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Sample records for survival time models

  1. A generalized additive regression model for survival times

    DEFF Research Database (Denmark)

    Scheike, Thomas H.

    2001-01-01

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

  2. Time series modeling of system self-assessment of survival

    Energy Technology Data Exchange (ETDEWEB)

    Lu, H.; Kolarik, W.J. [Texas Tech Univ., Lubbock, TX (United States). Dept. of Industrial Engineering

    1999-06-01

    Self-assessment of survival for a system, subsystem or component is implemented by assessing conditional performance reliability in real-time, which includes modeling and analysis of physical performance data. This paper proposes a time series analysis approach to system self-assessment (prediction) of survival. In the approach, physical performance data are modeled in a time series. The performance forecast is based on the model developed and is converted to the reliability of system survival. In contrast to a standard regression model, a time series model, using on-line data, is suitable for the real-time performance prediction. This paper illustrates an example of time series modeling and survival assessment, regarding an excessive tool edge wear failure mode for a twist drill operation.

  3. Two Artificial Neural Networks for Modeling Discrete Survival Time of Censored Data

    Directory of Open Access Journals (Sweden)

    Taysseer Sharaf

    2015-01-01

    Full Text Available Artificial neural network (ANN theory is emerging as an alternative to conventional statistical methods in modeling nonlinear functions. The popular Cox proportional hazard model falls short in modeling survival data with nonlinear behaviors. ANN is a good alternative to the Cox PH as the proportionality of the hazard assumption and model relaxations are not required. In addition, ANN possesses a powerful capability of handling complex nonlinear relations within the risk factors associated with survival time. In this study, we present a comprehensive comparison of two different approaches of utilizing ANN in modeling smooth conditional hazard probability function. We use real melanoma cancer data to illustrate the usefulness of the proposed ANN methods. We report some significant results in comparing the survival time of male and female melanoma patients.

  4. Tobit regression for modeling mean survival time using data subject to multiple sources of censoring.

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    Gong, Qi; Schaubel, Douglas E

    2018-01-22

    Mean survival time is often of inherent interest in medical and epidemiologic studies. In the presence of censoring and when covariate effects are of interest, Cox regression is the strong default, but mostly due to convenience and familiarity. When survival times are uncensored, covariate effects can be estimated as differences in mean survival through linear regression. Tobit regression can validly be performed through maximum likelihood when the censoring times are fixed (ie, known for each subject, even in cases where the outcome is observed). However, Tobit regression is generally inapplicable when the response is subject to random right censoring. We propose Tobit regression methods based on weighted maximum likelihood which are applicable to survival times subject to both fixed and random censoring times. Under the proposed approach, known right censoring is handled naturally through the Tobit model, with inverse probability of censoring weighting used to overcome random censoring. Essentially, the re-weighting data are intended to represent those that would have been observed in the absence of random censoring. We develop methods for estimating the Tobit regression parameter, then the population mean survival time. A closed form large-sample variance estimator is proposed for the regression parameter estimator, with a semiparametric bootstrap standard error estimator derived for the population mean. The proposed methods are easily implementable using standard software. Finite-sample properties are assessed through simulation. The methods are applied to a large cohort of patients wait-listed for kidney transplantation. Copyright © 2018 John Wiley & Sons, Ltd.

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

    Science.gov (United States)

    Kharchenko, Vyacheslav; Fesenko, Herman; Doukas, Nikos

    2017-09-01

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

  6. Modelling survival

    DEFF Research Database (Denmark)

    Ashauer, Roman; Albert, Carlo; Augustine, Starrlight

    2016-01-01

    well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates...

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

    Directory of Open Access Journals (Sweden)

    Lois A Gelfand

    2016-03-01

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

  8. Flexible survival regression modelling

    DEFF Research Database (Denmark)

    Cortese, Giuliana; Scheike, Thomas H; Martinussen, Torben

    2009-01-01

    Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time-varyi...

  9. Practical considerations when analyzing discrete survival times using the grouped relative risk model.

    Science.gov (United States)

    Altman, Rachel MacKay; Henrey, Andrew

    2017-10-11

    The grouped relative risk model (GRRM) is a popular semi-parametric model for analyzing discrete survival time data. The maximum likelihood estimators (MLEs) of the regression coefficients in this model are often asymptotically efficient relative to those based on a more restrictive, parametric model. However, in settings with a small number of sampling units, the usual properties of the MLEs are not assured. In this paper, we discuss computational issues that can arise when fitting a GRRM to small samples, and describe conditions under which the MLEs can be ill-behaved. We find that, overall, estimators based on a penalized score function behave substantially better than the MLEs in this setting and, in particular, can be far more efficient. We also provide methods of assessing the fit of a GRRM to small samples.

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

    Science.gov (United States)

    Perperoglou, Aris

    2016-12-10

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

  11. Regression analysis of restricted mean survival time based on pseudo-observations

    DEFF Research Database (Denmark)

    Andersen, Per Kragh; Hansen, Mette Gerster; Klein, John P.

    censoring; hazard function; health economics; regression model; survival analysis; mean survival time; restricted mean survival time; pseudo-observations......censoring; hazard function; health economics; regression model; survival analysis; mean survival time; restricted mean survival time; pseudo-observations...

  12. Regression Analysis of Restricted Mean Survival Time Based on Pseudo-Observations

    DEFF Research Database (Denmark)

    Andersen, Per Kragh; Hansen, Mette Gerster; Klein, John P.

    2004-01-01

    censoring; hazard function; health economics; mean survival time; pseudo-observations; regression model; restricted mean survival time; survival analysis......censoring; hazard function; health economics; mean survival time; pseudo-observations; regression model; restricted mean survival time; survival analysis...

  13. A mixed linear model controlling for case underascertainment across multiple cancer registries estimated time trends in survival.

    Science.gov (United States)

    Dahm, Stefan; Bertz, Joachim; Barnes, Benjamin; Kraywinkel, Klaus

    2018-01-10

    Large temporal and geographical variation in survival rates estimated from epidemiological cancer registries coupled with heterogeneity in death certificate only (DCO) notifications makes it difficult to interpret trends in survival. The aim of our study is to introduce a method for estimating such trends while accounting for heterogeneity in DCO notifications in a cancer site-specific manner. We used the data of 4.0 million cancer cases notified in 14 German epidemiological cancer registries. Annual 5-year relative survival rates from 2002 through 2013 were estimated, and proportions of DCO notifications were recorded. "DCO-excluded" survival rates were regressed on DCO proportions and calendar years using a mixed linear model with cancer registry as a random effect. Based on this model, trends in survival rates were estimated for Germany at 0% DCO. For most cancer sites and age groups, we estimated significant positive trends in survival. Age-standardized survival for all cancers combined increased by 7.1% units for women and 10.8% units for men. The described method could be used to estimate trends in cancer survival based on the data from epidemiological cancer registries with differing DCO proportions and with changing DCO proportions over time. Copyright © 2018 Elsevier Inc. All rights reserved.

  14. Tumor Cells Growth and Survival Time with the Ketogenic Diet in Animal Models: A Systematic Review.

    Science.gov (United States)

    Khodadadi, Soheila; Sobhani, Nafiseh; Mirshekar, Somaye; Ghiasvand, Reza; Pourmasoumi, Makan; Miraghajani, Maryam; Dehsoukhteh, Somayeh Shahraki

    2017-01-01

    Recently, interest in targeted cancer therapies via metabolic pathways has been renewed with the discovery that many tumors become dependent on glucose uptake during anaerobic glycolysis. Also the inability of ketone bodies metabolization due to various deficiencies in mitochondrial enzymes is the major metabolic changes discovered in malignant cells. Therefore, administration of a ketogenic diet (KD) which is based on high in fat and low in carbohydrates might inhibit tumor growth and provide a rationale for therapeutic strategies. So, we conducted this systematic review to assess the effects of KD on the tumor cells growth and survival time in animal studies. All databases were searched from inception to November 2015. We systematically searched the PubMed, Scopus, Google Scholars, Science Direct and Cochrane Library according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. To assess the quality of included studies we used SYRCLE's RoB tool. 268 articles were obtained from databases by primary search. Only 13 studies were eligible according to inclusion criteria. From included studies, 9 articles indicate that KD had a beneficial effect on tumor growth and survival time. Tumor types were included pancreatic, prostate, gastric, colon, brain, neuroblastoma and lung cancers. In conclusions, although studies in this field are rare and inconsistence, recent findings have demonstrated that KD can potentially inhibit the malignant cell growth and increase the survival time. Because of differences physiology between animals and humans, future studies in cancer patients treated with a KD are needed.

  15. Tumor cells growth and survival time with the ketogenic diet in animal models: A systematic review

    Directory of Open Access Journals (Sweden)

    Soheila Khodadadi

    2017-01-01

    Full Text Available Recently, interest in targeted cancer therapies via metabolic pathways has been renewed with the discovery that many tumors become dependent on glucose uptake during anaerobic glycolysis. Also the inability of ketone bodies metabolization due to various deficiencies in mitochondrial enzymes is the major metabolic changes discovered in malignant cells. Therefore, administration of a ketogenic diet (KD which is based on high in fat and low in carbohydrates might inhibit tumor growth and provide a rationale for therapeutic strategies. So, we conducted this systematic review to assess the effects of KD on the tumor cells growth and survival time in animal studies. All databases were searched from inception to November 2015. We systematically searched the PubMed, Scopus, Google Scholars, Science Direct and Cochrane Library according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. To assess the quality of included studies we used SYRCLE's RoB tool. 268 articles were obtained from databases by primary search. Only 13 studies were eligible according to inclusion criteria. From included studies, 9 articles indicate that KD had a beneficial effect on tumor growth and survival time. Tumor types were included pancreatic, prostate, gastric, colon, brain, neuroblastoma and lung cancers. In conclusions, although studies in this field are rare and inconsistence, recent findings have demonstrated that KD can potentially inhibit the malignant cell growth and increase the survival time. Because of differences physiology between animals and humans, future studies in cancer patients treated with a KD are needed.

  16. 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.

  17. Genetic relationship of discrete-time survival with fertility and production in dairy cattle using bivariate models

    Directory of Open Access Journals (Sweden)

    Alenda Rafael

    2007-07-01

    Full Text Available Abstract Bivariate analyses of functional longevity in dairy cattle measured as survival to next lactation (SURV with milk yield and fertility traits were carried out. A sequential threshold-linear censored model was implemented for the analyses of SURV. Records on 96 642 lactations from 41 170 cows were used to estimate genetic parameters, using animal models, for longevity, 305 d-standardized milk production (MY305, days open (DO and number of inseminations to conception (INS in the Spanish Holstein population; 31% and 30% of lactations were censored for DO and INS, respectively. Heritability estimates for SURV and MY305 were 0.11 and 0.27 respectively; while heritability estimates for fertility traits were lower (0.07 for DO and 0.03 for INS. Antagonist genetic correlations were estimated between SURV and fertility (-0.78 and -0.54 for DO and INS, respectively or production (-0.53 for MY305, suggesting reduced functional longevity with impaired fertility and increased milk production. Longer days open seems to affect survival more than increased INS. Also, high productive cows were more problematic, less functional and more liable to being culled. The results suggest that the sequential threshold model is a method that might be considered at evaluating genetic relationship between discrete-time survival and other traits, due to its flexibility.

  18. 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.

  19. 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.

  20. Artillery Survivability Model

    Science.gov (United States)

    2016-06-01

    experiment mode also enables users to set their own design of experiment by manipulating an editable CSV file. The second one is a real-time mode that...renders a 3D virtual environment of a restricted battlefield where the survivability movements of an artillery company are visualized . This mode...provides detailed visualization of the simulation and enables future experimental uses of the simulation as a training tool. 14. SUBJECT TERMS

  1. Applied the additive hazard model to predict the survival time of patient with diffuse large B- cell lymphoma and determine the effective genes, using microarray data

    Directory of Open Access Journals (Sweden)

    Arefa Jafarzadeh Kohneloo

    2015-09-01

    Full Text Available Background: Recent studies have shown that effective genes on survival time of cancer patients play an important role as a risk factor or preventive factor. Present study was designed to determine effective genes on survival time for diffuse large B-cell lymphoma patients and predict the survival time using these selected genes. Materials & Methods: Present study is a cohort study was conducted on 40 patients with diffuse large B-cell lymphoma. For these patients, 2042 gene expression was measured. In order to predict the survival time, the composition of the semi-parametric additive survival model with two gene selection methods elastic net and lasso were used. Two methods were evaluated by plotting area under the ROC curve over time and calculating the integral of this curve. Results: Based on our findings, the elastic net method identified 10 genes, and Lasso-Cox method identified 7 genes. GENE3325X increased the survival time (P=0.006, Whereas GENE3980X and GENE377X reduced the survival time (P=0.004. These three genes were selected as important genes in both methods. Conclusion: This study showed that the elastic net method outperformed the common Lasso method in terms of predictive power. Moreover, apply the additive model instead Cox regression and using microarray data is usable way for predict the survival time of patients.

  2. A Dual-Process Discrete-Time Survival Analysis Model: Application to the Gateway Drug Hypothesis

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    Malone, Patrick S.; Lamis, Dorian A.; Masyn, Katherine E.; Northrup, Thomas F.

    2010-01-01

    The gateway drug model is a popular conceptualization of a progression most substance users are hypothesized to follow as they try different legal and illegal drugs. Most forms of the gateway hypothesis are that "softer" drugs lead to "harder," illicit drugs. However, the gateway hypothesis has been notably difficult to…

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

    Science.gov (United States)

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

    2017-07-28

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

  4. DCE-MRI prediction of survival time for patients with glioblastoma multiforme: using an adaptive neuro-fuzzy-based model and nested model selection technique.

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    Dehkordi, Azimeh N V; Kamali-Asl, Alireza; Wen, Ning; Mikkelsen, Tom; Chetty, Indrin J; Bagher-Ebadian, Hassan

    2017-09-01

    This pilot study investigates the construction of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the prediction of the survival time of patients with glioblastoma multiforme (GBM). ANFIS is trained by the pharmacokinetic (PK) parameters estimated by the model selection (MS) technique in dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data analysis, and patient age. DCE-MRI investigations of 33 treatment-naïve patients with GBM were studied. Using the modified Tofts model and MS technique, the following physiologically nested models were constructed: Model 1, no vascular leakage (normal tissue); Model 2, leakage without efflux; Model 3, leakage with bidirectional exchange (influx and efflux). For each patient, the PK parameters of the three models were estimated as follows: blood plasma volume (vp ) for Model 1; vp and volume transfer constant (K(trans) ) for Model 2; vp , K(trans) and rate constant (kep ) for Model 3. Using Cox regression analysis, the best combination of the estimated PK parameters, together with patient age, was identified for the design and training of ANFIS. A K-fold cross-validation (K = 33) technique was employed for training, testing and optimization of ANFIS. Given the survival time distribution, three classes of survival were determined and a confusion matrix for the correct classification fraction (CCF) of the trained ANFIS was estimated as an accuracy index of ANFIS's performance. Patient age, kep and ve (K(trans) /kep ) of Model 3, and K(trans) of Model 2, were found to be the most effective parameters for training ANFIS. The CCF of the trained ANFIS was 84.8%. High diagonal elements of the confusion matrix (81.8%, 90.1% and 81.8% for Class 1, Class 2 and Class 3, respectively), with low off-diagonal elements, strongly confirmed the robustness and high performance of the trained ANFIS for predicting the three survival classes. This study confirms that DCE-MRI PK analysis, combined with the MS technique and ANFIS

  5. 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

  6. Jointly modeling longitudinal proportional data and survival times with an application to the quality of life data in a breast cancer trial.

    Science.gov (United States)

    Song, Hui; Peng, Yingwei; Tu, Dongsheng

    2017-04-01

    Motivated by the joint analysis of longitudinal quality of life data and recurrence free survival times from a cancer clinical trial, we present in this paper two approaches to jointly model the longitudinal proportional measurements, which are confined in a finite interval, and survival data. Both approaches assume a proportional hazards model for the survival times. For the longitudinal component, the first approach applies the classical linear mixed model to logit transformed responses, while the second approach directly models the responses using a simplex distribution. A semiparametric method based on a penalized joint likelihood generated by the Laplace approximation is derived to fit the joint model defined by the second approach. The proposed procedures are evaluated in a simulation study and applied to the analysis of breast cancer data motivated this research.

  7. Evaluating survival model performance: a graphical approach.

    Science.gov (United States)

    Mandel, M; Galai, N; Simchen, E

    2005-06-30

    In the last decade, many statistics have been suggested to evaluate the performance of survival models. These statistics evaluate the overall performance of a model ignoring possible variability in performance over time. Using an extension of measures used in binary regression, we propose a graphical method to depict the performance of a survival model over time. The method provides estimates of performance at specific time points and can be used as an informal test for detecting time varying effects of covariates in the Cox model framework. The method is illustrated on real and simulated data using Cox proportional hazard model and rank statistics. Copyright 2005 John Wiley & Sons, Ltd.

  8. Gray’s Time-Varying Coefficients Model for Posttransplant Survival of Pediatric Liver Transplant Recipients with a Diagnosis of Cancer

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    Yi Ren

    2013-01-01

    Full Text Available Transplantation is often the only viable treatment for pediatric patients with end-stage liver disease. Making well-informed decisions on when to proceed with transplantation requires accurate predictors of transplant survival. The standard Cox proportional hazards (PH model assumes that covariate effects are time-invariant on right-censored failure time; however, this assumption may not always hold. Gray’s piecewise constant time-varying coefficients (PC-TVC model offers greater flexibility to capture the temporal changes of covariate effects without losing the mathematical simplicity of Cox PH model. In the present work, we examined the Cox PH and Gray PC-TVC models on the posttransplant survival analysis of 288 pediatric liver transplant patients diagnosed with cancer. We obtained potential predictors through univariable (P<0.15 and multivariable models with forward selection (P<0.05 for the Cox PH and Gray PC-TVC models, which coincide. While the Cox PH model provided reasonable average results in estimating covariate effects on posttransplant survival, the Gray model using piecewise constant penalized splines showed more details of how those effects change over time.

  9. Linking age, survival, and transit time distributions

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    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.

  10. Modelling survival and connectivity of

    NARCIS (Netherlands)

    van der Molen, J.; van Beek, J.; Augustine, S.; Vansteenbrugge, L.; van Walraven, L.; van Langenberg, V.; van der Veer, H.W.; Hostens, K.; Pitois, S.; Robbens, J.

    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

  11. Frailty Models in Survival Analysis

    CERN Document Server

    Wienke, Andreas

    2010-01-01

    The concept of frailty offers a convenient way to introduce unobserved heterogeneity and associations into models for survival data. In its simplest form, frailty is an unobserved random proportionality factor that modifies the hazard function of an individual or a group of related individuals. "Frailty Models in Survival Analysis" presents a comprehensive overview of the fundamental approaches in the area of frailty models. The book extensively explores how univariate frailty models can represent unobserved heterogeneity. It also emphasizes correlated frailty models as extensions of

  12. Extension of the biotic ligand model of acute toxicity to a physiologically-based model of the survival time of rainbow trout (Oncorhynchus mykiss) exposed to silver.

    Science.gov (United States)

    Paquin, Paul R; Zoltay, Viktoria; Winfield, Richard P; Wu, Kuen Benjamin; Mathew, Rooni; Santore, Robert C; Di Toro, Dominic M

    2002-09-01

    Chemical speciation controls the bioavailability and toxicity of metals in aquatic systems and regulatory agencies are recognizing this as they develop updated water quality criteria (WQC) for metals. The factors that affect bioavailability may be quantitatively evaluated with the biotic ligand model (BLM). Within the context of the BLM framework, the 'biotic ligand' is the site where metal binding results in the manifestation of a toxic effect. While the BLM does account for the speciation and complexation of dissolved metal in solution, and competition among the free metal ion and other cations for binding sites at the biotic ligand, it does not explicitly consider either the physiological effects of metals on aquatic organisms, or the direct effect of water chemistry parameters such as pH, Ca(2+)and Na(+) on the physiological state of the organism. Here, a physiologically-based model of survival time is described. In addition to incorporating the effects of water chemistry on metal availability to the organism, via the BLM, it also considers the interaction of water chemistry on the physiological condition of the organism, independent of its effect on metal availability. At the same time it explicitly considers the degree of interaction of these factors with the organism and how this affects the rate at which cumulative damage occurs. An example application of the model to toxicity data for rainbow trout exposed to silver is presented to illustrate how this framework may be used to predict survival time for alternative exposure durations. The sodium balance model (SBM) that is described herein, a specific application of a more generic ion balance model (IBM) framework, adds a new physiological dimension to the previously developed BLM. As such it also necessarily adds another layer of complexity to this already useful predictive framework. While the demonstrated capability of the SBM to predict effects in relation to exposure duration is a useful feature of this

  13. Survival of timber rattlesnakes (Crotalus horridus) estimated by capture-recapture models in relation to age, sex, color morph, time, and birthplace

    Science.gov (United States)

    Brown, W.S.; Kery, M.; Hines, J.E.

