

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.

Single-incision laparoscopic surgery in a survival animal model using a transabdominal magnetic anchoring system.

Science.gov (United States)

Cho, Yong Beom; Park, Chan Ho; Kim, Hee Cheol; Yun, Seong Hyeon; Lee, Woo Yong; Chun, Ho-Kyung

2011-12-01

Though single-incision laparoscopic surgery (SILS) can reduce operative scarring and facilitates postoperative recovery, it does have some limitations, such as reduction in instrument working, difficulty in triangulation, and collision of instruments. To overcome these limitations, development of new instruments is needed. The aim of this study is to evaluate the feasibility and safety of a magnetic anchoring system in performing SILS ileocecectomy. Experiments were performed in a living dog model. Five dogs (26.3-29.2 kg) underwent ileocecectomy using a multichannel single port (OCTO port; Darim, Seoul, Korea). The port was inserted at the umbilicus and maintained a CO(2) pneumoperitoneum. Two magnet-fixated vascular clips were attached to the colon using an endoclip applicator, and it was held together across the abdominal wall by using an external handheld magnet. The cecum was then retracted in an upward direction by moving the external handheld magnet, and the mesocolon was dissected with Ultracision(®). Extracorporeal functional end-to-end anastomosis was done using a linear stapler. All animals survived during the observational period of 2 weeks, and then re-exploration was performed under general anesthesia for evaluation of intra-abdominal healing and complications. Mean operation time was 70 min (range 55-100 min), with each subsequent case taking less time. The magnetic anchoring system was effective in achieving adequate exposure in all cases. All animals survived and convalesced normally without evidence of clinical complication during the observation period. At re-exploration, all anastomoses were completely healed and there were no complications such as abscess, bleeding or organ injury. SILS ileocecectomy using a magnetic anchoring system was safe and effective in a dog model. The development of magnetic anchoring systems may be beneficial for overcoming the limitations of SILS.

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

Extinction time of a stochastic predator-prey model by the generalized cell mapping method

Science.gov (United States)

Han, Qun; Xu, Wei; Hu, Bing; Huang, Dongmei; Sun, Jian-Qiao

2018-03-01

The stochastic response and extinction time of a predator-prey model with Gaussian white noise excitations are studied by the generalized cell mapping (GCM) method based on the short-time Gaussian approximation (STGA). The methods for stochastic response probability density functions (PDFs) and extinction time statistics are developed. The Taylor expansion is used to deal with non-polynomial nonlinear terms of the model for deriving the moment equations with Gaussian closure, which are needed for the STGA in order to compute the one-step transition probabilities. The work is validated with direct Monte Carlo simulations. We have presented the transient responses showing the evolution from a Gaussian initial distribution to a non-Gaussian steady-state one. The effects of the model parameter and noise intensities on the steady-state PDFs are discussed. It is also found that the effects of noise intensities on the extinction time statistics are opposite to the effects on the limit probability distributions of the survival species.

The Impact of Timing and Graft Dysfunction on Survival and Cardiac Allograft Vasculopathy in Antibody Mediated Rejection

Science.gov (United States)

Clerkin, Kevin J.; Restaino, Susan W.; Zorn, Emmanuel; Vasilescu, Elena R.; Marboe, Charles C.; Mancini, Donna M.

2017-01-01

Background Antibody mediated rejection (AMR) has been associated with increased mortality and cardiac allograft vasculopathy (CAV). Early studies suggested that late AMR was rarely associated with graft dysfunction while recent reports have demonstrated an association with increased mortality. We sought to investigate the timing of AMR and its association with graft dysfunction, mortality, and CAV. Methods This retrospective cohort study identified all adult heart transplant recipients at Columbia University Medical Center from 2004–2013 (689 patients). There were 68 primary cases of AMR, which were stratified by early (1-year post-OHT) AMR. Kaplan-Meier survival analysis and modeling was performed with multivariable logistic regression and Cox proportional hazards regression. Results From January 1, 2004 through October 1, 2015 43 patients had early AMR (median 23 days post-OHT) and 25 had late AMR (median 1084 days post-OHT). Graft dysfunction was less common with early compared with late AMR (25.6% vs. 56%, p=0.01). Patients with late AMR had decreased post-AMR survival compared with early AMR (1-year 80% vs. 93%, 5-year 51% vs. 73%, p<0.05). When stratified by graft dysfunction, only those with late AMR and graft dysfunction had worse survival (30-day 79%, 1-year 64%, and 5-year 36%, p<0.006). The association remained irrespective of age, sex, DSA, LVAD use, reason for OHT, and recovery of graft function. Similarly, those with late AMR and graft dysfunction had accelerated development of de-novo CAV (50% at 1 year, HR 5.42, p=0.009), while all other groups were all similar to the general transplant population. Conclusion Late AMR is frequently associated with graft dysfunction. When graft dysfunction is present in late AMR there is an early and sustained increased risk of mortality and rapid development of de-novo CAV despite aggressive treatment. PMID:27423693

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.