    2007-01-01

    Juvenile survival is one of the least known elements of the life history of many species, in particular snakes. We conducted a mark–recapture study of Crotalus horridus from 1978–2002 in northeastern New York near the northern limits of the species' range. We marked 588 neonates and estimated annual age-, sex-, and morph-specific recapture and survival rates using the Cormack-Jolly-Seber (CJS) model. Wild-caught neonates (field-born, n  =  407) and neonates produced by captive-held gravid females (lab-born, n  =  181) allowed comparison of the birthplace, or lab treatment effect, in estimated survival. Recapture rates declined from about 10–20% over time while increasing from young to older age classes. Estimated survival rates (S ± 1 SE) in the first year were significantly higher among field-born (black morph: S  =  0.773 ± 0.203; yellow morph: S  =  0.531 ± 0.104) than among lab-born snakes (black morph: S  =  0.411 ± 0.131; yellow morph: S  =  0.301 ± 0.081). Lower birth weights combined with a lack of field exposure until release apparently contributed to the lower survival rate of lab-born snakes. Subsequent survival estimates for 2–4-yr-old snakes were S  =  0.845 ± 0.084 for the black morph and S  =  0.999 (SE not available) for the yellow morph, and for ≥5-yr-old snakes S  =  0.958 ± 0.039 (black morph) and S  =  0.822 ± 0.034 (yellow morph). The most parsimonious model overall contained an independent time trend for survival of each age, morph, and lab-treatment group. For snakes of the first two age groups (ages 1 yr and 2–4 yr), survival tended to decline over the years for both morphs, while for adult snakes (5 yr and older), survival was constant or even slightly increased. Our data on survival and recapture are among the first rigorous estimates of these parameters in a rattlesnake and among the few yet available for any viperid snake. These data are useful for analyses of the life

  14. Model selection criterion in survival analysis

    Science.gov (United States)

    Karabey, Uǧur; Tutkun, Nihal Ata

    2017-07-01

    Survival analysis deals with time until occurrence of an event of interest such as death, recurrence of an illness, the failure of an equipment or divorce. There are various survival models with semi-parametric or parametric approaches used in medical, natural or social sciences. The decision on the most appropriate model for the data is an important point of the analysis. In literature Akaike information criteria or Bayesian information criteria are used to select among nested models. In this study,the behavior of these information criterion is discussed for a real data set.

  15. Instrumental variables estimation of exposure effects on a time-to-event response using structural cumulative survival models

    DEFF Research Database (Denmark)

    Martinussen, T.; Vansteelandt, S.; Tchetgen, E. J. Tchetgen

    2016-01-01

    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......-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...

  16. Piecewise exponential survival trees with time-dependent covariates.

    Science.gov (United States)

    Huang, X; Chen, S; Soong, S J

    1998-12-01

    Survival trees methods are nonparametric alternatives to the semiparametric Cox regression in survival analysis. In this paper, a tree-based method for censored survival data with time-dependent covariates is proposed. The proposed method assumes a very general model for the hazard function and is fully nonparametric. The recursive partitioning algorithm uses the likelihood estimation procedure to grow trees under a piecewise exponential structure that handles time-dependent covariates in a parallel way to time-independent covariates. In general, the estimated hazard at a node gives the risk for a group of individuals during a specific time period. Both cross-validation and bootstrap resampling techniques are implemented in the tree selection procedure. The performance of the proposed survival trees method is shown to be good through simulation and application to real data.

  17. Modeling survival data extending the cox model

    CERN Document Server

    Therneau, Terry M

    2000-01-01

    Extending the Cox Model is aimed at researchers, practitioners, and graduate students who have some exposure to traditional methods of survival analysis The emphasis is on semiparametric methods based on the proportional hazards model The inclusion of examples with SAS and S-PLUS code will make the book accessible to most working statisticians

  18. Timing of therapeutic intervention determines functional and survival outcomes in a mouse model of late infantile batten disease.

    Science.gov (United States)

    Cabrera-Salazar, Mario A; Roskelley, Eric M; Bu, Jie; Hodges, Bradley L; Yew, Nelson; Dodge, James C; Shihabuddin, Lamya S; Sohar, Istvan; Sleat, David E; Scheule, Ronald K; Davidson, Beverly L; Cheng, Seng H; Lobel, Peter; Passini, Marco A

    2007-10-01

    Classical late infantile neuronal ceroid lipofuscinosis (cLINCL) is a monogenic disorder caused by the loss of tripeptidyl peptidase 1 (TPP1) activity as a result of mutations in CLN2. Absence of TPP1 results in lysosomal storage with an accompanying axonal degeneration throughout the central nervous system (CNS), which leads to progressive neurodegeneration and early death. In this study, we compared the efficacies of pre- and post-symptomatic injections of recombinant adeno-associated virus (AAV) for treating the cellular and functional abnormalities of CLN2 mutant mice. Intracranial injection of AAV1-hCLN2 resulted in widespread human TPP1 (hTPP1) activity in the brain that was 10-100-fold above wild-type levels. Injections before disease onset prevented storage and spared neurons from axonal degeneration, reflected by the preservation of motor function. Furthermore, the majority of CLN2 mutant mice treated pre-symptomatically lived for at least 330 days, compared with a median survival of 151 days in untreated CLN2 mutant controls. In contrast, although injection after disease onset ameliorated lysosomal storage, there was evidence of axonal degeneration, motor function showed limited recovery, and the animals had a median lifespan of 216 days. These data illustrate the importance of early intervention for enhanced therapeutic benefit, which may provide guidance in designing novel treatment strategies for cLINCL patients.

  19. Time-varying effects of aromatic oil constituents on the survival of aquatic species: Deviations between model estimates and observations

    NARCIS (Netherlands)

    Hoop, L. de; Viaene, K.P.; Schipper, A.M.; Huijbregts, M.A.; De Laender, F.; Hendriks, A.J.

    2017-01-01

    There is a need to study the time course of toxic chemical effects on organisms because there might be a time lag between the onset of chemical exposure and the corresponding adverse effects. For aquatic organisms, crude oil and oil constituents originating from either natural seeps or human

  20. 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.

  1. 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.

  2. Estimation of the probability of bacterial population survival: Development of a probability model to describe the variability in time to inactivation of Salmonella enterica.

    Science.gov (United States)

    Koyama, Kento; Hokunan, Hidekazu; Hasegawa, Mayumi; Kawamura, Shuso; Koseki, Shigenobu

    2017-12-01

    Despite the development of numerous predictive microbial inactivation models, a model focusing on the variability in time to inactivation for a bacterial population has not been developed. Additionally, an appropriate estimation of the risk of there being any remaining bacterial survivors in foods after the application of an inactivation treatment has not yet been established. Here, Gamma distribution, as a representative probability distribution, was used to estimate the variability in time to inactivation for a bacterial population. Salmonella enterica serotype Typhimurium was evaluated for survival in a low relative humidity environment. We prepared bacterial cells with an initial concentration that was adjusted to 2 × 10n colony-forming units/2 μl (n = 1, 2, 3, 4, 5) by performing a serial 10-fold dilution, and then we placed 2 μl of the inocula into each well of 96-well microplates. The microplates were stored in a desiccated environment at 10-20% relative humidity at 5, 15, or 25 °C. The survival or death of bacterial cells for each well in the 96-well microplate was confirmed by adding tryptic soy broth as an enrichment culture. The changes in the death probability of the 96 replicated bacterial populations were described as a cumulative Gamma distribution. The variability in time to inactivation was described by transforming the cumulative Gamma distribution into a Gamma distribution. We further examined the bacterial inactivation on almond kernels and radish sprout seeds. Additionally, we described certainty levels of bacterial inactivation that ensure the death probability of a bacterial population at six decimal reduction levels, ranging from 90 to 99.9999%. Consequently, the probability model developed in the present study enables us to estimate the death probability of bacterial populations in a desiccated environment over time. This probability model may be useful for risk assessment to estimate the amount of remaining bacteria in a given

  3. Shared Frailty Model for Left-Truncated Multivariate Survival Data

    DEFF Research Database (Denmark)

    Jensen, Henrik; Brookmeyer, Ron; Aaby, Peter

    multivariate survival data, left truncation, multiplicative hazard model, shared gamma frailty, conditional model, piecewise exponential model, childhood survival......multivariate survival data, left truncation, multiplicative hazard model, shared gamma frailty, conditional model, piecewise exponential model, childhood survival...

  4. Survival-time statistics for sample space reducing stochastic processes.

    Science.gov (United States)

    Yadav, Avinash Chand

    2016-04-01

    Stochastic processes wherein the size of the state space is changing as a function of time offer models for the emergence of scale-invariant features observed in complex systems. I consider such a sample-space reducing (SSR) stochastic process that results in a random sequence of strictly decreasing integers {x(t)},0≤t≤τ, with boundary conditions x(0)=N and x(τ) = 1. This model is shown to be exactly solvable: P_{N}(τ), the probability that the process survives for time τ is analytically evaluated. In the limit of large N, the asymptotic form of this probability distribution is Gaussian, with mean and variance both varying logarithmically with system size: 〈τ〉∼lnN and σ_{τ}^{2}∼lnN. Correspondence can be made between survival-time statistics in the SSR process and record statistics of independent and identically distributed random variables.

  5. Estimation of diver survival time in a lost bell

    Energy Technology Data Exchange (ETDEWEB)

    Tipton, M.J.; Franks, C. [Surrey Univ., Guildford (United Kingdom); Meneilly, G.S. [British Columbia Univ., Vancouver, BC (Canada). Dept. of Medicine; Mekjavic, I.B. [Simon Fraser University, Vancouver (Canada). Dept. of Kinesiology

    1997-04-01

    Mathematical models of the human thermoregulatory system have been used to make predictions of the likely survival of divers in a ``lost bell`` who can be exposed to very low ambient temperatures. The circumstances considered are not the most extreme but those where, partly by shivering, the individual can re-enter thermal balance. The ability accurately to predict the level and duration of metabolic heat production is critical for the estimation of survival time under these conditions. Limitations on the accuracy of current models arise from the lack of precision in modelling the intensity and duration of the metabolic (shivering) response. A different basis for predicting shivering endurance using the time to hypogylcaemia (blood glucose level less than 2.5 mmol/1) is proposed. This leads to predicted survival times ranging from 10 to over 24 hours for those individuals able to stabilise deep body temperature. This seems to be more consistent with the limited experimental data which exists than the 8-9 hours predicted by other models. In order to help maintain blood sugar levels, and hence metabolic heat production, it is recommended that emergency rations within bells should provide 500g of carbohydrate a day. (59 figures; 221 references). (UK)

  6. Potential factors affecting survival differ by run-timing and location: linear mixed-effects models of Pacific salmonids (Oncorhynchus spp. in the Klamath River, California.

    Directory of Open Access Journals (Sweden)

    Rebecca M Quiñones

    Full Text Available Understanding factors influencing survival of Pacific salmonids (Oncorhynchus spp. is essential to species conservation, because drivers of mortality can vary over multiple spatial and temporal scales. Although recent studies have evaluated the effects of climate, habitat quality, or resource management (e.g., hatchery operations on salmonid recruitment and survival, a failure to look at multiple factors simultaneously leaves open questions about the relative importance of different factors. We analyzed the relationship between ten factors and survival (1980-2007 of four populations of salmonids with distinct life histories from two adjacent watersheds (Salmon and Scott rivers in the Klamath River basin, California. The factors were ocean abundance, ocean harvest, hatchery releases, hatchery returns, Pacific Decadal Oscillation, North Pacific Gyre Oscillation, El Niño Southern Oscillation, snow depth, flow, and watershed disturbance. Permutation tests and linear mixed-effects models tested effects of factors on survival of each taxon. Potential factors affecting survival differed among taxa and between locations. Fall Chinook salmon O. tshawytscha survival trends appeared to be driven partially or entirely by hatchery practices. Trends in three taxa (Salmon River spring Chinook salmon, Scott River fall Chinook salmon; Salmon River summer steelhead trout O. mykiss were also likely driven by factors subject to climatic forcing (ocean abundance, summer flow. Our findings underscore the importance of multiple factors in simultaneously driving population trends in widespread species such as anadromous salmonids. They also show that the suite of factors may differ among different taxa in the same location as well as among populations of the same taxa in different watersheds. In the Klamath basin, hatchery practices need to be reevaluated to protect wild salmonids.

  7. General regression neural network and Monte Carlo simulation model for survival and growth of Salmonella on raw chicken skin as a function of serotype, temperature and time for use in risk assessment

    Science.gov (United States)

    A general regression neural network and Monte Carlo simulation model for predicting survival and growth of Salmonella on raw chicken skin as a function of serotype (Typhimurium, Kentucky, Hadar), temperature (5 to 50C) and time (0 to 8 h) was developed. Poultry isolates of Salmonella with natural r...

  8. 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

  9. N-Acetylaspartate Synthase Deficiency Corrects the Myelin Phenotype in a Canavan Disease Mouse Model But Does Not Affect Survival Time.

    Science.gov (United States)

    Maier, Helena; Wang-Eckhardt, Lihua; Hartmann, Dieter; Gieselmann, Volkmar; Eckhardt, Matthias

    2015-10-28

    Canavan disease (CD) is a severe, lethal leukodystrophy caused by deficiency in aspartoacylase (ASPA), which hydrolyzes N-acetylaspartate (NAA). In the brains of CD patients, NAA accumulates to high millimolar concentrations. The pathology of the disease is characterized by loss of oligodendrocytes and spongy myelin degeneration in the CNS. Whether accumulating NAA, absence of NAA-derived acetate, or absence of any unknown functions of the ASPA enzyme is responsible for the pathology of the disease is not fully understood. We generated ASPA-deficient (Aspa(nur7/nur7)) mice that are also deficient for NAA synthase Nat8L (Nat8L(-/-)/Aspa(nur7/nur7)). These mice have no detectable NAA. Nevertheless, they exhibited normal myelin content, myelin sphingolipid composition, and full reversal of spongy myelin and axonal degeneration. Surprisingly, although pathology was fully reversed, the survival time of the mice was not prolonged. In contrast, Aspa(nur7/nur7) mice with only one intact Nat8L allele accumulated less NAA, developed a less severe pathology, phenotypic improvements, and, importantly, an almost normal survival time. Therefore, inhibition of NAA synthase is a promising therapeutic option for CD. The reduced survival rate of Nat8L(-/-)/Aspa(nur7/nur7) mice, however, indicates that complete inhibition of NAA synthase may bear unforeseeable risks for the patient. Furthermore, we demonstrate that acetate derived from NAA is not essential for myelin lipid synthesis and that loss of NAA-derived acetate does not cause the myelin phenotype of Aspa(nur7/nur7) mice. Our data clearly support the hypothesis that NAA accumulation is the major factor in the development of CD. Copyright © 2015 the authors 0270-6474/15/3514501-16$15.00/0.

  10. Cranial trauma and the assessment of posttraumatic survival time

    NARCIS (Netherlands)

    Steyn, M.; de Boer, H. H. [=Hans H.; van der Merwe, A. E.

    2014-01-01

    Assessment of trauma on skeletal remains can be very difficult, especially when it comes to the estimation of posttraumatic survival time in partially healed lesions. The ability to reliably estimate the time an individual has survived after sustaining an injury is especially important in cases of

  11. Five-year survival and median survival time of nasopharyngeal carcinoma in Hospital Universiti Sains Malaysia.

    Science.gov (United States)

    Siti-Azrin, Ab Hamid; Norsa'adah, Bachok; Naing, Nyi Nyi

    2014-01-01

    Nasopharyngeal carcinoma (NPC) is the fourth most common cancer in Malaysia. The objective of this study was to determine the five-year survival rate and median survival time of NPC patients in Hospital Universiti Sains Malaysia (USM). One hundred and thirty four NPC cases confirmed by histopathology in Hospital USM between 1st January 1998 and 31st December 2007 that fulfilled the inclusion and exclusion criteria were retrospectively reviewed. Survival time of NPC patients were estimated by Kaplan-Meier survival analysis. Log-rank tests were performed to compare survival of cases among presenting symptoms, WHO type, TNM classification and treatment modalities. The overall five-year survival rate of NPC patients was 38.0% (95% confidence interval (CI): 29.1, 46.9). The overall median survival time of NPC patients was 31.30 months (95%CI: 23.76, 38.84). The significant factors that altered the survival rate and time were age (p=0.041), cranial nerve involvement (p=0.012), stage (p=0.002), metastases (p=0.008) and treatment (p<0.001). The median survival of NPC patients is significantly longer for age≤50 years, no cranial nerve involvement, and early stage and is dependent on treatment modalities.

  12. The Effect of Platelet-Rich Plasma on Survival of the Composite Graft and the Proper Time of Injection in a Rabbit Ear Composite Graft Model

    Directory of Open Access Journals (Sweden)

    Hyun Nam Choi

    2014-11-01

    Full Text Available BackgroundAdministration of growth factors has been associated with increased viability of composite grafts greater than 1-cm in diameter. Platelet-rich plasma (PRP contains many of the growth factors studied. In this study, we evaluate the effect of PRP injection on composite graft viability and the proper time for injection.MethodsA total of 24 New Zealand White rabbits were divided into four groups. Autologous PRP was injected into the recipient sites three days before grafting in group 1, on the day of grafting in group 2, and three days after grafting in group 3. Group 4 served as control without PRP administration. Auricular composite grafts of 3-cm diameter were harvested and grafted back into place after being rotated 180 degrees. Median graft viability and microvessel density were evaluated at day 21 of graft via macroscopic photographs and immunofluorescent staining, respectively.ResultsThe median graft survival rate was 97.8% in group 1, 69.2% in group 2, 55.7% in group 3, and 40.8% in the control group. The median vessel counts were 34 (per ×200 HPF in group 1, 24.5 in group 2, 19.5 in group 3, and 10.5 in the control group.ConclusionsThis study demonstrates that PRP administration is associated with increased composite graft viability. All experimental groups showed a significantly higher survival rate and microvessel density, compared with the control group. Pre-administration of PRP was followed by the highest graft survival rate and revascularization. PRP treatments are minimally invasive, fast, easily applicable, and inexpensive, and offer a potential clinical pathway to larger composite grafts.

  13. 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.

  14. A stochastic evolutionary model for survival dynamics

    Science.gov (United States)

    Fenner, Trevor; Levene, Mark; Loizou, George

    2014-09-01

    The recent interest in human dynamics has led researchers to investigate the stochastic processes that explain human behaviour in different contexts. Here we propose a generative model to capture the essential dynamics of survival analysis, traditionally employed in clinical trials and reliability analysis in engineering. In our model, the only implicit assumption made is that the longer an actor has been in the system, the more likely it is to have failed. We derive a power-law distribution for the process and provide preliminary empirical evidence for the validity of the model from two well-known survival analysis data sets.