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.

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

Time-of-Day Dependent Neuronal Injury After Ischemic Stroke: Implication of Circadian Clock Transcriptional Factor Bmal1 and Survival Kinase AKT.

Science.gov (United States)

Beker, Mustafa Caglar; Caglayan, Berrak; Yalcin, Esra; Caglayan, Ahmet Burak; Turkseven, Seyma; Gurel, Busra; Kelestemur, Taha; Sertel, Elif; Sahin, Zafer; Kutlu, Selim; Kilic, Ulkan; Baykal, Ahmet Tarik; Kilic, Ertugrul

2018-03-01

Occurrence of stroke cases displays a time-of-day variation in human. However, the mechanism linking circadian rhythm to the internal response mechanisms against pathophysiological events after ischemic stroke remained largely unknown. To this end, temporal changes in the susceptibility to ischemia/reperfusion (I/R) injury were investigated in mice in which the ischemic stroke induced at four different Zeitgeber time points with 6-h intervals (ZT0, ZT6, ZT12, and ZT18). Besides infarct volume and brain swelling, neuronal survival, apoptosis, ischemia, and circadian rhythm related proteins were examined using immunohistochemistry, Western blot, planar surface immune assay, and liquid chromatography-mass spectrometry tools. Here, we present evidence that midnight (ZT18; 24:00) I/R injury in mice resulted in significantly improved infarct volume, brain swelling, neurological deficit score, neuronal survival, and decreased apoptotic cell death compared with ischemia induced at other time points, which were associated with increased expressions of circadian proteins Bmal1, PerI, and Clock proteins and survival kinases AKT and Erk-1/2. Moreover, ribosomal protein S6, mTOR, and Bad were also significantly increased, while the levels of PRAS40, negative regulator of AKT and mTOR, and phosphorylated p53 were decreased at this time point compared to ZT0 (06:00). Furthermore, detailed proteomic analysis revealed significantly decreased CSKP, HBB-1/2, and HBA levels, while increased GNAZ, NEGR1, IMPCT, and PDE1B at midnight as compared with early morning. Our results indicate that nighttime I/R injury results in less severe neuronal damage, with increased neuronal survival, increased levels of survival kinases and circadian clock proteins, and also alters the circadian-related proteins.

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....

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

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.

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.

The influence of design characteristics on statistical inference in nonlinear estimation: A simulation study based on survival data and hazard modeling

DEFF Research Database (Denmark)

Andersen, J.S.; Bedaux, J.J.M.; Kooijman, S.A.L.M.

2000-01-01

This paper describes the influence of design characteristics on the statistical inference for an ecotoxicological hazard-based model using simulated survival data. The design characteristics of interest are the number and spacing of observations (counts) in time, the number and spacing of exposure...... concentrations (within c(min) and c(max)), and the initial number of individuals at time 0 in each concentration. A comparison of the coverage probabilities for confidence limits arising from the profile-likelihood approach and the Wald-based approach is carried out. The Wald-based approach is very sensitive...

The frontotemporal syndrome of ALS is associated with poor survival.

Science.gov (United States)

Govaarts, Rosanne; Beeldman, Emma; Kampelmacher, Mike J; van Tol, Marie-Jose; van den Berg, Leonard H; van der Kooi, Anneke J; Wijkstra, Peter J; Zijnen-Suyker, Marianne; Cobben, Nicolle A M; Schmand, Ben A; de Haan, Rob J; de Visser, Marianne; Raaphorst, Joost