  15. Modelling population-based cancer survival trends using join point models for grouped survival data.

    Science.gov (United States)

    Yu, Binbing; Huang, Lan; Tiwari, Ram C; Feuer, Eric J; Johnson, Karen A

    2009-04-01

    In the United States cancer as a whole is the second leading cause of death and a major burden to health care, thus the medical progress against cancer is a major public health goal. There are many individual studies to suggest that cancer treatment breakthroughs and early diagnosis have significantly improved the prognosis of cancer patients. To better understand the relationship between medical improvements and the survival experience for the patient population at large, it is useful to evaluate cancer survival trends on the population level, e.g., to find out when and how much the cancer survival rates changed. In this paper, we analyze the population-based grouped cancer survival data by incorporating joinpoints into the survival models. A joinpoint survival model facilitates the identification of trends with significant change points in cancer survival, when related to cancer treatments or interventions. The Bayesian Information Criterion is used to select the number of joinpoints. The performance of the joinpoint survival models is evaluated with respect to cancer prognosis, joinpoint locations, annual percent changes in death rates by year of diagnosis, and sample sizes through intensive simulation studies. The model is then applied to the grouped relative survival data for several major cancer sites from the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute. The change points in the survival trends for several major cancer sites are identified and the potential driving forces behind such change points are discussed.

  16. Red cell survival time in chronic renal failure

    Energy Technology Data Exchange (ETDEWEB)

    Rath, R.N.; Das, R.K.; Panda, R.K.; Mahakur, A.C.; Patnaik, S.R. (M.K.C.G. Medical College, Berhampur (India))

    1979-10-01

    The red cell survival time was estimated in 50 cases of chronic renal failure and 20 healthy subjects, using radioactive chromium /sup 51/Cr. The mean value of red cell survival half time (T1/2/sup 51/Cr) was determined to be 25.9 +- 1.1 days in control subjects. The red cell survival half time (17.9 +- 4.67 days) was found to be significantly decreased in cases of chronic renal failure, when compared to the control group. An inverse relationship was observed between T1/2/sup 51/Cr value and blood urea, serum creatinine, the magnitude of hypertension, and duration of illness, whereas, creatinine clearance showed a direct relationship. There was no increased splenic uptake of radioactive chromium, indicating that haemolysis occurred elsewhere in the circulation other than spleen. The possible mechanism for the reduction of red cell survival time and the effect of uraemic environment on it has been discussed.

  17. Modeling association between times to recurrence of the different polarities in bipolar disorder among service seekers in urban Nigeria: a survival analysis approach

    Directory of Open Access Journals (Sweden)

    Fagbamigbe AF

    2017-07-01

    Full Text Available Adeniyi Francis Fagbamigbe,1 Victor Adesola Makanjuola2 1Department of Epidemiology and Medical Statistics, Faculty of Public Health, College of Medicine, University of Ibadan, Nigeria; 2Department of Psychiatry, College of Medicine, University of Ibadan, Ibadan Nigeria Introduction: Bipolar disorder (BD remains both a clinical and public health challenge worldwide, especially in developing countries such as Nigeria. Many studies have focused on prevalence and recurrences among BD service seekers but little has been documented on the nature, strength, direction, existence, and estimation of association between times to recurrence of the two possible polarities or mood episodes in BD. In this study, we explored the association between durations before recurrence of depression and manic episodes among people seeking treatment for BD.Methods: This analytical study used retrospective data of 467 persons who sought treatment for BD at the psychiatric clinic of University College Hospital, Ibadan, Nigeria between 2005 and 2014. Descriptive statistics were used to explore the data. We right-censored the data and obtained Kaplan–Meier estimates of the time to recurrence of the outcomes and transformed the estimates to standardized binormal data using quantile-quantile transformation. The likelihood was maximized to obtain the maximum likelihood estimate of the association parameter at 5% significance level.Results: The mean (± standard deviation age of the respondents was 32.9±12.9 years, this was lower among service seekers who were initially diagnosed with mania than among those initially diagnosed with depression (31.3±11.6, 33.2±11.9, respectively. The median survival time to recurrence of mania and depression among the patients was 1,120 and 745 days, respectively, whereas association between times to recurrence of mania and depression was maximized at 0.67 (95% confidence interval: 0.62–0.71.Conclusion: There exists a strong and positive

  18. Identifying Some Risk Factors for the Time to Death of the Elderly Using the Semi-Parametric Blended Model of Survival Analysis With Competing Risks

    Directory of Open Access Journals (Sweden)

    Samane Hajiabbasi

    2018-01-01

    Conclusion In single-variable fitting, age, history of myocardial infarction, history of stroke, and kidney problems were identified to have significant effects on the time to death of the elderly. Based on one-variable semi-parametric competing risk mixture fitted models, more significant risk factors for the time to death of elderly was identified when compared with a fitted multivariate mode to the data. This implies that the role of some independent variables can be explained by other independent variables.

  19. Survival time of direct dental restorations in adults

    Directory of Open Access Journals (Sweden)

    Thaís Torres Barros Dutra

    Full Text Available AbstractIntroductionThe presence of dental caries is the main reason for the placement and replacement of restorations. Maintaining restorations to a satisfactory clinical condition is a challenge, despite the evolution of materials and surgical operative techniques.ObjectiveTo investigate the survival time and technical-operatory characteristics of dental restorations among adults in Teresina-PI.Material and methodData collection was carried out from September 2009 to January 2010 at a non-profit dental service. Data were collected at the moment of restoration replacement. The sample consisted of 262 defective restorations in 139 individuals. Survival time was calculated using the placement date that was registered on the individual’s dental form. Kruskal-Wallis and Mann-Whitney tests were used to compare the survival time of the different types of restorations and the chi-square test was used to assess the association between qualitative variables, at a 5% significance level.ResultThe median survival time of the restorations was 2 years. The survival time for amalgam was higher than for composite and glass ionomer cement (p=0.004. The most replaced dental material was the composite (66.4%. The majority of the replaced restorations had been placed in anterior teeth, in proximal surfaces.ConclusionAmalgam restorations have a longer survival time than composite resin. Technical and operatory variables had no influence on the survival time of restorations. Dental restorations have a low survival time and this fact might be associated with the decion-making process that is adopted by the professionals.

  20. Mediation analysis for survival data using semiparametric probit models.

    Science.gov (United States)

    Huang, Yen-Tsung; Cai, Tianxi

    2016-06-01

    Causal mediation modeling has become a popular approach for studying the effect of an exposure on an outcome through mediators. Currently, the literature on mediation analyses with survival outcomes largely focused on settings with a single mediator and quantified the mediation effects on the hazard, log hazard and log survival time (Lange and Hansen 2011; VanderWeele 2011). In this article, we propose a multi-mediator model for survival data by employing a flexible semiparametric probit model. We characterize path-specific effects (PSEs) of the exposure on the outcome mediated through specific mediators. We derive closed form expressions for PSEs on a transformed survival time and the survival probabilities. Statistical inference on the PSEs is developed using a nonparametric maximum likelihood estimator under the semiparametric probit model and the functional Delta method. Results from simulation studies suggest that our proposed methods perform well in finite sample. We illustrate the utility of our method in a genomic study of glioblastoma multiforme survival. © 2015, The International Biometric Society.

  1. Investigation of survival time of some poultry mycoplasmas.

    Science.gov (United States)

    Simon, A; Tamasi, G; Stipkovits, L

    1989-01-01

    Glucose-fermenting poultry mycoplasmas (Mycoplasma [M.] gallisepticum, M. pullorum, M. gallinaceum, M. gallopavonis) were tested in 2 experiments for their survival time at 20 degrees C and 37 degrees C on 18 different materials used on farms and in hatcheries. All mycoplasmas survived up to 16 days in egg yolk at both temperatures. On other materials, like egg shell, egg white, paper trails, feather, and others mycoplasmas generally survived 2 to 16 days at 20 degrees C. M. gallinaceum and M. gallopavonis proved more resistant to the environment than M. gallisepticum and M. pullorum.

  2. Combining parametric, semi-parametric, and non-parametric survival models with stacked survival models.

    Science.gov (United States)

    Wey, Andrew; Connett, John; Rudser, Kyle

    2015-07-01

    For estimating conditional survival functions, non-parametric estimators can be preferred to parametric and semi-parametric estimators due to relaxed assumptions that enable robust estimation. Yet, even when misspecified, parametric and semi-parametric estimators can possess better operating characteristics in small sample sizes due to smaller variance than non-parametric estimators. Fundamentally, this is a bias-variance trade-off situation in that the sample size is not large enough to take advantage of the low bias of non-parametric estimation. Stacked survival models estimate an optimally weighted combination of models that can span parametric, semi-parametric, and non-parametric models by minimizing prediction error. An extensive simulation study demonstrates that stacked survival models consistently perform well across a wide range of scenarios by adaptively balancing the strengths and weaknesses of individual candidate survival models. In addition, stacked survival models perform as well as or better than the model selected through cross-validation. Finally, stacked survival models are applied to a well-known German breast cancer study. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  3. Preoperative Nutritional Risk Index to predict postoperative survival time in primary liver cancer patients.

    Science.gov (United States)

    Bo, Yacong; Yao, Mingjie; Zhang, Ling; Bekalo, Wolde; Lu, Weiquan; Lu, Quanjun

    2015-01-01

    We designed this study to determine the predictive value of Nutritional Risk Index (NRI) for postoperative survival time of patients who had undergone hepatectomy for primary liver cancer. The 620 patients who underwent hepatectomy for primary liver cancer (PLC) in the Department of Hepatobiliary Surgery, Cancer Hospital of Henan Province, Zhengzhou, China from December 1, 2008 to December 1, 2012 were followed up. A nutritional risk index (NRI) was used to screen the patients with malnutrition (NRI100) patients had longer postoperative survival time compared with malnourished patients. NRI values>100 was sig-nificantly associated with longer postoperative survival time. Cox proportional hazards model showed that NRI was an independent predictor of postoperative survival time and that NRI varied inversely with the risk of death. The patients with NRI values>100 survived longer than those with NRI values

  4. Graphing survival curve estimates for time-dependent covariates.

    Science.gov (United States)

    Schultz, Lonni R; Peterson, Edward L; Breslau, Naomi

    2002-01-01

    Graphical representation of statistical results is often used to assist readers in the interpretation of the findings. This is especially true for survival analysis where there is an interest in explaining the patterns of survival over time for specific covariates. For fixed categorical covariates, such as a group membership indicator, Kaplan-Meier estimates (1958) can be used to display the curves. For time-dependent covariates this method may not be adequate. Simon and Makuch (1984) proposed a technique that evaluates the covariate status of the individuals remaining at risk at each event time. The method takes into account the change in an individual's covariate status over time. The survival computations are the same as the Kaplan-Meier method, in that the conditional survival estimates are the function of the ratio of the number of events to the number at risk at each event time. The difference between the two methods is that the individuals at risk within each level defined by the covariate is not fixed at time 0 in the Simon and Makuch method as it is with the Kaplan-Meier method. Examples of how the two methods can differ for time dependent covariates in Cox proportional hazards regression analysis are presented.

  5. Prediction of survival for older hospitalized patients: the HELP survival model. Hospitalized Elderly Longitudinal Project.

    Science.gov (United States)

    Teno, J M; Harrell, F E; Knaus, W; Phillips, R S; Wu, A W; Connors, A; Wenger, N S; Wagner, D; Galanos, A; Desbiens, N A; Lynn, J

    2000-05-01

    To develop and validate a model estimating the survival time of hospitalized persons aged 80 years and older. A prospective cohort study with mortality follow-up using the National Death Index. Four teaching hospitals in the US. Hospitalized patients enrolled between January 1993 and November 1994 in the Hospitalized Elderly Longitudinal Project (HELP). Patients were excluded if their length of hospital stay was 48 hours or less or if admitted electively for planned surgery. A log-normal model of survival time up to 711 days was developed with the following variables: patient demographics, disease category, nursing home residence, severity of physiologic imbalance, chart documentation of weight loss, current quality of life, exercise capacity, and functional status. We assessed whether model accuracy could be improved by including symptoms of depression or history of recent fall, serum albumin, physician's subjective estimate of prognosis, and physician and patient preferences for general approach to care. A total of 1266 patients were enrolled over a 10-month period, (median age 84.9, 61% female, 68% with one or more dependency), and 505 (40%) died during an average follow-up of more than 2 years. Important prognostic factors included the Acute Physiology Score of APACHE III collected on the third hospital day, modified Glasgow coma score, major diagnosis (ICU categories together, congestive heart failure, cancer, orthopedic, and all other), age, activities of daily living, exercise capacity, chart documentation of weight loss, and global quality of life. The Somers' Dxy for a model including these factors was 0.48 (equivalent to a receiver-operator curve (ROC) area of 0.74, suggesting good discrimination). Bootstrap estimation indicated good model validation (corrected Dxy of 0.46, ROC of 0.73). A nomogram based on this log-normal model is presented to facilitate calculation of median survival time and 10th and 90th percentile of survival time. A count of

  6. effect of liquid nitrogen storage time on the survival and ...

    African Journals Online (AJOL)

    Administrator

    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 ...

  7. 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 ...

  8. Assessment of lead-time bias in estimates of relative survival for breast cancer.

    Science.gov (United States)

    Andersson, Therese M-L; Rutherford, Mark J; Humphreys, Keith

    2017-02-01

    Relative survival ratios (RSRs) can be useful for evaluating the impact of changes in cancer care on the prognosis of cancer patients or for comparing the prognosis for different subgroups of patients, but their use is problematic for cancer sites where screening has been introduced due to the potential of lead-time bias. Lead-time is survival time that is added to a patient's survival time because of an earlier diagnosis irrespective of a possibly postponed time of death. In the presence of screening it is difficult to disentangle how much of an observed improvement in survival is real and how much is due to lead-time bias. Even so, RSRs are often presented for breast cancer, a site where screening has led to early diagnosis, with the assumption that the lead-time bias is small. We describe a simulation-based framework for studying the lead-time bias due to mammography screening on RSRs of breast cancer based on a natural history model developed in a Swedish setting. We have performed simulations, using this framework, under different assumptions for screening sensitivity and breast cancer survival with the aim of estimating the lead-time bias. Screening every second year among ages 40-75 was introduced assuming that screening had no effect on survival, except for lead-time bias. Relative survival was estimated both with and without screening to enable quantification of the lead-time bias. Scenarios with low, moderate and high breast cancer survival, and low, moderate and high screening sensitivity were simulated, and the lead-time bias assessed in all scenarios. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Adenoviral vector-mediated gene transfer: timing of wild-type p53 gene expression in vivo and effect of tumor transduction on survival in a rat glioma brachytherapy model.

    Science.gov (United States)

    Bampoe, J; Glen, J; Hubbard, S L; Salhia, B; Shannon, P; Rutka, J; Bernstein, M

    2000-08-01

    This study sought to investigate modification of the radiation response in a rat 9L brain tumor model in vivo by the wild-type p53 gene (wtp53). Determination of the timing and dose of radiation therapy required the assessment of the duration of the effect of wtp53 expression on 9L tumors after in vivo transfection. Anesthetized male F-344 rats each were stereotactically inoculated with 4 x 10(4) 9L gliosarcoma cells through a skull screw into the cerebrum in the right frontal region. Twelve-day-old tumors were inoculated through the screw with recombinant adenoviral vectors under isoflurane anaesthesia: control rats with Ad5/RSV/GL2 (carrying the luciferase gene), and study rats with Ad5CMV-p53 (carrying the wtp53 gene). Brain tumors removed at specific times after transfection were measured, homogenized, and lysed and wtp53 expression determined by Western blot analysis. Four groups of nine rats were, subsequently, implanted with iodine-125 seeds 15 days post-tumor inoculation to give a minimum tumor dose of 40 or 60 Gy. We demonstrated transfer of wtp53 into rat 9L tumors in vivo using the Ad5CMV-p53 vector. The expression of wtp53 was demonstrated to be maximum between days 1 and 3 post-vector inoculation. Tumors expressing wtp53 were smaller than controls transfected with Ad5/RSV/GL2 but this difference was not statistically significant. Radiation made a significant difference to the survival of tumor-bearing rats. Moreover, wtp53 expression conferred a significant additional survival advantage. The expression of wtp53 significantly improves the survival of irradiated tumor-bearing rats in our model.

  10. 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....... The discrete time models used are multivariate variants of the discrete relative risk models. These models allow for regular parametric likelihood-based inference by exploring a coincidence of their likelihood functions and the likelihood functions of suitably defined multivariate generalized linear mixed...... models. The models include a dispersion parameter, which is essential for obtaining a decomposition of the variance of the trait of interest as a sum of parcels representing the additive genetic effects, environmental effects and unspecified sources of variability; as required in quantitative genetic...

  11. 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....... The discrete time models used are multivariate variants of the discrete relative risk models. These models allow for regular parametric likelihood-based inference by exploring a coincidence of their likelihood functions and the likelihood functions of suitably defined multivariate generalized linear mixed...... models. The models include a dispersion parameter, which is essential for obtaining a decomposition of the variance of the trait of interest as a sum of parcels representing the additive genetic effects, environmental effects and unspecified sources of variability; as required in quantitative genetic...

  12. A mixture model for the joint analysis of latent developmental trajectories and survival

    NARCIS (Netherlands)

    Klein Entink, R.H.; Fox, J.P.; Hout, A. van den

    2011-01-01

    A general joint modeling framework is proposed that includes a parametric stratified survival component for continuous time survival data, and a mixture multilevel item response component to model latent developmental trajectories given mixed discrete response data. The joint model is illustrated in

  13. Modeling Complex Time Limits

    Directory of Open Access Journals (Sweden)

    Oleg Svatos

    2013-01-01

    Full Text Available In this paper we analyze complexity of time limits we can find especially in regulated processes of public administration. First we review the most popular process modeling languages. There is defined an example scenario based on the current Czech legislature which is then captured in discussed process modeling languages. Analysis shows that the contemporary process modeling languages support capturing of the time limit only partially. This causes troubles to analysts and unnecessary complexity of the models. Upon unsatisfying results of the contemporary process modeling languages we analyze the complexity of the time limits in greater detail and outline lifecycles of a time limit using the multiple dynamic generalizations pattern. As an alternative to the popular process modeling languages there is presented PSD process modeling language, which supports the defined lifecycles of a time limit natively and therefore allows keeping the models simple and easy to understand.

  14. Modeling post-fledging survival of lark buntings in response to ecological and biological factors

    Science.gov (United States)

    Yackel Adams, A.A.; Skagen, S.K.; Savidge, J.A.

    2006-01-01

    We evaluated the influences of several ecological, biological, and methodological factors on post-fledging survival of a shortgrass prairie bird, the Lark Bunting (Calamospiza melanocorys). We estimated daily post-fledging survival (n = 206, 82 broods) using radiotelemetry and color bands to track fledglings. Daily survival probabilities were best explained by drought intensity, time in season (quadratic trend), ages ≤3 d post-fledging, and rank given drought intensity. Drought intensity had a strong negative effect on survival. Rank was an important predictor of fledgling survival only during the severe drought of 2002 when the smallest fledglings had lower survival. Recently fledged young (ages ≤3 d post-fledging) undergoing the transition from nest to surrounding habitat experienced markedly lower survival, demonstrating the vulnerable nature of this time period. Survival was greater in mid and late season than early season, corresponding to our assumptions of food availability. Neither mark type nor sex of attending parent influenced survival. The model-averaged product of the 22-d survival calculated using mean rank and median value of time in season was 0.360 ± 0.08 in 2001 and 0.276 ± 0.08 in 2002. Survival estimates that account for age, condition of young, ecological conditions, and other factors are important for parameterization of realistic population models. Biologists using population growth models to elucidate mechanisms of population declines should attempt to estimate species-specific of post-fledging survival rather than use generalized estimates.