2016-12-01

Thirty percent of ALS patients have a frontotemporal syndrome (FS), defined as behavioral changes or cognitive impairment. Despite previous studies, there are no firm conclusions on the effect of the FS on survival and the use of non-invasive ventilation (NIV) in ALS. We examined the effect of the FS on survival and the start and duration of NIV in ALS. Behavioral changes were defined as >22 points on the ALS-Frontotemporal-Dementia-Questionnaire or ≥3 points on ≥2 items of the Neuropsychiatric Inventory. Cognitive impairment was defined as below the fifth percentile on ≥2 tests of executive function, memory or language. Classic ALS was defined as ALS without the frontotemporal syndrome. We performed survival analyses from symptom onset and time from NIV initiation, respectively, to death. The impact of the explanatory variables on survival and NIV initiation were examined using Cox proportional hazards models. We included 110 ALS patients (76 men) with a mean age of 62 years. Median survival time was 4.3 years (95 % CI 3.53-5.13). Forty-seven patients (43 %) had an FS. Factors associated with shorter survival were FS, bulbar onset, older age at onset, short time to diagnosis and a C9orf72 repeat expansion. The adjusted hazard ratio (HR) for the FS was 2.29 (95 % CI 1.44-3.65, p NIV initiation (adjusted HR 2.70, 95 % CI 1.04-4.67, p = 0.04). In conclusion, there is an association between the frontotemporal syndrome and poor survival in ALS, which remains present after initiation of NIV.

A mathematical model resolving normal human blood lymphocyte population X-ray survival curves into six components: radiosensitivity, death rate and size of two responding sub-populations

International Nuclear Information System (INIS)

Thomson, A.E.R.; Vaughan-Smith, S.; Peel, W.E.

1982-01-01

The analysis was based on observations of survival decrease as a function of dose (range 0-5 Gy (= 500 rad)) and time after irradiation in vitro. Since lymphocyte survival is also sensitive to culture conditions the effects of radiation were examined daily up to 3 days only, while survival of control cells remained ca. 90 per cent. The time-dependent changes were resolved as the death rates (first-order governed) of lethally-hit cells (apparent survivors), so rendering these distinguishable from the morphologically identical, true (ultimate) survivors. For 12 blood donors the estimated dose permitting 37 per cent ultimate survival (D 37 value) averaged 0.72 +- 0.18 (SD) Gy for the more radiosensitive lymphocyte fraction and 2.50 +- 0.67 Gy for the less radiosensitive, each fraction proving homogeneously radiosensitive and the latter identifying substantially in kind with T-type (E-rosetting lymphocytes). The half-life of lethally-hit members of either fraction varied widely among the donors (ranges, 25-104 hours and 11-40 hours, respectively). Survival curves reconstructed by summating the numerical estimates of the six parameters according to the theoretical model closely matched those observed experimentally (ranged in multiple correlation coefficient, 0.9709-0.9994) for all donors). This signified the absence of any additional, totally radioresistant cell fraction. (author)

Effect of time interval between capecitabine intake and radiotherapy on local recurrence-free survival in preoperative chemoradiation for locally advanced rectal cancer

Energy Technology Data Exchange (ETDEWEB)

Kim, Yeon Joo; Kim, Jong Hoon; Yu, Chang Sik; Kim, Tae Won; Jang, Se Jin; Choi, Eun Kyung; Kim, Jin Cheon [Asan Medical Center, University of Ulsan College of Medicine, Seoul (Korea, Republic of); Choi, Won Sik [University of Ulsan College of Medicine, Gangneung (Korea, Republic of)

2017-06-15

The concentration of capecitabine peaks at 1–2 hours after administration. We therefore assumed that proper timing of capecitabine administration and radiotherapy would maximize radiosensitization and influence survival among patients with locally advanced rectal cancer. We retrospectively reviewed 223 patients with locally advanced rectal cancer who underwent preoperative chemoradiation, followed by surgery from January 2002 to May 2006. All patients underwent pelvic radiotherapy (50 Gy/25 fractions) and received capecitabine twice daily at 12-hour intervals (1,650 mg/m2/day). Patients were divided into two groups according to the time interval between capecitabine intake and radiotherapy. Patients who took capecitabine 1 hour before radiotherapy were classified as Group A (n = 109); all others were classified as Group B (n = 114). The median follow-up period was 72 months (range, 7 to 149 months). Although Group A had a significantly higher rate of good responses (44% vs. 25%; p = 0.005), the 5-year local recurrence-free survival rates of 93% in Group A and 97% in Group B did not differ significantly (p = 0.519). The 5-year disease-free survival and overall survival rates were also comparable between the groups. Despite the better pathological response in Group A, the time interval between capecitabine and radiotherapy administration did not have a significant effect on survivals. Further evaluations are needed to clarify the interaction of these treatment modalities.