  15. Characteristic Time Model Validation

    Science.gov (United States)

    1988-09-01

    Characteristic Time Model Validation Final Technical Report .’ ". Tallio, R.C. Prior, Jr., and A. M. Mellor* U.S. Army Research Office Contract...Park, NC 27709-2211 I N I 11, TITLE (Include Securrty Cassification) Characteristic Time Model Validation (unclassified)512 PERSONAL AUTHOR(S) Tallio...number) FIELD GROUP SUB-GROUP Two-dimensional confined shear layers; two-dimensional prefilming airblast atomizers; characteristic time model; finite

  16. List and liver transplant survival according to waiting time in patients with hepatocellular carcinoma.

    Science.gov (United States)

    Salvalaggio, P R; Felga, G; Axelrod, D A; Della Guardia, B; Almeida, M D; Rezende, M B

    2015-03-01

    The time that patients with hepatocellular carcinoma (HCC) can safely remain on the waiting list for liver transplantation (LT) is unknown. We investigated whether waiting time on the list impacts transplant survival of HCC candidates and transplant recipients. This is a single-center retrospective study of 283 adults with HCC. Patients were divided in groups according to waiting-list time. The main endpoint was survival. The median waiting time for LT was 4.9 months. The dropout rates at 3-, 6-, and 12-months were 6.4%, 12.4%, and 17.7%, respectively. Mortality on the list was 4.8%, but varied depending of the time on the list. Patients who waited less than 3-months had an inferior overall survival when compared to the other groups (p = 0.027). Prolonged time on the list significantly reduced mortality in this analysis (p = 0.02, HR = 0.28). Model for End Stage Liver Disease (MELD) score at transplantation did also independently impact overall survival (p = 0.03, HR = 1.06). MELD was the only factor that independently impacted posttransplant survival (p = 0.048, HR = 1.05). We conclude that waiting time had no relation with posttransplant survival. It is beneficial to prolong the waiting list time for HCC candidates without having a negative impact in posttransplant survival. © Copyright 2015 The American Society of Transplantation and the American Society of Transplant Surgeons.

  17. 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.......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....

  18. 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.

  19. The effect of time until surgical intervention on survival in dogs with secondary septic peritonitis.

    Science.gov (United States)

    Bush, Maxwell; Carno, Margaret A; St Germaine, Lindsay; Hoffmann, Daniel E

    2016-12-01

    This retrospective study examined the effect of time to intervention on outcome in cases of dogs with secondary septic peritonitis, and also searched for other potential prognostic factors. The medical records of 55 dogs were reviewed. No association was found between outcome and the time from hospital admission to surgical source control. However, several other factors were found to influence survival, including: age, needing vasopressors, lactate, pre-operative packed cell volume, serum alkaline phosphatase, serum total bilirubin, and post-operative serum albumin. These values were then used to create accurate pre- and post-operative survival prediction models.

  20. The survival time of chocolates on hospital wards: covert observational study.

    Science.gov (United States)

    Gajendragadkar, Parag R; Moualed, Daniel J; Nicolson, Phillip L R; Adjei, Felicia D; Cakebread, Holly E; Duehmke, Rudolf M; Martin, Claire A

    2013-12-14

    To quantify the consumption of chocolates in a hospital ward environment. Multicentre, prospective, covert observational study. Four wards at three hospitals (where the authors worked) within the United Kingdom. Boxes of Quality Street (Nestlé) and Roses (Cadbury) on the ward and anyone eating these chocolates. Observers covertly placed two 350 g boxes of Quality Street and Roses chocolates on each ward (eight boxes were used in the study containing a total of 258 individual chocolates). These boxes were kept under continuous covert surveillance, with the time recorded when each chocolate was eaten. Median survival time of a chocolate. 191 out of 258 (74%) chocolates were observed being eaten. The mean total observation period was 254 minutes (95% confidence interval 179 to 329). The median survival time of a chocolate was 51 minutes (39 to 63). The model of chocolate consumption was non-linear, with an initial rapid rate of consumption that slowed with time. An exponential decay model best fitted these findings (model R(2)=0.844, Pchocolates to be eaten) of 99 minutes. The mean time taken to open a box of chocolates from first appearance on the ward was 12 minutes (95% confidence interval 0 to 24). Quality Street chocolates survived longer than Roses chocolates (hazard ratio for survival of Roses v Quality Street 0.70, 95% confidence interval 0.53 to 0.93, P=0.014). The highest percentages of chocolates were consumed by healthcare assistants (28%) and nurses (28%), followed by doctors (15%). From our observational study, chocolate survival in a hospital ward was relatively short, and was modelled well by an exponential decay model. Roses chocolates were preferentially consumed to Quality Street chocolates in a ward setting. Chocolates were consumed primarily by healthcare assistants and nurses, followed by doctors. Further practical studies are needed.

  1. 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.

  2. Survival model construction guided by fit and predictive strength.

    Science.gov (United States)

    Chauvel, Cécile; O'Quigley, John

    2017-06-01

    Survival model construction can be guided by goodness-of-fit techniques as well as measures of predictive strength. Here, we aim to bring together these distinct techniques within the context of a single framework. The goal is how to best characterize and code the effects of the variables, in particular time dependencies, when taken either singly or in combination with other related covariates. Simple graphical techniques can provide an immediate visual indication as to the goodness-of-fit but, in cases of departure from model assumptions, will point in the direction of a more involved and richer alternative model. These techniques appear to be intuitive. This intuition is backed up by formal theorems that underlie the process of building richer models from simpler ones. Measures of predictive strength are used in conjunction with these goodness-of-fit techniques and, again, formal theorems show that these measures can be used to help identify models closest to the unknown non-proportional hazards mechanism that we can suppose generates the observations. Illustrations from studies in breast cancer show how these tools can be of help in guiding the practical problem of efficient model construction for survival data. © 2016, The International Biometric Society.

  3. Random-effects regression analysis of correlated grouped-time survival data.

    Science.gov (United States)

    Hedeker, D; Siddiqui, O; Hu, F B

    2000-04-01

    Random-effects regression modelling is proposed for analysis of correlated grouped-time survival data. Two analysis approaches are considered. The first treats survival time as an ordinal outcome, which is either right-censored or not. The second approach treats survival time as a set of dichotomous indicators of whether the event occurred for time periods up to the period of the event or censor. For either approach both proportional hazards and proportional odds versions of the random-effects model are developed, while partial proportional hazards and odds generalizations are described for the latter approach. For estimation, a full-information maximum marginal likelihood solution is implemented using numerical quadrature to integrate over the distribution of multiple random effects. The quadrature solution allows some flexibility in the choice of distributions for the random effects; both normal and rectangular distributions are considered in this article. An analysis of a dataset where students are clustered within schools is used to illustrate features of random-effects analysis of clustered grouped-time survival data.

  4. 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...... between the psychosocial work environment and sickness absence were used to illustrate the results. RESULTS: Standard methods were found to underestimate true effect sizes by approximately one-tenth [method i] and one-third [method ii] and to have lower statistical power than frailty models. CONCLUSIONS...

  5. 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.

  6. 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.

  7. A life-cycle model with ambiguous survival beliefs

    NARCIS (Netherlands)

    Groneck, Max; Ludwig, Alexander; Zimper, Alexander

    Based on a cognitive notion of neo-additive capacities reflecting likelihood insensitivity with respect to survival chances, we construct a Choquet Bayesian learning model over the life-cycle that generates a motivational notion of neo-additive survival beliefs expressing ambiguity attitudes. We

  8. Prediction of survival with alternative modeling techniques using pseudo values

    NARCIS (Netherlands)

    T. van der Ploeg (Tjeerd); F.R. Datema (Frank); R.J. Baatenburg de Jong (Robert Jan); E.W. Steyerberg (Ewout)

    2014-01-01

    textabstractBackground: The use of alternative modeling techniques for predicting patient survival is complicated by the fact that some alternative techniques cannot readily deal with censoring, which is essential for analyzing survival data. In the current study, we aimed to demonstrate that pseudo

  9. Travel time reliability modeling.

    Science.gov (United States)

    2011-07-01

    This report includes three papers as follows: : 1. Guo F., Rakha H., and Park S. (2010), "A Multi-state Travel Time Reliability Model," : Transportation Research Record: Journal of the Transportation Research Board, n 2188, : pp. 46-54. : 2. Park S.,...

  10. Looking inward: The impact of operative time on graft survival after liver transplantation.

    Science.gov (United States)

    Lee, David D; Li, Jun; Wang, Guihua; Croome, Kristopher P; Burns, Justin M; Perry, Dana K; Nguyen, Justin H; Hopp, Wallace J; Taner, C Burcin

    2017-10-01

    Operative time often has been cited as an important factor for postoperative outcomes. Despite this belief, most efforts to improve liver transplant outcomes have largely focused on only patient and donor factors, and little attention has been paid on operative time. The primary objective of this project was to determine the impact of operative time on graft survival after liver transplant. A retrospective review of 2,877 consecutive liver transplants performed at a single institution was studied. Data regarding recipient, donor, and operative characteristics, including detailed granular operative times were collected prospectively and retrospectively reviewed. Using an instrument variable approach, Cox multivariate modeling was performed to assess the impact of operative time without the confounding of known and unknown variables. Of the 2,396 patients who met the criteria for review, the most important factors determining liver transplant graft survival included recipient history of Hepatitis C (hazard ratio 1.45, P = .02), donor age (hazard ratio 1.23, P = .03), use of liver graft from donation after cardiac death donor (hazard ratio 1.50, P operative time (hazard ratio 1.26, P = .01). In detailed analysis of stages of the liver transplant operation, the time interval from incision to anhepatic phase was associated with graft survival (hazard ratio 1.33; P = .02). Using a novel instrument variable approach, we demonstrate that operative time (in particular, the time interval from incision to anhepatic time) has a significant impact on graft survival. It also seems that some of this efficiency is under the influence of the transplant surgeon. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. 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

  12. 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

  13. A hierarchical nest survival model integrating incomplete temporally varying covariates

    Science.gov (United States)

    Converse, Sarah J.; Royle, J. Andrew; Adler, Peter H.; Urbanek, Richard P.; Barzan, Jeb A.

    2013-01-01

    Nest success is a critical determinant of the dynamics of avian populations, and nest survival modeling has played a key role in advancing avian ecology and management. Beginning with the development of daily nest survival models, and proceeding through subsequent extensions, the capacity for modeling the effects of hypothesized factors on nest survival has expanded greatly. We extend nest survival models further by introducing an approach to deal with incompletely observed, temporally varying covariates using a hierarchical model. Hierarchical modeling offers a way to separate process and observational components of demographic models to obtain estimates of the parameters of primary interest, and to evaluate structural effects of ecological and management interest. We built a hierarchical model for daily nest survival to analyze nest data from reintroduced whooping cranes (Grus americana) in the Eastern Migratory Population. This reintroduction effort has been beset by poor reproduction, apparently due primarily to nest abandonment by breeding birds. We used the model to assess support for the hypothesis that nest abandonment is caused by harassment from biting insects. We obtained indices of blood-feeding insect populations based on the spatially interpolated counts of insects captured in carbon dioxide traps. However, insect trapping was not conducted daily, and so we had incomplete information on a temporally variable covariate of interest. We therefore supplemented our nest survival model with a parallel model for estimating the values of the missing insect covariates. We used Bayesian model selection to identify the best predictors of daily nest survival. Our results suggest that the black fly Simulium annulus may be negatively affecting nest survival of reintroduced whooping cranes, with decreasing nest survival as abundance of S. annulus increases. The modeling framework we have developed will be applied in the future to a larger data set to evaluate the

  14. Functional Status, Time to Transplantation, and Survival Benefit of Kidney Transplantation Among Wait-Listed Candidates

    Science.gov (United States)

    Reese, Peter P.; Shults, Justine; Bloom, Roy D.; Mussell, Adam; Harhay, Meera N.; Abt, Peter; Levine, Matthew; Johansen, Kirsten L.; Karlawish, Jason T.; Feldman, Harold I.

    2015-01-01

    Background In the context of an aging end-stage renal disease population with multiple comorbidities, transplantation professionals face challenges in evaluating the global health of patients awaiting kidney transplantation. Functional status might be useful for identifying which patients will derive a survival benefit from transplantation versus dialysis. Study Design Retrospective cohort study of wait-listed patients using data on functional status from a national dialysis provider linked to United Network for Organ Sharing registry data. Setting & Participants Adult kidney transplant candidates added to the waiting list between the years 2000 and 2006. Predictor Physical function scale of the Medical Outcomes Study 36-Item Short Form Healthy Survey, analyzed as a time-varying covariate. Outcomes Kidney transplantation; Survival benefit of transplantation versus remaining wait-listed. Measurements We used multivariable Cox regression to assess the association between physical function with study outcomes. In survival benefit analyses, transplant status was modeled as a time-varying covariate. Results The cohort comprised 19,242 kidney transplant candidates (median age, 51 years; 36% black race) receiving maintenance dialysis. Candidates in the lowest baseline physical function quartile were more likely to be inactivated (adjusted HR vs. highest quartile, 1.30; 95% CI, 1.21-1.39) and less likely to undergo transplantation (adjusted HR vs. highest quartile, 0.64; 95% CI, 0.61-0.68). After transplantation, worse physical function was associated with shorter 3-year survival (84% vs. 92% for the lowest vs. highest function quartiles). However, compared to dialysis, transplantation was associated with a statistically significant survival benefit by 9 months for patients in every function quartile. Limitations Functional status is self-reported. Conclusions Even patients with low function appear to live longer with kidney transplantation versus dialysis. For waitlisted

  15. Survival

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — These data provide information on the survival of California red-legged frogs in a unique ecosystem to better conserve this threatened species while restoring...

  16. Rubber dam may increase the survival time of dental restorations.

    Science.gov (United States)

    Keys, William; Carson, Susan J

    2017-03-01

    Data sourcesCochrane Oral Health's Trials Register, Cochrane Central Register of Controlled Trials (CENTRAL), Medline, Embase, LILACS, SciELO, Chinese BioMedical Literature Database, VIP, China National Knowledge Infrastructure, ClinicalTrials.gov, World Health Organization International Clinical Trials Registry Platform, OpenGrey and Sciencepaper Online databases. Handsearches in a number of journals.Study selectionRandomised controlled trials, including split-mouth studies assessing the effects of rubber dam isolation for restorative treatments in dental patients.Data extraction and synthesisTwo review authors independently screened the results of the electronic searches, extracted data and assessed the risk of bias of the included studies.ResultsFour studies involving a total of 1,270 patients were included. The studies were at high risk of bias. One trial was excluded from the analysis due to inconsistencies in the presented data. Restorations had a significantly higher survival rate in the rubber dam isolation group compared to the cotton roll isolation group at six months in participants receiving composite restorative treatment of non-carious cervical lesions (risk ratio (RR) 1.19, 95% confidence interval (CI) 1.04 to 1.37, very low-quality evidence). The rubber dam group had a lower risk of failure at two years in children undergoing proximal atraumatic restorative treatment in primary molars (hazard ratio (HR) 0.80, 95% CI 0.66 to 0.97, very low-quality evidence). One trial reported limited data showing that rubber dam usage during fissure sealing might shorten the treatment time. None of the included studies mentioned adverse effects or reported the direct cost of the treatment, or the level of patient acceptance/satisfaction. There was also no evidence evaluating the effects of rubber dam usage on the quality of the restorations.ConclusionsWe found some very low-quality evidence, from single studies, suggesting that rubber dam usage in dental direct

  17. Improving lung cancer survival; time to move on

    NARCIS (Netherlands)

    M.E. Heuvers (Marlies); J.P.J.J. Hegmans (Joost); B.H.Ch. Stricker (Bruno); J.G.J.V. Aerts (Joachim)

    2012-01-01

    textabstractBackground: During the past decades, numerous efforts have been made to decrease the death rate among lung cancer patients. Nonetheless, the improvement in long-term survival has been limited and lung cancer is still a devastating disease.Discussion: With this article we would like to

  18. Time-Dependent Tree-Structured Survival Analysis with Unbiased Variable Selection through Permutation Tests

    Science.gov (United States)

    Wallace, M. L.

    2014-01-01

    Incorporating time-dependent covariates into tree-structured survival analysis (TSSA) may result in more accurate prognostic models than if only baseline values are used. Available time-dependent TSSA methods exhaustively test every binary split on every covariate; however, this approach may result in selection bias towards covariates with more observed values. We present a method that uses unbiased significance levels from newly proposed permutation tests to select the time-dependent or baseline covariate with the strongest relationship with the survival outcome. The specific splitting value is identified using only the selected covariate. Simulation results show that the proposed time-dependent TSSA method produces tree models of equal or greater accuracy as compared to baseline TSSA models, even with high censoring rates and large within-subject variability in the time-dependent covariate. To illustrate, the proposed method is applied to data from a cohort of bipolar youth to identify subgroups at risk for self-injurious behavior. PMID:25043382

  19. Modeling receptor kinetics in the analysis of survival data for organophosphorus pesticides.

    NARCIS (Netherlands)

    Jager, D.T.; Kooijman, S.A.L.M.

    2005-01-01

    Acute ecotoxicological tests usually focus on survival at a standardized exposure time. However, LC50's decrease in time in a manner that depends both on the chemical and on the organism. DEBtox is an existing approach to analyze toxicity data in time, based on hazard modeling (the internal

  20. Semiparametric Methods to Contrast Gap Time Survival Functions: Application to Repeat Kidney Transplantation

    OpenAIRE

    Shu, Xu; Schaubel, Douglas E.

    2015-01-01

    Times between successive events (i.e., gap times) are of great importance in survival analysis. Although many methods exist for estimating covariate effects on gap times, very few existing methods allow for comparisons between gap times themselves. Motivated by the comparison of primary and repeat transplantation, our interest is specifically in contrasting the gap time survival functions and their integration (restricted mean gap time). Two major challenges in gap time analysis are non-ident...

  1. Survey Article: Ukraine's Industrial Enterprise: Surviving Hard Times

    OpenAIRE

    Fyodor I Kushnirsky

    1994-01-01

    Ukraine's industrial organizational structure, retaining the features of a planned system, has been slow to change. The state plays a greater role in controlling industrial production there than in Russia. Along with privatization, the government encourages different types of associations and conglomerates. Industry's dismal performance could probably be worse without surviving strategies used by enterprise management, such as retaining working collectives, loyalty to suppliers and buyers, av...

  2. 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.

  3. A prognostic model for lung adenocarcinoma patient survival with a focus on four miRNAs.

    Science.gov (United States)

    Li, Xianqiu; An, Zhaoling; Li, Peihui; Liu, Haihua

    2017-09-01

    There is currently no effective biomarker for determining the survival of patients with lung adenocarcinoma. The purpose of the present study was to construct a prognostic survival model using microRNA (miRNA) expression data from patients with lung adenocarcinoma. miRNA data were obtained from The Cancer Genome Atlas, and patients with lung adenocarcinoma were divided into either the training or validation set based on the random allocation principle. The prognostic model focusing on miRNA was constructed, and patients were divided into high-risk or low-risk groups as per the scores, to assess their survival time. The 5-year survival rate from the subgroups within the high- and low-risk groups was assessed. P-values of the prognostic model in the total population, the training set and validation set were 0.0017, 0.01986 and 0.02773, respectively, indicating that the survival time of the lung adenocarcinoma high-risk group was less than that of the low-risk group. Thus, the model had a good assessment effectiveness for the untreated group (P=0.00088) and the Caucasian patient group (P=0.00043). In addition, the model had the best prediction effect for the 5-year survival rate of the Caucasian patient group (AUC=0.629). In conclusion, the prognostic model developed in the present study can be used as an independent prognostic model for patients with lung adenocarcinoma.

  4. Investing to Survive in a Duopoly Model

    Science.gov (United States)

    Pinto, Alberto A.; Oliveira, Bruno M. P. M.; Ferreira, Fernanda A.; Ferreira, Miguel

    We present deterministic dynamics on the production costs of Cournot competitions, based on perfect Nash equilibria of nonlinear R&D investment strategies to reduce the production costs of the firms at every period of the game. We analyse the effects that the R&D investment strategies can have in the profits of the firms along the time. We show that small changes in the initial production costs or small changes in the parameters that determine the efficiency of the R&D programs or of the firms can produce strong economic effects in the long run of the profits of the firms.

  5. 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.

  6. Estimation of Survival Probabilities for Use in Cost-effectiveness Analyses: A Comparison of a Multi-state Modeling Survival Analysis Approach with Partitioned Survival and Markov Decision-Analytic Modeling.

    Science.gov (United States)

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

    2017-05-01

    Modeling of clinical-effectiveness in a cost-effectiveness analysis typically involves some form of partitioned survival or Markov decision-analytic modeling. The health states progression-free, progression and death and the transitions between them are frequently of interest. With partitioned survival, progression is not modeled directly as a state; instead, time in that state is derived from the difference in area between the overall survival and the progression-free survival curves. With Markov decision-analytic modeling, a priori assumptions are often made with regard to the transitions rather than using the individual patient data directly to model them. This article compares a multi-state modeling survival regression approach to these two common methods. As a case study, we use a trial comparing rituximab in combination with fludarabine and cyclophosphamide v. fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia. We calculated mean Life Years and QALYs that involved extrapolation of survival outcomes in the trial. We adapted an existing multi-state modeling approach to incorporate parametric distributions for transition hazards, to allow extrapolation. The comparison showed that, due to the different assumptions used in the different approaches, a discrepancy in results was evident. The partitioned survival and Markov decision-analytic modeling deemed the treatment cost-effective with ICERs of just over £16,000 and £13,000, respectively. However, the results with the multi-state modeling were less conclusive, with an ICER of just over £29,000. This work has illustrated that it is imperative to check whether assumptions are realistic, as different model choices can influence clinical and cost-effectiveness results.

  7. 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 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.

  8. The log-Burr XII regression model for grouped survival data.

    Science.gov (United States)

    Hashimoto, Elizabeth M; Ortega, Edwin M M; Cordeiro, Gauss M; Barreto, Mauricio L

    2012-01-01

    The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.

  9. 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

  10. Up-to-date and precise estimates of cancer patient survival: model-based period analysis.

    Science.gov (United States)

    Brenner, Hermann; Hakulinen, Timo

    2006-10-01

    Monitoring of progress in cancer patient survival by cancer registries should be as up-to-date as possible. Period analysis has been shown to provide more up-to-date survival estimates than do traditional methods of survival analysis. However, there is a trade-off between up-to-dateness and the precision of period estimates, in that increasing the up-to-dateness of survival estimates by restricting the analysis to a relatively short, recent time period, such as the most recent calendar year for which cancer registry data are available, goes along with a loss of precision. The authors propose a model-based approach to maximize the up-to-dateness of period estimates at minimal loss of precision. The approach is illustrated for monitoring of 5-year relative survival of patients diagnosed with one of 20 common forms of cancer in Finland between 1953 and 2002 by use of data from the nationwide Finnish Cancer Registry. It is shown that the model-based approach provides survival estimates that are as up-to-date as the most up-to-date conventional period estimates and at the same time much more precise than the latter. The modeling approach may further enhance the use of period analysis for deriving up-to-date cancer survival rates.

  11. Application of Cox and Parametric Survival Models to Assess Social Determinants of Health Affecting Three-Year Survival of Breast Cancer Patients.

    Science.gov (United States)

    Mohseny, Maryam; Amanpour, Farzaneh; Mosavi-Jarrahi, Alireza; Jafari, Hossein; Moradi-Joo, Mohammad; Davoudi Monfared, Esmat

    2016-01-01

    Breast cancer is one of the most common causes of cancer mortality in Iran. Social determinants of health are among the key factors affecting the pathogenesis of diseases. This cross-sectional study aimed to determine the social determinants of breast cancer survival time with parametric and semi-parametric regression models. It was conducted on male and female patients diagnosed with breast cancer presenting to the Cancer Research Center of Shohada-E-Tajrish Hospital from 2006 to 2010. The Cox proportional hazard model and parametric models including the Weibull, log normal and log-logistic models were applied to determine the social determinants of survival time of breast cancer patients. The Akaike information criterion (AIC) was used to assess the best fit. Statistical analysis was performed with STATA (version 11) software. This study was performed on 797 breast cancer patients, aged 25-93 years with a mean age of 54.7 (±11.9) years. In both semi-parametric and parametric models, the three-year survival was related to level of education and municipal district of residence (P<0.05). The AIC suggested that log normal distribution was the best fit for the three-year survival time of breast cancer patients. Social determinants of health such as level of education and municipal district of residence affect the survival of breast cancer cases. Future studies must focus on the effect of childhood social class on the survival times of cancers, which have hitherto only been paid limited attention.

  12. The effect of time to defibrillation and targeted temperature management on functional survival after out-of-hospital cardiac arrest.

    Science.gov (United States)

    Drennan, Ian R; Lin, Steve; Thorpe, Kevin E; Morrison, Laurie J

    2014-11-01

    Cardiac arrest physiology has been proposed to occur in three distinct phases: electrical, circulatory and metabolic. There is limited research evaluating the relationship of the 3-phase model of cardiac arrest to functional survival at hospital discharge. Furthermore, the effect of post-cardiac arrest targeted temperature management (TTM) on functional survival during each phase is unknown. To determine the effect of TTM on the relationship between the time of initial defibrillation during each phase of cardiac arrest and functional survival at hospital discharge. This was a retrospective observational study of consecutive adult (≥18 years) out-of-hospital cardiac arrest (OHCA) patients with initial shockable rhythms. Included patients obtained a return of spontaneous circulation (ROSC) and were eligible for TTM. Multivariable logistic regression was used to determine predictors of functional survival at hospital discharge. There were 20,165 OHCA treated by EMS and 871 patients were eligible for TTM. Of these patients, 622 (71.4%) survived to hospital discharge and 487 (55.9%) had good functional survival. Good functional survival was associated with younger age (OR 0.94; 95% CI 0.93-0.95), shorter times from collapse to initial defibrillation (OR 0.73; 95% CI 0.65-0.82), and use of post-cardiac arrest TTM (OR 1.49; 95% CI 1.07-2.30). Functional survival decreased during each phase of the model (65.3% vs. 61.7% vs. 50.2%, Pdefibrillation and was decreased during each successive phase of the 3-phase model. Post-cardiac arrest TTM was associated with improved functional survival. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  13. Modelling urban travel times

    NARCIS (Netherlands)

    Zheng, F.

    2011-01-01

    Urban travel times are intrinsically uncertain due to a lot of stochastic characteristics of traffic, especially at signalized intersections. A single travel time does not have much meaning and is not informative to drivers or traffic managers. The range of travel times is large such that certain

  14. Infinite mixture-of-experts model for sparse survival regression with application to breast cancer

    Directory of Open Access Journals (Sweden)

    Dahl Edgar

    2010-10-01

    Full Text Available Abstract Background We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox’s proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso. Results Simulated examples justify the need of such an elaborate framework for identifying sub-groups along with their key characteristics versus other simpler models. When applied to a breast-cancer dataset consisting of survival times and protein expression levels of patients, it results in identifying two distinct sub-groups with different survival patterns (low-risk and high-risk along with the respective sets of compound markers. Conclusions The unified framework presented here, combining elements of cluster and feature detection for survival analysis, is clearly a powerful tool for analyzing survival patterns within a patient group. The model also demonstrates the feasibility of analyzing complex interactions which can contribute to definition of novel prognostic compound markers.

  15. Infinite mixture-of-experts model for sparse survival regression with application to breast cancer

    Science.gov (United States)

    2010-01-01

    Background We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox’s proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso. Results Simulated examples justify the need of such an elaborate framework for identifying sub-groups along with their key characteristics versus other simpler models. When applied to a breast-cancer dataset consisting of survival times and protein expression levels of patients, it results in identifying two distinct sub-groups with different survival patterns (low-risk and high-risk) along with the respective sets of compound markers. Conclusions The unified framework presented here, combining elements of cluster and feature detection for survival analysis, is clearly a powerful tool for analyzing survival patterns within a patient group. The model also demonstrates the feasibility of analyzing complex interactions which can contribute to definition of novel prognostic compound markers. PMID:21034433

  16. Functional status, time to transplantation, and survival benefit of kidney transplantation among wait-listed candidates.

    Science.gov (United States)

    Reese, Peter P; Shults, Justine; Bloom, Roy D; Mussell, Adam; Harhay, Meera N; Abt, Peter; Levine, Matthew; Johansen, Kirsten L; Karlawish, Jason T; Feldman, Harold I

    2015-11-01

    In the context of an aging end-stage renal disease population with multiple comorbid conditions, transplantation professionals face challenges in evaluating the global health of patients awaiting kidney transplantation. Functional status might be useful for identifying which patients will derive a survival benefit from transplantation versus dialysis. Retrospective cohort study of wait-listed patients using data for functional status from a national dialysis provider linked to United Network for Organ Sharing registry data. Adult kidney transplantation candidates added to the waiting list between 2000 and 2006. Physical Functioning scale of the Medical Outcomes Study 36-Item Short Form Health Survey, analyzed as a time-varying covariate. Kidney transplantation; survival benefit of transplantation versus remaining wait-listed. We used multivariable Cox regression to assess the association between physical function with study outcomes. In survival benefit analyses, transplantation status was modeled as a time-varying covariate. The cohort comprised 19,242 kidney transplantation candidates (median age, 51 years; 36% black race) receiving maintenance dialysis. Candidates in the lowest baseline Physical Functioning score quartile were more likely to be inactivated (adjusted HR vs highest quartile, 1.30; 95% CI, 1.21-1.39) and less likely to undergo transplantation (adjusted HR vs highest quartile, 0.64; 95% CI, 0.61-0.68). After transplantation, worse Physical Functioning score was associated with shorter 3-year survival (84% vs 92% for the lowest vs highest function quartiles). However, compared to dialysis, transplantation was associated with a statistically significant survival benefit by 9 months for patients in every function quartile. Functional status is self-reported. Even patients with low function appear to live longer with kidney transplantation versus dialysis. For wait-listed patients, global health measures such as functional status may be more useful in

  17. A simple prognostic model for overall survival in metastatic renal cell carcinoma

    Science.gov (United States)

    Assi, Hazem I.; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

    2016-01-01

    Introduction: The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. Methods: We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. Results: There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. Conclusions: In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis. PMID:27217858

  18. In-season retail sales forecasting using survival models | Hattingh ...

    African Journals Online (AJOL)

    In order to identify products that should be marked down, the Retailer forecasts future sales of new products. With the aim of improving on the Retailer's current sales forecasting method, this study investigates statistical techniques, viz. classical time series analysis (Holt's smoothing method) and survival analysis. Forecasts ...

  19. 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.

  20. ESTIMATION OF SURVIVAL FUNCTION BASED ON MODELING OF CENSORING PATTERN

    OpenAIRE

    Akio, Suzukawa; Nobuhiro, Taneichi; Department of Animal Production and Agricultural Economics, Obihiro University

    2000-01-01

    The Kaplan-Meier estimator(KM-estimator)is an important tool in the analysis of right censored data. It is a non-parametric estimator of an unknown survival function of a lifetime random variable. The purpose of this paper is to obtain a semi-parametric estimator of the survival function. In many practical data, there are several patterns of censoring, for example, censoring is apt to occur for the larger observable time. Such a pattern can be expressed by a function defined by conditional pr...

  1. The forecast of the postoperative survival time of patients suffered from non-small cell lung cancer based on PCA and extreme learning machine.

    Science.gov (United States)

    Han, Fei; Huang, De-Shuang; Zhu, Zhi-Hua; Rong, Tie-Hua

    2006-02-01

    In this paper, a new effective model is proposed to forecast how long the postoperative patients suffered from non-small cell lung cancer will survive. The new effective model which is based on the extreme learning machine (ELM) and principal component analysis (PCA) can forecast successfully the postoperative patients' survival time. The new model obtains better prediction accuracy and faster convergence rate which the model using backpropagation (BP) algorithm and the Levenberg-Marquardt (LM) algorithm to forecast the postoperative patients' survival time can not achieve. Finally, simulation results are given to verify the efficiency and effectiveness of our proposed new model.

  2. Surviving in a metastable de Sitter space-time

    Energy Technology Data Exchange (ETDEWEB)

    Kashyap, Sitender Pratap; Mondal, Swapnamay [Harish-Chandra Research Institute,Chhatnag Road, Jhusi, Allahabad 211019 (India); Sen, Ashoke [Harish-Chandra Research Institute,Chhatnag Road, Jhusi, Allahabad 211019 (India); School of Physics, Korea Institute for Advanced Study,Seoul 130-722 (Korea, Republic of); Verma, Mritunjay [Harish-Chandra Research Institute,Chhatnag Road, Jhusi, Allahabad 211019 (India); International Centre for Theoretical Sciences,Malleshwaram, Bengaluru 560 012 (India)

    2015-09-21

    In a metastable de Sitter space any object has a finite life expectancy beyond which it undergoes vacuum decay. However, by spreading into different parts of the universe which will fall out of causal contact of each other in future, a civilization can increase its collective life expectancy, defined as the average time after which the last settlement disappears due to vacuum decay. We study in detail the collective life expectancy of two comoving objects in de Sitter space as a function of the initial separation, the horizon radius and the vacuum decay rate. We find that even with a modest initial separation, the collective life expectancy can reach a value close to the maximum possible value of 1.5 times that of the individual object if the decay rate is less than 1% of the expansion rate. Our analysis can be generalized to any number of objects, general trajectories not necessarily at rest in the comoving coordinates and general FRW space-time. As part of our analysis we find that in the current state of the universe dominated by matter and cosmological constant, the vacuum decay rate is increasing as a function of time due to accelerated expansion of the volume of the past light cone. Present decay rate is about 3.7 times larger than the average decay rate in the past and the final decay rate in the cosmological constant dominated epoch will be about 56 times larger than the average decay rate in the past. This considerably weakens the lower bound on the half-life of our universe based on its current age.

  3. 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

  4. 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 ...

  5. 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...

  6. Mixed Hitting-Time Models

    NARCIS (Netherlands)

    Abbring, J.H.

    2009-01-01

    We study mixed hitting-time models, which specify durations as the first time a Levy process (a continuous-time process with stationary and independent increments) crosses a heterogeneous threshold. Such models of substantial interest because they can be reduced from optimal-stopping models with

  7. Mixed hitting-time models

    NARCIS (Netherlands)

    Abbring, J.H.

    2012-01-01

    We study mixed hitting-time models that specify durations as the first time a Lévy process—a continuous-time process with stationary and independent increments—crosses a heterogeneous threshold. Such models are of substantial interest because they can be deduced from optimal-stopping models with

  8. 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.

  9. Decompression Sickness After Air Break in Prebreathe Described with a Survival Model

    Science.gov (United States)

    Conkin, J.; Pilmanis, A. A.

    2010-01-01

    Data from Brooks City-Base show the decompression sickness (DCS) and venous gas emboli (VGE) consequences of air breaks in a resting 100% O2 prebreathe (PB) prior to a hypobaric exposure. METHODS: DCS and VGE survival times from 95 controls for a 60 min PB prior to 2-hr or 4-hr exposures to 4.37 psia are statistically compared to 3 break in PB conditions: a 10 min (n=40), 20 min (n=40), or 60 min break (n=32) 30 min into the PB followed by 30 min of PB. Ascent rate was 1,524 meters / min and all exposures included light exercise and 4 min of VGE monitoring of heart chambers at 16 min intervals. DCS survival time for combined control and air breaks were described with an accelerated log logistic model where exponential N2 washin during air break was described with a 10 min half-time and washout during PB with a 60 min half-time. RESULTS: There was no difference in VGE or DCS survival times among 3 different air breaks, or when air breaks were compared to control VGE times. However, 10, 20, and 60 min air breaks had significantly earlier survival times compared to control DCS times, certainly early in the exposures. CONCLUSION: Air breaks of 10, 20, and 60 min after 30 min of a 60 min PB reduced DCS survival time. The survival model combined discrete comparisons into a global description mechanistically linked to asymmetrical N2 washin and washout kinetics based on inspired pN2. Our unvalidated regression is used to compute additional PB time needed to compensate for an air break in PB within the range of tested conditions.

  10. Bayesian analysis of right censored survival time data | Abiodun ...

    African Journals Online (AJOL)

    We analyzed cancer data using Fully Bayesian inference approach based on Markov Chain Monte Carlo (MCMC) simulation technique which allows the estimation of very complex and realistic models. The results show that sex and age are significant risk factors for dying from some selected cancers. The risk of dying from ...

  11. Improving lung cancer survival: Time to move on

    NARCIS (Netherlands)

    M.E. Heuvers (Marlies)

    2013-01-01

    markdownabstract__Abstract__ In 1761, lung cancer was first described as a distinct disease based on autopsies by Giovanni Morgagni. In 1810, Gaspard Laurent Bayle described lung cancer in more detail in his book entitled Recherches sur la phthisie pulmonaire. At that time it was an extremely

  12. 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…

  13. Cure fraction estimation from the mixture cure models for grouped survival data.

    Science.gov (United States)

    Yu, Binbing; Tiwari, Ram C; Cronin, Kathleen A; Feuer, Eric J

    2004-06-15

    Mixture cure models are usually used to model failure time data with long-term survivors. These models have been applied to grouped survival data. The models provide simultaneous estimates of the proportion of the patients cured from disease and the distribution of the survival times for uncured patients (latency distribution). However, a crucial issue with mixture cure models is the identifiability of the cure fraction and parameters of kernel distribution. Cure fraction estimates can be quite sensitive to the choice of latency distributions and length of follow-up time. In this paper, sensitivity of parameter estimates under semi-parametric model and several most commonly used parametric models, namely lognormal, loglogistic, Weibull and generalized Gamma distributions, is explored. The cure fraction estimates from the model with generalized Gamma distribution is found to be quite robust. A simulation study was carried out to examine the effect of follow-up time and latency distribution specification on cure fraction estimation. The cure models with generalized Gamma latency distribution are applied to the population-based survival data for several cancer sites from the Surveillance, Epidemiology and End Results (SEER) Program. Several cautions on the general use of cure model are advised. Copyright 2004 John Wiley & Sons, Ltd.

  14. Modeling longitudinal data with nonparametric multiplicative random effects jointly with survival data.

    Science.gov (United States)

    Ding, Jimin; Wang, Jane-Ling

    2008-06-01

    In clinical studies, longitudinal biomarkers are often used to monitor disease progression and failure time. Joint modeling of longitudinal and survival data has certain advantages and has emerged as an effective way to mutually enhance information. Typically, a parametric longitudinal model is assumed to facilitate the likelihood approach. However, the choice of a proper parametric model turns out to be more elusive than models for standard longitudinal studies in which no survival endpoint occurs. In this article, we propose a nonparametric multiplicative random effects model for the longitudinal process, which has many applications and leads to a flexible yet parsimonious nonparametric random effects model. A proportional hazards model is then used to link the biomarkers and event time. We use B-splines to represent the nonparametric longitudinal process, and select the number of knots and degrees based on a version of the Akaike information criterion (AIC). Unknown model parameters are estimated through maximizing the observed joint likelihood, which is iteratively maximized by the Monte Carlo Expectation Maximization (MCEM) algorithm. Due to the simplicity of the model structure, the proposed approach has good numerical stability and compares well with the competing parametric longitudinal approaches. The new approach is illustrated with primary biliary cirrhosis (PBC) data, aiming to capture nonlinear patterns of serum bilirubin time courses and their relationship with survival time of PBC patients.

  15. Radiographic response to locoregional therapy in hepatocellular carcinoma predicts patient survival times.

    Science.gov (United States)

    Memon, Khairuddin; Kulik, Laura; Lewandowski, Robert J; Wang, Edward; Riaz, Ahsun; Ryu, Robert K; Sato, Kent T; Marshall, Karen; Gupta, Ramona; Nikolaidis, Paul; Miller, Frank H; Yaghmai, Vahid; Senthilnathan, Seanthan; Baker, Talia; Gates, Vanessa L; Abecassis, Michael; Benson, Al B; Mulcahy, Mary F; Omary, Reed A; Salem, Riad

    2011-08-01

    It is not clear whether survival times of patients with hepatocellular carcinoma (HCC) are associated with their response to therapy. We analyzed the association between tumor response and survival times of patients with HCC who were treated with locoregional therapies (LRTs) (chemoembolization and radioembolization). Patients received LRTs over a 9-year period (n = 463). Patients with metastases, portal venous thrombosis, or who had received transplants were excluded; 159 patients with Child-Pugh B7 or lower were analyzed. Response (based on European Association for the Study of the Liver [EASL] and World Health Organization [WHO] criteria) was associated with survival times using the landmark, risk-of-death, and Mantel-Byar methodologies. In a subanalysis, survival times of responders were compared with those of patients with stable disease and progressive disease. Based on 6-month data, in landmark analysis, responders survived longer than nonresponders (based on EASL but not WHO criteria: P = .002 and .0694). The risk of death was also lower for responders (based on EASL but not WHO criteria: P = .0463 and .707). Landmark analysis of 12-month data showed that responders survived longer than nonresponders (P < .0001 and .004, based on EASL and WHO criteria, respectively). The risk of death was lower for responders (P = .0132 and .010, based on EASL and WHO criteria, respectively). By the Mantel-Byar method, responders had longer survival than nonresponders, based on EASL criteria (P < .0001; P = .596 with WHO criteria). In the subanalysis, responders lived longer than patients with stable disease or progressive disease. Radiographic response to LRTs predicts survival time. EASL criteria for response more consistently predicted survival times than WHO criteria. The goal of LRT should be to achieve a radiologic response, rather than to stabilize disease. Copyright © 2011 AGA Institute. Published by Elsevier Inc. All rights reserved.