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

Timing of chemotherapy and survival in patients with resectable gastric adenocarcinoma

Science.gov (United States)

Arrington, Amanda K; Nelson, Rebecca; Patel, Supriya S; Luu, Carrie; Ko, Michelle; Garcia-Aguilar, Julio; Kim, Joseph

2013-01-01

AIM: To evaluate the timing of chemotherapy in gastric cancer by comparing survival outcomes in treatment groups. METHODS: Patients with surgically resected gastric adenocarcinoma from 1988 to 2006 were identified from the Los Angeles County Cancer Surveillance Program. To evaluate the population most likely to receive and/or benefit from adjunct chemotherapy, inclusion criteria consisted of Stage II or III gastric cancer patients > 18 years of age who underwent curative-intent surgical resection. Patients were categorized into three groups according to the receipt of chemotherapy: (1) no chemotherapy; (2) preoperative chemotherapy; or (3) postoperative chemotherapy. Clinical and pathologic characteristics were compared across the different treatment arms. RESULTS: Of 1518 patients with surgically resected gastric cancer, 327 (21.5%) received perioperative chemotherapy. The majority of these 327 patients were male (68%) with a mean age of 61.5 years; and they were significantly younger than non-chemotherapy patients (mean age, 70.7; P advanced gastric cancer. CONCLUSION: This study supports the implementation of a randomized trial comparing the timing of perioperative therapy in patients with locally advanced gastric cancer. PMID:24392183

Nutrition management methods effective in increasing weight, survival time and functional status in ALS patients: a systematic review.

Science.gov (United States)

Kellogg, Jaylin; Bottman, Lindsey; Arra, Erin J; Selkirk, Stephen M; Kozlowski, Frances

2018-02-01

Poor prognosis and decreased survival time correlate with the nutritional status of patients with amyotrophic lateral sclerosis (ALS). Various studies were reviewed which assessed weight, body mass index (BMI), survival time and ALS functional rating scale revised (ALSFRS-R) in order to determine the best nutrition management methods for this patient population. A systematic review was conducted using CINAHL, Medline, and PubMed, and various search terms in order to determine the most recent clinical trials and observational studies that have been conducted concerning nutrition and ALS. Four articles met criteria to be included in the review. Data were extracted from these articles and were inputted into the Data Extraction Tool (DET) provided by the Academy of Nutrition and Dietetics (AND). Results showed that nutrition supplementation does promote weight stabilisation or weight gain in individuals with ALS. Given the low risk and low cost associated with intervention, early and aggressive nutrition intervention is recommended. This systematic review shows that there is a lack of high quality evidence regarding the efficacy of any dietary interventions for promoting survival in ALS or slowing disease progression; therefore more research is necessary related to effects of nutrition interventions.

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.

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.

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.)

Mixture regression models for the gap time distributions and illness-death processes.

Science.gov (United States)

Huang, Chia-Hui

2018-01-27

The aim of this study is to provide an analysis of gap event times under the illness-death model, where some subjects experience "illness" before "death" and others experience only "death." Which event is more likely to occur first and how the duration of the "illness" influences the "death" event are of interest. Because the occurrence of the second event is subject to dependent censoring, it can lead to bias in the estimation of model parameters. In this work, we generalize the semiparametric mixture models for competing risks data to accommodate the subsequent event and use a copula function to model the dependent structure between the successive events. Under the proposed method, the survival function of the censoring time does not need to be estimated when developing the inference procedure. We incorporate the cause-specific hazard functions with the counting process approach and derive a consistent estimation using the nonparametric maximum likelihood method. Simulations are conducted to demonstrate the performance of the proposed analysis, and its application in a clinical study on chronic myeloid leukemia is reported to illustrate its utility.

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.

Abundance of early functional HIV-specific CD8+ T cells does not predict AIDS-free survival time.

Directory of Open Access Journals (Sweden)

Ingrid M M Schellens

Full Text Available BACKGROUND: T-cell immunity is thought to play an important role in controlling HIV infection, and is a main target for HIV vaccine development. HIV-specific central memory CD8(+ and CD4(+ T cells producing IFNgamma and IL-2 have been associated with control of viremia and are therefore hypothesized to be truly protective and determine subsequent clinical outcome. However, the cause-effect relationship between HIV-specific cellular immunity and disease progression is unknown. We investigated in a large prospective cohort study involving 96 individuals of the Amsterdam Cohort Studies with a known date of seroconversion whether the presence of cytokine-producing HIV-specific CD8(+ T cells early in infection was associated with AIDS-free survival time. METHODS AND FINDINGS: The number and percentage of IFNgamma and IL-2 producing CD8(+ T cells was measured after in vitro stimulation with an overlapping Gag-peptide pool in T cells sampled approximately one year after seroconversion. Kaplan-Meier survival analysis and Cox proportional hazard models showed that frequencies of cytokine-producing Gag-specific CD8(+ T cells (IFNgamma, IL-2 or both shortly after seroconversion were neither associated with time to AIDS nor with the rate of CD4(+ T-cell decline. CONCLUSIONS: These data show that high numbers of functional HIV-specific CD8(+ T cells can be found early in HIV infection, irrespective of subsequent clinical outcome. The fact that both progressors and long-term non-progressors have abundant T cell immunity of the specificity associated with low viral load shortly after seroconversion suggests that the more rapid loss of T cell immunity observed in progressors may be a consequence rather than a cause of disease progression.

A gene expression signature associated with survival in metastatic melanoma

Science.gov (United States)

Mandruzzato, Susanna; Callegaro, Andrea; Turcatel, Gianluca; Francescato, Samuela; Montesco, Maria C; Chiarion-Sileni, Vanna; Mocellin, Simone; Rossi, Carlo R; Bicciato, Silvio; Wang, Ena; Marincola, Francesco M; Zanovello, Paola

2006-01-01

Background Current clinical and histopathological criteria used to define the prognosis of melanoma patients are inadequate for accurate prediction of clinical outcome. We investigated whether genome screening by means of high-throughput gene microarray might provide clinically useful information on patient survival. Methods Forty-three tumor tissues from 38 patients with stage III and stage IV melanoma were profiled with a 17,500 element cDNA microarray. Expression data were analyzed using significance analysis of microarrays (SAM) to identify genes associated with patient survival, and supervised principal components (SPC) to determine survival prediction. Results SAM analysis revealed a set of 80 probes, corresponding to 70 genes, associated with survival, i.e. 45 probes characterizing longer and 35 shorter survival times, respectively. These transcripts were included in a survival prediction model designed using SPC and cross-validation which allowed identifying 30 predicting probes out of the 80 associated with survival. Conclusion The longer-survival group of genes included those expressed in immune cells, both innate and acquired, confirming the interplay between immunological mechanisms and the natural history of melanoma. Genes linked to immune cells were totally lacking in the poor-survival group, which was instead associated with a number of genes related to highly proliferative and invasive tumor cells. PMID:17129373

A gene expression signature associated with survival in metastatic melanoma

Directory of Open Access Journals (Sweden)

Rossi Carlo R

2006-11-01

Full Text Available Abstract Background Current clinical and histopathological criteria used to define the prognosis of melanoma patients are inadequate for accurate prediction of clinical outcome. We investigated whether genome screening by means of high-throughput gene microarray might provide clinically useful information on patient survival. Methods Forty-three tumor tissues from 38 patients with stage III and stage IV melanoma were profiled with a 17,500 element cDNA microarray. Expression data were analyzed using significance analysis of microarrays (SAM to identify genes associated with patient survival, and supervised principal components (SPC to determine survival prediction. Results SAM analysis revealed a set of 80 probes, corresponding to 70 genes, associated with survival, i.e. 45 probes characterizing longer and 35 shorter survival times, respectively. These transcripts were included in a survival prediction model designed using SPC and cross-validation which allowed identifying 30 predicting probes out of the 80 associated with survival. Conclusion The longer-survival group of genes included those expressed in immune cells, both innate and acquired, confirming the interplay between immunological mechanisms and the natural history of melanoma. Genes linked to immune cells were totally lacking in the poor-survival group, which was instead associated with a number of genes related to highly proliferative and invasive tumor cells.

Prediction error variance and expected response to selection, when selection is based on the best predictor - for Gaussian and threshold characters, traits following a Poisson mixed model and survival traits

DEFF Research Database (Denmark)

Andersen, Anders Holst; Korsgaard, Inge Riis; Jensen, Just

2002-01-01