  16. Semiparametric maximum likelihood estimation in normal transformation models for bivariate survival data

    OpenAIRE

    Yi Li; Ross L. Prentice; Xihong Lin

    2008-01-01

    We consider a class of semiparametric normal transformation models for right-censored bivariate failure times. Nonparametric hazard rate models are transformed to a standard normal model and a joint normal distribution is assumed for the bivariate vector of transformed variates. A semiparametric maximum likelihood estimation procedure is developed for estimating the marginal survival distribution and the pairwise correlation parameters. This produces an efficient estimator of the correlation ...

  17. Survival times for canine intranasal sarcomas treated with radiation therapy: 86 cases (1996-2011).

    Science.gov (United States)

    Sones, Evan; Smith, Annette; Schleis, Stephanie; Brawner, William; Almond, Gregory; Taylor, Kathryn; Haney, Siobhan; Wypij, Jackie; Keyerleber, Michele; Arthur, Jennifer; Hamilton, Terrance; Lawrence, Jessica; Gieger, Tracy; Sellon, Rance; Wright, Zack

    2013-01-01

    Sarcomas comprise approximately one-third of canine intranasal tumors, however few veterinary studies have described survival times of dogs with histologic subtypes of sarcomas separately from other intranasal tumors. One objective of this study was to describe median survival times for dogs treated with radiation therapy for intranasal sarcomas. A second objective was to compare survival times for dogs treated with three radiation therapy protocols: daily-fractionated radiation therapy; Monday, Wednesday, and Friday fractionated radiation therapy; and palliative radiation therapy. Medical records were retrospectively reviewed for dogs that had been treated with radiation therapy for confirmed intranasal sarcoma. A total of 86 dogs met inclusion criteria. Overall median survival time for included dogs was 444 days. Median survival time for dogs with chondrosarcoma (n = 42) was 463 days, fibrosarcoma (n = 12) 379 days, osteosarcoma (n = 6) 624 days, and undifferentiated sarcoma (n = 22) 344 days. Dogs treated with daily-fractionated radiation therapy protocols; Monday, Wednesday and Friday fractionated radiation therapy protocols; and palliative radiation therapy protocols had median survival times of 641, 347, and 305 days, respectively. A significant difference in survival time was found for dogs receiving curative intent radiation therapy vs. palliative radiation therapy (P = 0.032). A significant difference in survival time was also found for dogs receiving daily-fractionated radiation therapy vs. Monday, Wednesday and Friday fractionated radiation therapy (P = 0.0134). Findings from this study support the use of curative intent radiation therapy for dogs with intranasal sarcoma. Future prospective, randomized trials are needed for confirmation of treatment benefits. © 2012 Veterinary Radiology & Ultrasound.

  18. 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.

  19. Mixed Hitting-Time Models

    NARCIS (Netherlands)

    Abbring, J.H.

    2007-01-01

    We study a mixed hitting-time (MHT) model that specifies durations as the first time a Lévy process - a continuous-time process with stationary and independent increments� crosses a heterogeneous threshold. Such models are of substantial interest because they can be reduced from optimal-stopping

  20. Extensions and applications of the Cox-Aalen survival model.

    Science.gov (United States)

    Scheike, Thomas H; Zhang, Mei-Jie

    2003-12-01

    Cox's regression model is the standard regression tool for survival analysis in most applications. Often, however, the model only provides a rough summary of the effect of some covariates. Therefore, if the aim is to give a detailed description of covariate effects and to consequently calculate predicted probabilities, more flexible models are needed. In another article, Scheike and Zhang (2002, Scandinavian Journal of Statistics 29, 75-88), we suggested a flexible extension of Cox's regression model, which aimed at extending the Cox model only for those covariates where additional flexibility are needed. One important advantage of the suggested approach is that even though covariates are allowed a nonparametric effect, the hassle and difficulty of finding smoothing parameters are not needed. We show how the extended model also leads to simple formulae for predicted probabilities and their standard errors, for example, in the competing risk framework.

  1. Modeling survival of Listeria monocytogenes in the traditional Greek soft cheese Katiki.

    Science.gov (United States)

    Mataragas, Marios; Stergiou, Virginia; Nychas, George-John E

    2008-09-01

    In the present work, survival of Listeria monocytogenes in the traditional Greek soft, spreadable cheese Katiki was studied throughout the shelf life of the product. Samples of finished cheese were inoculated with a cocktail of five L. monocytogenes strains (ca. 6 log CFU g(-1)) and stored at 5, 10, 15, and 20 degrees C. Acid-stress adaptation or cross-protection to the same stress was also investigated by inoculation of acid-adapted cells in the product. The results showed that pathogen survival was biphasic. Various mathematical equations (Geeraerd, Cerf, Albert-Mafart, Whiting, Zwietering, and Baranyi models) were fitted to the experimental data. A thorough statistical analysis was performed to choose the best model. The Geeraerd model was finally selected, and the results revealed no acid tolerance acquisition (no significant differences, P > 0.05, in the survival rates of the non-acid-adapted and acid-adapted cells). Secondary modeling (second-order polynomial with a(0) = 0.8453, a(1) = -0.0743, and a(2) = 0.0059) of the survival rate (of sensitive population), and other parameters that were similar at all temperatures (fraction of initial population in the major population = 99.98%, survival rate of resistant population = 0.10 day(-1), and initial population = 6.29 log CFU g(-1)), showed that survival of the pathogen was temperature dependent with bacterial cells surviving for a longer period of time at lower temperatures. Finally, the developed predictive model was successfully validated at two independent temperatures (12 and 17 degrees C). This study underlines the usefulness of predictive modeling as a tool for realistic estimation and control of L. monocytogenes risk in food products. Such data are also useful when conducting risk assessment studies.

  2. Hidden Markov model for dependent mark loss and survival estimation

    Science.gov (United States)

    Laake, Jeffrey L.; Johnson, Devin S.; Diefenbach, Duane R.; Ternent, Mark A.

    2014-01-01

    Mark-recapture estimators assume no loss of marks to provide unbiased estimates of population parameters. We describe a hidden Markov model (HMM) framework that integrates a mark loss model with a Cormack–Jolly–Seber model for survival estimation. Mark loss can be estimated with single-marked animals as long as a sub-sample of animals has a permanent mark. Double-marking provides an estimate of mark loss assuming independence but dependence can be modeled with a permanently marked sub-sample. We use a log-linear approach to include covariates for mark loss and dependence which is more flexible than existing published methods for integrated models. The HMM approach is demonstrated with a dataset of black bears (Ursus americanus) with two ear tags and a subset of which were permanently marked with tattoos. The data were analyzed with and without the tattoo. Dropping the tattoos resulted in estimates of survival that were reduced by 0.005–0.035 due to tag loss dependence that could not be modeled. We also analyzed the data with and without the tattoo using a single tag. By not using.

  3. Evaluation of parametric models by the prediction error in colorectal cancer survival analysis.

    Science.gov (United States)

    Baghestani, Ahmad Reza; Gohari, Mahmood Reza; Orooji, Arezoo; Pourhoseingholi, Mohamad Amin; Zali, Mohammad Reza

    2015-01-01

    The aim of this study is to determine the factors influencing predicted survival time for patients with colorectal cancer (CRC) using parametric models and select the best model by predicting error's technique. Survival models are statistical techniques to estimate or predict the overall time up to specific events. Prediction is important in medical science and the accuracy of prediction is determined by a measurement, generally based on loss functions, called prediction error. A total of 600 colorectal cancer patients who admitted to the Cancer Registry Center of Gastroenterology and Liver Disease Research Center, Taleghani Hospital, Tehran, were followed at least for 5 years and have completed selected information for this study. Body Mass Index (BMI), Sex, family history of CRC, tumor site, stage of disease and histology of tumor included in the analysis. The survival time was compared by the Log-rank test and multivariate analysis was carried out using parametric models including Log normal, Weibull and Log logistic regression. For selecting the best model, the prediction error by apparent loss was used. Log rank test showed a better survival for females, BMI more than 25, patients with early stage at diagnosis and patients with colon tumor site. Prediction error by apparent loss was estimated and indicated that Weibull model was the best one for multivariate analysis. BMI and Stage were independent prognostic factors, according to Weibull model. In this study, according to prediction error Weibull regression showed a better fit. Prediction error would be a criterion to select the best model with the ability to make predictions of prognostic factors in survival analysis.

  4. Analysis of multilevel grouped survival data with time-varying regression coefficients.

    Science.gov (United States)

    Wong, May C M; Lam, K F; Lo, Edward C M

    2011-02-10

    Correlated or multilevel grouped survival data are common in medical and dental research. Two common approaches to analyze such data are the marginal and the random-effects approaches. Models and methods in the literature generally assume that the treatment effect is constant over time. A researcher may be interested in studying whether the treatment effects in a clinical trial vary over time, say fade out gradually. This is of particular clinical value when studying the long-term effect of a treatment. This paper proposed to extend the random effects grouped proportional hazards models by incorporating the possibly time-varying covariate effects into the model in terms of a state-space formulation. The proposed model is very flexible and the estimation can be performed using the MCMC approach with non-informative priors in the Bayesian framework. The method is applied to a data set from a prospective clinical trial investigating the effectiveness of silver diamine fluoride (SDF) and sodium fluoride (NaF) varnish in arresting active dentin caries in the Chinese preschool children. It is shown that the treatment groups with caries removal prior to the topical fluoride applications are most effective in shortening the arrest times in the first 6-month interval, but their effects fade out rapidly since then. The effects of treatment groups without caries removal prior to topical fluoride application drop at a very slow rate and can be considered as more or less constant over time. The applications of SDF solution is found to be more effective than the applications of NaF vanish. Copyright © 2010 John Wiley & Sons, Ltd.

  5. Modeling the survival kinetics of Salmonella in tree nuts for use in risk assessment.

    Science.gov (United States)

    Santillana Farakos, Sofia M; Pouillot, Régis; Anderson, Nathan; Johnson, Rhoma; Son, Insook; Van Doren, Jane

    2016-06-16

    Salmonella has been shown to survive in tree nuts over long periods of time. This survival capacity and its variability are key elements for risk assessment of Salmonella in tree nuts. The aim of this study was to develop a mathematical model to predict survival of Salmonella in tree nuts at ambient storage temperatures that considers variability and uncertainty separately and can easily be incorporated into a risk assessment model. Data on Salmonella survival on raw almonds, pecans, pistachios and walnuts were collected from the peer reviewed literature. The Weibull model was chosen as the baseline model and various fixed effect and mixed effect models were fit to the data. The best model identified through statistical analysis testing was then used to develop a hierarchical Bayesian model. Salmonella in tree nuts showed slow declines at temperatures ranging from 21°C to 24°C. A high degree of variability in survival was observed across tree nut studies reported in the literature. Statistical analysis results indicated that the best applicable model was a mixed effect model that included a fixed and random variation of δ per tree nut (which is the time it takes for the first log10 reduction) and a fixed variation of ρ per tree nut (parameter which defines the shape of the curve). Higher estimated survival rates (δ) were obtained for Salmonella on pistachios, followed in decreasing order by pecans, almonds and walnuts. The posterior distributions obtained from Bayesian inference were used to estimate the variability in the log10 decrease levels in survival for each tree nut, and the uncertainty of these estimates. These modeled uncertainty and variability distributions of the estimates can be used to obtain a complete exposure assessment of Salmonella in tree nuts when including time-temperature parameters for storage and consumption data. The statistical approach presented in this study may be applied to any studies that aim to develop predictive models to be

  6. 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

  7. Prognostic survival model for people diagnosed with invasive cutaneous melanoma.

    Science.gov (United States)

    Baade, Peter D; Royston, Patrick; Youl, Philipa H; Weinstock, Martin A; Geller, Alan; Aitken, Joanne F

    2015-01-31

    The ability of medical practitioners to communicate risk estimates effectively to patients diagnosed with melanoma relies on accurate information about prognostic factors and their impact on survival. This study reports the development of one of the few melanoma prognostic models, called the Melanoma Severity Index (MSI), based on population-based cancer registry data. Data from the Queensland Cancer Registry for people (20-89 years) diagnosed with a single invasive melanoma between 1995 and 2008 (n = 28,654; 1,700 melanoma deaths). Additional clinical information about metastasis, ulceration and positive lymph nodes was manually extracted from pathology forms. Flexible parametric survival models were combined with multivariable fractional polynomial for selecting variables and transformations of continuous variables. Multiple imputation was used for missing covariate values. The MSI contained the variables thickness (transformed, explained 40.6% of variation in survival), body site (additional 1.9% in variation), metastasis (1.8%), positive nodes (0.7%), ulceration (1.3%), age (1.1%). Royston and Sauerbrei's D statistic (measure of discrimination) was 1.50 (95% CI = 1.44, 1.56) and the corresponding RD2 (measure of explained variation) was 0.47 (0.45, 0.49), demonstrating strong explanatory performance. The Harrell-C statistic was 0.88 (0.88, 0.89). Lacking an external validation dataset, we applied internal-external cross validation to demonstrate the consistency of the prognostic information across geographically-defined subsets of the cohort. The MSI provides good ability to predict survival for melanoma patients. Beyond the immediate clinical use, the MSI may have important public health and research applications for evaluations of public health interventions aimed at reducing deaths from melanoma.

  8. Irradiation shortens the survival time of red cells deficient in glucose-6-phosphate dehydrogenasee

    Energy Technology Data Exchange (ETDEWEB)

    Westerman, M.P. (Rush Medical College, Chicago, IL); Wald, N.; Diloy-Puray, M.

    1980-03-01

    X radiation of glucose-6-phosphate dehydrogenase (G6PD)-deficient red cells causes distinct shortening of their survival time. This is accompanied by significant lowering of reduced glutathione content and is not observed in similarly prepared and treated normal cells. The damage is most likely related to irradiation-induced formation of activated oxygen products and to their subsequent effects on the cells. Neither methemoglobin increases nor Heinz body formation were observed, suggesting that hemolysis occurred prior to these changes. The study provides a model for examining the effects of irradiation and activated oxygen on red cells and suggests that patients with G6PD deficiency who receive irradiation could develop severe hemolysis in certain clinical settings.

  9. 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.

  10. Application of Survival Analysis and Multistate Modeling to Understand Animal Behavior: Examples from Guide Dogs

    Science.gov (United States)

    Asher, Lucy; Harvey, Naomi D.; Green, Martin; England, Gary C. W.

    2017-01-01

    Epidemiology is the study of patterns of health-related states or events in populations. Statistical models developed for epidemiology could be usefully applied to behavioral states or events. The aim of this study is to present the application of epidemiological statistics to understand animal behavior where discrete outcomes are of interest, using data from guide dogs to illustrate. Specifically, survival analysis and multistate modeling are applied to data on guide dogs comparing dogs that completed training and qualified as a guide dog, to those that were withdrawn from the training program. Survival analysis allows the time to (or between) a binary event(s) and the probability of the event occurring at or beyond a specified time point. Survival analysis, using a Cox proportional hazards model, was used to examine the time taken to withdraw a dog from training. Sex, breed, and other factors affected time to withdrawal. Bitches were withdrawn faster than dogs, Labradors were withdrawn faster, and Labrador × Golden Retrievers slower, than Golden Retriever × Labradors; and dogs not bred by Guide Dogs were withdrawn faster than those bred by Guide Dogs. Multistate modeling (MSM) can be used as an extension of survival analysis to incorporate more than two discrete events or states. Multistate models were used to investigate transitions between states of training to qualification as a guide dog or behavioral withdrawal, and from qualification as a guide dog to behavioral withdrawal. Sex, breed (with purebred Labradors and Golden retrievers differing from F1 crosses), and bred by Guide Dogs or not, effected movements between states. We postulate that survival analysis and MSM could be applied to a wide range of behavioral data and key examples are provided. PMID:28804710

  11. Application of Survival Analysis and Multistate Modeling to Understand Animal Behavior: Examples from Guide Dogs.

    Science.gov (United States)

    Asher, Lucy; Harvey, Naomi D; Green, Martin; England, Gary C W

    2017-01-01

    Epidemiology is the study of patterns of health-related states or events in populations. Statistical models developed for epidemiology could be usefully applied to behavioral states or events. The aim of this study is to present the application of epidemiological statistics to understand animal behavior where discrete outcomes are of interest, using data from guide dogs to illustrate. Specifically, survival analysis and multistate modeling are applied to data on guide dogs comparing dogs that completed training and qualified as a guide dog, to those that were withdrawn from the training program. Survival analysis allows the time to (or between) a binary event(s) and the probability of the event occurring at or beyond a specified time point. Survival analysis, using a Cox proportional hazards model, was used to examine the time taken to withdraw a dog from training. Sex, breed, and other factors affected time to withdrawal. Bitches were withdrawn faster than dogs, Labradors were withdrawn faster, and Labrador × Golden Retrievers slower, than Golden Retriever × Labradors; and dogs not bred by Guide Dogs were withdrawn faster than those bred by Guide Dogs. Multistate modeling (MSM) can be used as an extension of survival analysis to incorporate more than two discrete events or states. Multistate models were used to investigate transitions between states of training to qualification as a guide dog or behavioral withdrawal, and from qualification as a guide dog to behavioral withdrawal. Sex, breed (with purebred Labradors and Golden retrievers differing from F1 crosses), and bred by Guide Dogs or not, effected movements between states. We postulate that survival analysis and MSM could be applied to a wide range of behavioral data and key examples are provided.

  12. Application of Survival Analysis and Multistate Modeling to Understand Animal Behavior: Examples from Guide Dogs

    Directory of Open Access Journals (Sweden)

    Lucy Asher

    2017-07-01

    Full Text Available Epidemiology is the study of patterns of health-related states or events in populations. Statistical models developed for epidemiology could be usefully applied to behavioral states or events. The aim of this study is to present the application of epidemiological statistics to understand animal behavior where discrete outcomes are of interest, using data from guide dogs to illustrate. Specifically, survival analysis and multistate modeling are applied to data on guide dogs comparing dogs that completed training and qualified as a guide dog, to those that were withdrawn from the training program. Survival analysis allows the time to (or between a binary event(s and the probability of the event occurring at or beyond a specified time point. Survival analysis, using a Cox proportional hazards model, was used to examine the time taken to withdraw a dog from training. Sex, breed, and other factors affected time to withdrawal. Bitches were withdrawn faster than dogs, Labradors were withdrawn faster, and Labrador × Golden Retrievers slower, than Golden Retriever × Labradors; and dogs not bred by Guide Dogs were withdrawn faster than those bred by Guide Dogs. Multistate modeling (MSM can be used as an extension of survival analysis to incorporate more than two discrete events or states. Multistate models were used to investigate transitions between states of training to qualification as a guide dog or behavioral withdrawal, and from qualification as a guide dog to behavioral withdrawal. Sex, breed (with purebred Labradors and Golden retrievers differing from F1 crosses, and bred by Guide Dogs or not, effected movements between states. We postulate that survival analysis and MSM could be applied to a wide range of behavioral data and key examples are provided.

  13. Time-Varying Effects of Prognostic Factors Associated With Disease-Free Survival in Breast Cancer

    Science.gov (United States)

    Natarajan, Loki; Pu, Minya; Parker, Barbara A.; Thomson, Cynthia A.; Caan, Bette J.; Flatt, Shirley W.; Madlensky, Lisa; Hajek, Richard A.; Al-Delaimy, Wael K.; Saquib, Nazmus; Gold, Ellen B.

    2009-01-01

    Early detection and effective treatments have dramatically improved breast cancer survivorship, yet the risk of relapse persists even 15 years after the initial diagnosis. It is important to identify prognostic factors for late breast cancer events. The authors investigated time-varying effects of tumor characteristics on breast-cancer-free survival using data on 3,088 breast cancer survivors from 4 US states who participated in a randomized dietary intervention trial in 1995–2006, with maximum follow-up through 15 years (median, 9 years). A piecewise constant penalized spline approach incorporating time-varying coefficients was adopted, allowing for deviations from the proportional hazards assumption. This method is more flexible than standard approaches, provides direct estimates of hazard ratios across time intervals, and is computationally tractable. Having a stage II or III tumor was associated with a 3-fold higher hazard of breast cancer than having a stage I tumor during the first 2.5 years after diagnosis; this hazard ratio decreased to 2.1 after 7.7 years, but higher tumor stage remained a significant risk factor. Similar diminishing effects were found for poorly differentiated tumors. Interestingly, having a positive estrogen receptor status was protective up to 4 years after diagnosis but detrimental after 7.7 years (hazard ratio = 1.5). These results emphasize the importance of careful statistical modeling allowing for possibly time-dependent effects in long-term survivorship studies. PMID:19403844

  14. Survival of Patients on Hemodialysis and Predictors of Mortality: a Single-Centre Analysis of Time-Dependent Factors.

    Science.gov (United States)

    Ossareh, Shahrzad; Farrokhi, Farhat; Zebarjadi, Marjan

    2016-11-01

    This study aimed to evaluate the outcome and predictors of survival in hemodialysis patients of Hasheminejad Kidney Center where a comprehensive dialysis care program has been placed since 2004. Data of 560 hemodialysis patients were used to evaluate 9-year survival rates and predictors of mortality. Cox regression models included comorbidities as well as averaged and 6-month-averaged time-dependent values of laboratory findings as independent factors. Survival rates were 91.9%, 66.0%, 46.3%, and 28.5%,  at 1, 3, 5, and 9 years, respectively, in all patients and 90.8%, 61.6%, 42.1%, and 28.0% in 395 incident patients starting hemodialysis after 2004. Adjusted survival models demonstrated age, male sex, diabetes mellitus, cardiovascular disease, and high-risk vascular access as baseline predictors of mortality, as well as averaged low hemoglobin level (hazard ratio [HR], 1.98; 95% confidence interval [CI], 1.36 to 2.90) and a single-pool KT/V patients have relatively comparable survival rates with high-profile dialysis centers. Aiming to better achieve the recommended targets, especially hemoglobin and nutritional and bone metabolism factors, should be considered for optimal dialysis outcomes.

  15. Modelling human myoblasts survival upon xenotransplantation into immunodeficient mouse muscle.

    Science.gov (United States)

    Praud, Christophe; Vauchez, Karine; Zongo, Pascal; Vilquin, Jean-Thomas

    2018-03-15

    Cell transplantation has been challenged in several clinical indications of genetic or acquired muscular diseases, but therapeutic success were mitigated. To understand and improve the yields of tissue regeneration, we aimed at modelling the fate of CD56-positive human myoblasts after transplantation. Using immunodeficient severe combined immunodeficiency (SCID) mice as recipients, we assessed the survival, integration and satellite cell niche occupancy of human myoblasts by a triple immunohistochemical labelling of laminin, dystrophin and human lamin A/C. The counts were integrated into a classical mathematical decline equation. After injection, human cells were essentially located in the endomysium, then they disappeared progressively from D0 to D28. The final number of integrated human nuclei was grossly determined at D2 after injection, suggesting that no more efficient fusion between donor myoblasts and host fibers occurs after the resolution of the local damages created by needle insertion. Almost 1% of implanted human cells occupied a satellite-like cell niche. Our mathematical model validated by histological counting provided a reliable quantitative estimate of human myoblast survival and/or incorporation into SCID muscle fibers. Informations brought by histological labelling and this mathematical model are complementary. Copyright © 2018 Elsevier Inc. All rights reserved.

  16. Correlation between Pulmonary Function Indexes and Survival Time 
in Patients with Advanced Lung Cancer

    Directory of Open Access Journals (Sweden)

    Hui GE

    2013-07-01

    Full Text Available Background and objective To those patients with advanced lung cancer, the ultimate objective is to improve the curative effect and quality of life, lung function indexes are an important factor. We investigate the change of lung function and the relationship between pulmonary function indexs and survival time in patients with advanced lung cancer. Methods Lung function was detected in 59 cases with lung cancer and 63 normal controls. The relationship between pulmonary function indexs and survival time was analyzed. Results There was significant difference in ventilation function and diffusing capacity between in lung cancer group and control group. Vital capacity (VC, forced expiratory volume in one second (FEV1, forced vital capacity (FVC, peak expiratory flow (PEF, peak expiratory flow% (PEF%, maximal ventilatory volume (MVV were positively correlated with survival time in patients with advanced lung cancer (r=0.29, 0.28, 0.28, 0.27, 0.26, 0.28, P<0.05, residual volume/total lung capacity was negatively correlated with survival time (r=-0.31, P<0.05. Conclusion The lung function decreases in the patients with lung cancer. VC, FEV1, FVC, PEF, PEF%, MVV, residual volume/total lung capacity were correlated with survival time in patients with advanced lung cancer. The pulmonary function indexs were important marker of prognosis in patients with lung cancer.

  17. Analysis of individual- and time-specific covariate effects on survival of Serinus serinus in north-eastern Spain

    Science.gov (United States)

    Conroy, M.J.; Senar, J.C.; Domenech, J.

    2002-01-01

    We developed models for the analysis of recapture data for 2678 serins (Serinus serinus) ringed in north-eastern Spain since 1985. We investigated several time- and individual-specific factors as potential predictors of overall mortality and dispersal patterns, and of gender and age differences in these patterns. Time-specific covariates included minimum daily temperature, days below freezing, and abundance of a strong competitor, siskins (Carduelis spinus) during winter, and maximum temperature and rainfall during summer. Individual covariates included body mass (i.e. body condition), and wing length (i.e. flying ability), and interactions between body mass and environmental factors. We found little support of a predictive relationship between environmental factors and survival, but good evidence of relationships between body mass and survival, especially for juveniles. Juvenile survival appears to vary in a curvilinear manner with increasing mass, suggesting that there may exist an optimal mass beyond which increases are detrimental. The mass-survival relationship does seem to be influenced by at least one environmental factor, namely the abundance of wintering siskins. When siskins are abundant, increases in body mass appear to relate strongly to increasing survival. When siskin numbers are average or low the relationship is largely reversed, suggesting that the presence of strong competition mitigates the otherwise largely negative aspects of greater body mass. Wing length in juveniles also appears to be related positively to survival, perhaps largely due to the influence of a few unusually large juveniles with adult-like survival. Further work is needed to test these relationships, ideally under experimentation.

  18. Survival analysis and prognostic factors of timing of first childbirth among women in Nigeria.

    Science.gov (United States)

    Fagbamigbe, Adeniyi Francis; Idemudia, Erhabor Sunday

    2016-05-13

    First childbirth in a woman's life is one of the most important events in her life. It marks a turnaround when she might have to drop roles of career building and education, for motherhood and parenthood. The timing of the commencement of these roles affects the child bearing behavior of women as they progress in their reproductive ages. Prevalent early first childbirth in Nigeria has been reported as the main cause of high population growth and high  fertility, mortality and morbidity among women, but little has been documented on the progression into first birth as well as factors affecting it in Nigeria. This paper modelled timing of first birth among women in Nigeria and determined socio-demographic and other factors affecting its timing. We hypothesized that background characteristics of a woman will influence her progression into having first birth. We developed and fitted a survival analysis model to understand the timing of first birth among women in Nigeria using a national representative 2013 NDHS data. Women with no children were right censored as of the date of the survey. The Kaplan Meier survival function was used to estimate the probabilities of first birth not occurring until certain ages of women while Cox proportional hazard regression was used to model the timing of first births at 5 % significance level. About 75.7 % of the respondents had given birth in the Northern region of Nigerian compared with 63.8 % in the South. Half (50.1 %) of the first childbirth occurred within the 15-19 years age bracket and 38.1 % within 20-29 years. The overall median survival time to first birth was 20 years (North 19, South 22), 27 years among women with higher education and 18 years for those with no formal education. The adjusted hazard of first birth was higher in the Northern region of Nigeria than in the South (aHR = 1.24, 95 % CI: 1.20-1.27), and higher in rural areas than in urban areas (aHR = 1.15, 95 % CI: 1.12-1.19). Also, hazard of earlier first

  19. Attenuation caused by infrequently updated covariates in survival analysis

    DEFF Research Database (Denmark)

    Andersen, Per Kragh; Liestøl, Knut

    2003-01-01

    Attenuation; Cox regression model; Measurement errors; Survival analysis; Time-dependent covariates......Attenuation; Cox regression model; Measurement errors; Survival analysis; Time-dependent covariates...

  20. A Systematic Review of Testicle Survival Time After a Torsion Event.

    Science.gov (United States)

    Mellick, Larry Bruce; Sinex, James E; Gibson, Robert W; Mears, Kim

    2017-09-25

    The time window for possible salvage and survival of a torsed testicle is commonly thought to be 6 to 8 hours. However, survival of torsed testicles with or without subsequent atrophy is known to occur outside that critical time window. In this article, we performed a systematic review of the English literature to provide a more accurate understanding of reported time frames of testicle survival after a torsion event. The primary research question was to determine the relationship between time to treatment and the rate of survival for testicles of male patients presenting with testicular torsion (TT). A systematic review of the literature was performed and structured according to PRISMA guidelines. An exhaustive library search was performed after search strategies were developed for multiple databases that included PubMed, Cochrane library, Ovid MEDLINE, Web of Science, and ProQuest Theses and Dissertations. Two different searches were developed including "testicular torsion" and TT with the search term "time" added. Articles specifically reporting TT case series, testicle outcomes, and time to surgical or manual treatment were selected for review. In addition to and preceding the systematic review, an exhaustive manual search of the literature was also performed by the authors. As a result of these searches, a total of 30 studies with data considered relevant to the research question were included. The information extracted from the articles was tabulated with regard to time intervals to treatment and survival outcome. The systematic review process and protocol are reported in this article. A total of 30 studies were found that reported case series of TT patients and their outcomes as well as time to treatment reported in useful time frames. From these reports, a total of 2116 TT patients were culled, and their outcomes and time to treatment are reported. Because the time to treatment was reported variously in different case series, the 3 most common formats for

  1. Effect of first cannulation time and dialysis machine blood flows on survival of arteriovenous fistulas.

    Science.gov (United States)

    Wilmink, Teun; Powers, Sarah; Hollingworth, Lee; Stevenson, Tamasin

    2017-10-16

    To study the effect of cannulation time on arteriovenous fistula (AVF) survival. Analysis of two prospective databases of access operations and dialysis sessions from 12 January 2002 through 4 January 2015 with follow-up until 4 January 2016. First cannulation time (FCT), defined from operation to first cannulation, was categorized as machine blood flow rate (BFR) for the first 29 dialysis sessions on AVF was analysed. Altogether, 1167 AVF with functional dialysis use were analysed: 667 (57%) radial cephalic AVF, 383 (33%) brachiocephalic AVF and 117 (10%) brachiobasilic AVF. The 631 (54%) AVF created in on-dialysis patients were analysed separately from 536 (46%) AVF created in pre-dialysis patients. AVF survival was similar between cannulation categories for both pre-dialysis patients (P = 0.19) and on-dialysis patients (P = 0.83). Early cannulation was associated with similar AVF survival in both pre-dialysis patients (P = 0.82) and on-dialysis patients (P = 0.17). Six consecutive successful cannulations from the start were associated with improved AVF survival (P = 0.0002). A below-median BFR at the start of dialysis was associated with better AVF survival (P machine BFR in the first week of dialysis are associated with decreased AVF survival.

  2. BAYESIAN INFERENCE OF HIDDEN GAMMA WEAR PROCESS MODEL FOR SURVIVAL DATA WITH TIES.

    Science.gov (United States)

    Sinha, Arijit; Chi, Zhiyi; Chen, Ming-Hui

    2015-10-01

    Survival data often contain tied event times. Inference without careful treatment of the ties can lead to biased estimates. This paper develops the Bayesian analysis of a stochastic wear process model to fit survival data that might have a large number of ties. Under a general wear process model, we derive the likelihood of parameters. When the wear process is a Gamma process, the likelihood has a semi-closed form that allows posterior sampling to be carried out for the parameters, hence achieving model selection using Bayesian deviance information criterion. An innovative simulation algorithm via direct forward sampling and Gibbs sampling is developed to sample event times that may have ties in the presence of arbitrary covariates; this provides a tool to assess the precision of inference. An extensive simulation study is reported and a data set is used to further illustrate the proposed methodology.

  3. Small sample bias in the gamma frailty model for univariate survival.

    Science.gov (United States)

    Barker, Peter; Henderson, Robin

    2005-06-01

    The gamma frailty model is a natural extension of the Cox proportional hazards model in survival analysis. Because the frailties are unobserved, an E-M approach is often used for estimation. Such an approach is shown to lead to finite sample underestimation of the frailty variance, with the corresponding regression parameters also being underestimated as a result. For the univariate case, we investigate the source of the bias with simulation studies and a complete enumeration. The rank-based E-M approach, we note, only identifies frailty through the order in which failures occur; additional frailty which is evident in the survival times is ignored, and as a result the frailty variance is underestimated. An adaption of the standard E-M approach is suggested, whereby the non-parametric Breslow estimate is replaced by a local likelihood formulation for the baseline hazard which allows the survival times themselves to enter the model. Simulations demonstrate that this approach substantially reduces the bias, even at small sample sizes. The method developed is applied to survival data from the North West Regional Leukaemia Register.

  4. 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.

  5. 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…

  6. Semiparametric Maximum Likelihood Estimation in Normal Transformation Models for Bivariate Survival Data

    Science.gov (United States)

    Li, Yi; Prentice, Ross L.; Lin, Xihong

    2008-01-01

    SUMMARY We consider a class of semiparametric normal transformation models for right censored bivariate failure times. Nonparametric hazard rate models are transformed to a standard normal model and a joint normal distribution is assumed for the bivariate vector of transformed variates. A semiparametric maximum likelihood estimation procedure is developed for estimating the marginal survival distribution and the pairwise correlation parameters. This produces an efficient estimator of the correlation parameter of the semiparametric normal transformation model, which characterizes the bivariate dependence of bivariate survival outcomes. In addition, a simple positive-mass-redistribution algorithm can be used to implement the estimation procedures. Since the likelihood function involves infinite-dimensional parameters, the empirical process theory is utilized to study the asymptotic properties of the proposed estimators, which are shown to be consistent, asymptotically normal and semiparametric efficient. A simple estimator for the variance of the estimates is also derived. The finite sample performance is evaluated via extensive simulations. PMID:19079778

  7. Semiparametric Maximum Likelihood Estimation in Normal Transformation Models for Bivariate Survival Data.

    Science.gov (United States)

    Li, Yi; Prentice, Ross L; Lin, Xihong

    2008-12-01

    We consider a class of semiparametric normal transformation models for right censored bivariate failure times. Nonparametric hazard rate models are transformed to a standard normal model and a joint normal distribution is assumed for the bivariate vector of transformed variates. A semiparametric maximum likelihood estimation procedure is developed for estimating the marginal survival distribution and the pairwise correlation parameters. This produces an efficient estimator of the correlation parameter of the semiparametric normal transformation model, which characterizes the bivariate dependence of bivariate survival outcomes. In addition, a simple positive-mass-redistribution algorithm can be used to implement the estimation procedures. Since the likelihood function involves infinite-dimensional parameters, the empirical process theory is utilized to study the asymptotic properties of the proposed estimators, which are shown to be consistent, asymptotically normal and semiparametric efficient. A simple estimator for the variance of the estimates is also derived. The finite sample performance is evaluated via extensive simulations.

  8. Graft pathology at the time of harvest: impact on long-term survival

    Directory of Open Access Journals (Sweden)

    Shi-Min Yuan

    2014-12-01

    Full Text Available Objective: This study aims to present the graft pathology at the time of harvest and its impact on long-term survival. Methods: The remnants of the bypass grafts from 66 consecutive patients with coronary artery disease receiving a coronary artery bypass grafting were investigated pathologically, and pertinent predictive risk factors and survival were analyzed. Results: Medial degenerative changes with or without intimal proliferation were present in 36.8%, 37.8% and 35.6% of left internal mammary artery (IMA, radial artery and saphenous vein grafts. There were 2 (3.0% hospital deaths and 9 (14.1% late deaths. Multinomial logistic regression revealed left IMA pathological changes, dyslipidemia, history of percutaneous transluminal coronary angioplasty/stent deployment and Y-graft were significant predictive risk factors negatively influencing the patients’ long-term survival. Kaplan-Meier survival analysis revealed that the long-term survival of patients with left IMA pathological changes were significantly reduced compared with those without (74.1% vs. 91.4%, P=0.002; whereas no differences were noted in long-term survivals between patients with and without pathological changes of the radial arterial or saphenous vein grafts. Conclusion: Pathological changes may be seen in the bypass graft at the time of harvest. The subtle ultrastructural modifications and the expressions of vascular tone regulators might be responsible for late graft patency. The pathological changes of the left IMA at the time of harvest rather than those of the radial artery or saphenous vein graft affect significantly longterm survival. Non-traumatic maneuver of left IMA harvest, well-controlled dyslipidemia and avoidance of using composite grafts can be helpful in maintaining the architecture of the grafts.

  9. Relationship between Plasma Fibroblast Growth Factor-23 Concentration and Survival Time in Cats with Chronic Kidney Disease.

    Science.gov (United States)

    Geddes, R F; Elliott, J; Syme, H M

    2015-01-01

    Fibroblast growth factor-23 (FGF-23) and parathyroid hormone (PTH) are commonly increased in cats with azotemic chronic kidney disease (CKD). Both are predictors of survival time in human patients, but these relationships have not previously been examined in the cat. To investigate the relationship between plasma FGF-23 and PTH concentrations at diagnosis of CKD in cats with survival time and with disease progression over 12 months. 214 azotemic, client-owned cats (≥9 years). Retrospective study: Biochemical and urinary variables at diagnosis of azotemic CKD, including plasma FGF-23 and PTH concentrations were assessed as predictors of survival time (all-cause mortality) using Cox regression, and as predictors of CKD progression over 12 months using logistic regression. In the final multivariable Cox regression model, survival was negatively associated with plasma creatinine (P = .002) and FGF-23 concentrations (P = .014), urine protein-to-creatinine ratio (P cats with CKD, independent of other factors including plasma creatinine and phosphate concentrations. Further work is required to assess if FGF-23 contributes directly to CKD progression, but regardless these findings may make FGF-23 a useful biomarker for predicting poorer outcomes in cats with CKD. Copyright © 2015 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

  10. 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.

  11. Modelling circulating tumour cells for personalised survival prediction in metastatic breast cancer.

    Directory of Open Access Journals (Sweden)

    Gianluca Ascolani

    2015-05-01

    Full Text Available Ductal carcinoma is one of the most common cancers among women, and the main cause of death is the formation of metastases. The development of metastases is caused by cancer cells that migrate from the primary tumour site (the mammary duct through the blood vessels and extravasating they initiate metastasis. Here, we propose a multi-compartment model which mimics the dynamics of tumoural cells in the mammary duct, in the circulatory system and in the bone. Through a branching process model, we describe the relation between the survival times and the four markers mainly involved in metastatic breast cancer (EPCAM, CD47, CD44 and MET. In particular, the model takes into account the gene expression profile of circulating tumour cells to predict personalised survival probability. We also include the administration of drugs as bisphosphonates, which reduce the formation of circulating tumour cells and their survival in the blood vessels, in order to analyse the dynamic changes induced by the therapy. We analyse the effects of circulating tumour cells on the progression of the disease providing a quantitative measure of the cell driver mutations needed for invading the bone tissue. Our model allows to design intervention scenarios that alter the patient-specific survival probability by modifying the populations of circulating tumour cells and it could be extended to other cancer metastasis dynamics.

  12. Determinants of treatment waiting times for head and neck cancer in the Netherlands and their relation to survival.

    Science.gov (United States)

    van Harten, Michel C; Hoebers, Frank J P; Kross, Kenneth W; van Werkhoven, Erik D; van den Brekel, Michiel W M; van Dijk, Boukje A C

    2015-03-01

    Waiting to start treatment has been shown to be associated with tumor progression and upstaging in head and neck squamous cell carcinomas (HNSCCs). This diminishes the chance of cure and might lead to unnecessary mortality. We investigated the association between waiting times and survival in the Netherlands and assessed which factors were associated to longer waiting times. Patient (age, sex, socioeconomic status (SES), tumor (site, stage) and treatment (type, of institute of diagnosis/treatment) characteristics for patients with HNSCC who underwent treatment were extracted from the Netherlands Cancer Registry (NCR) for 2005-2011. Waiting time was defined as the number of days between histopathological diagnosis and start of treatment. Univariable and multivariable Cox regression was used to evaluate survival. In total, 13,140 patients were included, who had a median waiting time of 37days. Patients who were more likely to wait longer were men, patients with a low SES, oropharynx tumors, stage IV tumors, patients to be treated with radiotherapy or chemoradiation, and patients referred for treatment to a Head and Neck Oncology Center (HNOC) from another hospital. The 5-year overall survival was 58% for all patients. Our multivariable Cox regression model showed that longer waiting time, was significantly related to a higher hazard of dying (p<0.0001). This is the first large population-based study showing that longer waiting time for surgery, radiotherapy or chemoradiation is a significant negative prognostic factor for HNSCC patients. Copyright © 2014 Elsevier Ltd. All rights reserved.

  13. Racial Disparities in Clinical Presentation and Survival Times Among Young-Onset Colorectal Adenocarcinoma.

    Science.gov (United States)

    Arshad, Hafiz Muhammad Sharjeel; Kabir, Christopher; Tetangco, Eula; Shah, Natahsa; Raddawi, Hareth

    2017-09-01

    Recently published data indicate increasing incidence of colorectal adenocarcinoma (CRC) in young-onset (racial disparities in presentation and survival times among non-Hispanic Blacks (NHB) and Hispanics compared with non-Hispanic Whites (NHW). A retrospective single-center cohort study was conducted from 2004 through 2014 using 96 patient medical charts with a diagnosis of young-onset CRC. Age, gender, primary site, and histological stage at the time of diagnosis were assessed for survival probabilities by racial group over a minimum follow-up period of 5 years. Among subjects with CRC diagnosis before 50 years of age, the majority of subjects were between 40 and 50 years, with CRC presentation occurring among this age group for 51 (79.7%) of NHW, 18 (81.8%) of NHB, and 5 (50.0%) of Hispanics. The majority of all patients presented with advanced stages of CRC (31.3% with stage III and 27.1% with stage IV). NHB exhibited statistically significantly worse survival compared to NHW (adjusted hazard ratio for death = 2.09; 95% confidence interval 1.14-3.84; P = 0.02). A possible trend of worse survival was identified for Hispanics compared to NHW, but this group was low in numbers and results were not statistically significant. Disparities between racial groups among young-onset CRC cases were identified in overall survival and reflect growing concern in rising incidence and differentiated care management.

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

    Energy Technology Data Exchange (ETDEWEB)

    Yokokura, T.; Onoue, M.; Mutai, M. (Yakult Institute for Microbiological Research)

    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.

  15. 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)

  16. Recidivism and Survival Time: Racial Disparity among Jail Ex-Inmates

    Science.gov (United States)

    Jung, Hyunzee; Spjeldnes, Solveig; Yamatani, Hide

    2010-01-01

    Incarcerated men, most of whom are recidivists, are disproportionately black. Much literature about prison ex-inmates reports on this disparity, yet little is known about racial disparity in recidivism rates among jail ex-inmates. This study examined recidivism rates and survival time (period from release date to rearrest) among male ex-inmates…

  17. 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.

  18. 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

    of stroke and remained so during the first month suggesting a female survival advantage. Throughout the second month the rate reversed in favour of men suggesting that women in that period are paying a 'toll' for their initial survival advantage. Hereafter, the rate steadily decreased, and after 4 months...

  19. Model evaluation based on the negative predictive value for interval-censored survival outcomes.

    Science.gov (United States)

    Han, Seungbong; Tsui, Kam-Wah; Andrei, Adin-Cristian

    2017-04-01

    In many cohort studies, time to events such as disease recurrence is recorded in an interval-censored format. An important objective is to predict patient outcomes. Clinicians are interested in predictive covariates. Prediction rules based on the receiver operating characteristic curve alone are not related to the survival endpoint. We propose a model evaluation strategy to leverage the predictive accuracy based on negative predictive functions. Our proposed method makes very few assumptions and only requires a working model to obtain the regression coefficients. A nonparametric estimate of the predictive accuracy provides a simple and flexible approach for model evaluation to interval-censored survival outcomes. The implementation effort is minimal, therefore this method has an increased potential for immediate use in biomedical data analyses. Simulation studies and a breast cancer trial example further illustrate the practical advantages of this approach.

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

    Science.gov (United States)

    Régnière, Jacques; Powell, James; Bentz, Barbara; Nealis, Vincent

    2012-05-01

    The developmental response of insects to temperature is important in understanding the ecology of insect life histories. Temperature-dependent phenology models permit examination of the impacts of temperature on the geographical distributions, population dynamics and management of insects. The measurement of insect developmental, survival and reproductive responses to temperature poses practical challenges because of their modality, variability among individuals and high mortality near the lower and upper threshold temperatures. We address this challenge with an integrated approach to the design of experiments and analysis of data based on maximum likelihood. This approach expands, simplifies and unifies the analysis of laboratory data parameterizing the thermal responses of insects in particular and poikilotherms in general. This approach allows the use of censored observations (records of surviving individuals that have not completed development after a certain time) and accommodates observations from temperature transfer treatments in which individuals pass only a portion of their development at an extreme (near-threshold) temperature and are then placed in optimal conditions to complete their development with a higher rate of survival. Results obtained from this approach are directly applicable to individual-based modeling of insect development, survival and reproduction with respect to temperature. This approach makes possible the development of process-based phenology models that are based on optimal use of available information, and will aid in the development of powerful tools for analyzing eruptive insect population behavior and response to changing climatic conditions. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

  1. Survival probability and first-passage-time statistics of a Wiener process driven by an exponential time-dependent drift

    Science.gov (United States)

    Urdapilleta, Eugenio

    2011-02-01

    The survival probability and the first-passage-time statistics are important quantities in different fields. The Wiener process is the simplest stochastic process with continuous variables, and important results can be explicitly found from it. The presence of a constant drift does not modify its simplicity; however, when the process has a time-dependent component the analysis becomes difficult. In this work we analyze the statistical properties of the Wiener process with an absorbing boundary, under the effect of an exponential time-dependent drift. Based on the backward Fokker-Planck formalism we set the time-inhomogeneous equation and conditions that rule the diffusion of the corresponding survival probability. We propose as the solution an expansion series in terms of the intensity of the exponential drift, resulting in a set of recurrence equations. We explicitly solve the expansion up to second order and comment on higher-order solutions. The first-passage-time density function arises naturally from the survival probability and preserves the proposed expansion. Explicit results, related properties, and limit behaviors are analyzed and extensively compared to numerical simulations.

  2. Survival probability and first-passage-time statistics of a Wiener process driven by an exponential time-dependent drift.

    Science.gov (United States)

    Urdapilleta, Eugenio

    2011-02-01

    The survival probability and the first-passage-time statistics are important quantities in different fields. The Wiener process is the simplest stochastic process with continuous variables, and important results can be explicitly found from it. The presence of a constant drift does not modify its simplicity; however, when the process has a time-dependent component the analysis becomes difficult. In this work we analyze the statistical properties of the Wiener process with an absorbing boundary, under the effect of an exponential time-dependent drift. Based on the backward Fokker-Planck formalism we set the time-inhomogeneous equation and conditions that rule the diffusion of the corresponding survival probability. We propose as the solution an expansion series in terms of the intensity of the exponential drift, resulting in a set of recurrence equations. We explicitly solve the expansion up to second order and comment on higher-order solutions. The first-passage-time density function arises naturally from the survival probability and preserves the proposed expansion. Explicit results, related properties, and limit behaviors are analyzed and extensively compared to numerical simulations.

  3. Cancer patient survival in Estonia 1995-2009: time trends and data quality.

    Science.gov (United States)

    Innos, K; Baburin, A; Aareleid, T

    2014-06-01

    Survival from most cancers in Estonia has been consistently below European average. The objective of this study was to examine recent survival trends in Estonia and to quantify the effect on survival estimates of the temporary disruption of the Estonian Cancer Registry (ECR) practices in 2001-2007 when death certificates could not be used for case ascertainment. ECR data on all adult cases of 16 common cancers diagnosed in Estonia during 1995-2008 and followed up for vital status until 2009 were used to estimate relative survival ratios (RSR). We used cohort analysis for patients diagnosed in 1995-1999 and 2000-2004; and period hybrid approach to obtain the most recent estimates (2005-2009). We compared five-year RSRs calculated from data sets with and without death certificate initiated (DCI) cases. A total of 64328 cancer cases were included in survival analysis. Compared with 1995-1999, five-year age-standardized RSR increased 20 percent units for prostate cancer, reaching 76% in 2005-2009. A rise of 10 percent units or more was also seen for non-Hodgkin lymphoma (five-year RSR 51% in 2005-2009), and cancers of rectum (49%), breast (73%) and ovary (37%). The effect of including/excluding DCI cases from survival analysis was small except for lung and pancreatic cancers. Relative survival continued to increase in Estonia during the first decade of the 21st century, although for many cancers, a gap between Estonia and more affluent countries still exists. Cancer control efforts should aim at the reduction of risk factors amenable to primary prevention, but also at the improvement of early diagnosis and ensuring timely and optimal care to all cancer patients. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. The effect of time until surgical intervention on survival in dogs with secondary septic peritonitis

    OpenAIRE

    Bush, Maxwell; Carno, Margaret A.; St. Germaine, Lindsay; Hoffmann, Daniel E.

    2016-01-01

    This retrospective study examined the effect of time to intervention on outcome in cases of dogs with secondary septic peritonitis, and also searched for other potential prognostic factors. The medical records of 55 dogs were reviewed. No association was found between outcome and the time from hospital admission to surgical source control. However, several other factors were found to influence survival, including: age, needing vasopressors, lactate, pre-operative packed cell volume, serum alk...

  5. Epithelial ovarian cancer mortality among Hispanic women: Sub-ethnic disparities and survival trend across time: An analysis of SEER 1992-2013.

    Science.gov (United States)

    Chen, Chen; Markossian, Talar W; Silva, Abigail; Tarasenko, Yelena N

    2018-02-01

    Over the past half century the proportion of Hispanics in the US population has been steadily increasing, and groups of Hispanic origin have diversified. Despite notable racial and ethnic disparities in ovarian cancer (OC) mortality, population-based studies on OC among Hispanic females are lacking. To examine sub-ethnic disparities in OC mortality and survival trends using the Surveillance, Epidemiology, and End Results Program (SEER) 18 data on Hispanic women diagnosed with epithelial OC during 1992-2013. The disparities in OC 5 year survival and mortality were examined using log-rank tests and Cox proportional hazards models, adjusted for sociodemographic and pathological characteristics, time of diagnosis, receipt of resection surgery and county socioeconomic status. Trends in 5-year survival rates were examined using joinpoint regression models. The 5-year survival was lowest in Puerto Ricans (median survival: 33 months; survival rate: 31.07%) and was highest in the "Other" Hispanic subgroup (median survival: 59 months; survival rate: 49.14%) (log-rank test: P survival rates: from 43.37% to 48.94% (APC = 0.41, P = 0.40) and from 48.72% to 53.46% (APC = 0.29, P = 0.50), respectively. OC mortality in Hispanic patients varied by sub-ethnicity. This heterogeneity should be considered in future cancer data collection, reports and research. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Modeling biological rhythms in failure time data

    Directory of Open Access Journals (Sweden)

    Myles James D

    2006-11-01

    Full Text Available Abstract Background The human body exhibits a variety of biological rhythms. There are patterns that correspond, among others, to the daily wake/sleep cycle, a yearly seasonal cycle and, in women, the menstrual cycle. Sine/cosine functions are often used to model biological patterns for continuous data, but this model is not appropriate for analysis of biological rhythms in failure time data. Methods We adapt the cosinor method to the proportional hazards model and present a method to provide an estimate and confidence interval of the time when the minimum hazard is achieved. We then apply this model to data taken from a clinical trial of adjuvant of pre-menopausal breast cancer patients. Results The application of this technique to the breast cancer data revealed that the optimal day for pre-resection incisional or excisional biopsy of 28-day cycle (i. e. the day associated with the lowest recurrence rate is day 8 with 95% confidence interval of 4–12 days. We found that older age, fewer positive nodes, smaller tumor size, and experimental treatment were predictive of longer relapse-free survival. Conclusion In this paper we have described a method for modeling failure time data with an underlying biological rhythm. The advantage of adapting a cosinor model to proportional hazards model is its ability to model right censored data. We have presented a method to provide an estimate and confidence interval of the day in the menstrual cycle where the minimum hazard is achieved. This method is not limited to breast cancer data, and may be applied to any biological rhythms linked to right censored data.

  7. Semi-parametric regression model for survival data: graphical visualization with R.

    Science.gov (United States)

    Zhang, Zhongheng

    2016-12-01

    Cox proportional hazards model is a semi-parametric model that leaves its baseline hazard function unspecified. The rationale to use Cox proportional hazards model is that (I) the underlying form of hazard function is stringent and unrealistic, and (II) researchers are only interested in estimation of how the hazard changes with covariate (relative hazard). Cox regression model can be easily fit with coxph() function in survival package. Stratified Cox model may be used for covariate that violates the proportional hazards assumption. The relative importance of covariates in population can be examined with the rankhazard package in R. Hazard ratio curves for continuous covariates can be visualized using smoothHR package. This curve helps to better understand the effects that each continuous covariate has on the outcome. Population attributable fraction is a classic quantity in epidemiology to evaluate the impact of risk factor on the occurrence of event in the population. In survival analysis, the adjusted/unadjusted attributable fraction can be plotted against survival time to obtain attributable fraction function.

  8. External validation of a prognostic model for predicting survival of cirrhotic patients with refractory ascites.

    Science.gov (United States)

    Guardiola, Jordi; Baliellas, Carme; Xiol, Xavier; Fernandez Esparrach, Glòria; Ginès, Pere; Ventura, Pere; Vazquez, Santiago

    2002-09-01

    Cirrhotic patients with refractory ascites (RA) have a poor prognosis, although individual survival varies greatly. A model that could predict survival for patients with RA would be helpful in planning treatment. Moreover, in cases of potential liver transplantation, a model of these characteristics would provide the bases for establishing priorities of organ allocation and the selection of patients for a living donor graft. Recently, we developed a model to predict survival of patients with RA. The aim of this study was to establish its generalizability for predicting the survival of patients with RA. The model was validated by assessing its performance in an external cohort of patients with RA included in a multicenter, randomized, controlled trial that compared large-volume paracentesis and peritoneovenous shunt. The values for actual and model-predicted survival of three risk groups of patients, established according to the model, were compared graphically and by means of the one-sample log-rank test. The model provided a very good fit to the survival data of the three risk groups in the validation cohort. We also found good agreement between the survival predicted from the model and the observed survival when patients treated with peritoneovenous shunt and with paracentesis were considered separately. Our survival model can be used to predict the survival of patients with RA and may be a useful tool in clinical decision making, especially in deciding priority for liver transplantation.

  9. 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; Narod, Steven A

    2017-05-01

    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. 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. 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. 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. 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. 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 87.5% (95% CI, 86.5%-88.4%) for women with no pregnancy) (age-adjusted HR, 0.22; 95% CI, 0.10-0.49; P

  10. Habitat-specific breeder survival of Florida Scrub-Jays: Inferences from multistate models

    Science.gov (United States)

    Breininger, D.R.; Nichols, J.D.; Carter, G.M.; Oddy, D.M.

    2009-01-01

    Quantifying habitat-specific survival and changes in habitat quality within disturbance-prone habitats is critical for understanding population dynamics and variation in fitness, and for managing degraded ecosystems. We used 18 years of color-banding data and multistate capture-recapture models to test whether habitat quality within territories influences survival and detection probability of breeding Florida Scrub-Jays (Aphelocoma coerulescens) and to estimate bird transition probabilities from one territory quality state to another. Our study sites were along central Florida's Atlantic coast and included two of the four largest metapopulations within the species range. We developed Markov models for habitat transitions and compared these to bird transition probabilities. Florida Scrub-Jay detection probabilities ranged from 0.88 in the tall territory state to 0.99 in the optimal state; detection probabilities were intermediate in the short state. Transition probabilities were similar for birds and habitat in grid cells mapped independently of birds. Thus, bird transitions resulted primarily from habitat transitions between states over time and not from bird movement. Survival ranged from 0.71 in the short state to 0.82 in the optimal state, with tall states being intermediate. We conclude that average Florida Scrub-Jay survival will remain at levels that lead to continued population declines because most current habitat quality is only marginally suitable across most of the species range. Improvements in habitat are likely to be slow and difficult because tall states are resistant to change and the optimal state represents an intermediate transitional stage. The multistate modeling approach to quantifying survival and habitat transition probabilities is useful for quantifying habitat transition probabilities and comparing them to bird transition probabilities to test for habitat selection in dynamic environments. ?? 2009 by the Ecological society ot America.

  11. 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.

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

    Science.gov (United States)

    Dharap, S B; Kamath, S; Kumar, V

    2017-01-01

    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. 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). 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 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. 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-duty doctors in primary centers should be a priority in countries with limited resources.

  13. Surviving blind decomposition: A distributional analysis of the time-course of complex word recognition.

    Science.gov (United States)

    Schmidtke, Daniel; Matsuki, Kazunaga; Kuperman, Victor

    2017-11-01

    The current study addresses a discrepancy in the psycholinguistic literature about the chronology of information processing during the visual recognition of morphologically complex words. Form-then-meaning accounts of complex word recognition claim that morphemes are processed as units of form prior to any influence of their meanings, whereas form-and-meaning models posit that recognition of complex word forms involves the simultaneous access of morphological and semantic information. The study reported here addresses this theoretical discrepancy by applying a nonparametric distributional technique of survival analysis (Reingold & Sheridan, 2014) to 2 behavioral measures of complex word processing. Across 7 experiments reported here, this technique is employed to estimate the point in time at which orthographic, morphological, and semantic variables exert their earliest discernible influence on lexical decision RTs and eye movement fixation durations. Contrary to form-then-meaning predictions, Experiments 1-4 reveal that surface frequency is the earliest lexical variable to exert a demonstrable influence on lexical decision RTs for English and Dutch derived words (e.g., badness; bad + ness), English pseudoderived words (e.g., wander; wand + er) and morphologically simple control words (e.g., ballad; ball + ad). Furthermore, for derived word processing across lexical decision and eye-tracking paradigms (Experiments 1-2; 5-7), semantic effects emerge early in the time-course of word recognition, and their effects either precede or emerge simultaneously with morphological effects. These results are not consistent with the premises of the form-then-meaning view of complex word recognition, but are convergent with a form-and-meaning account of complex word recognition. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  14. Simple parametric survival analysis with anonymized register data: A cohort study with truncated and interval censored event and censoring times

    Directory of Open Access Journals (Sweden)

    Kristiansen Ivar S

    2011-08-01

    Full Text Available Abstract Background To preserve patient anonymity, health register data may be provided as binned data only. Here we consider as example, how to estimate mean survival time after a diagnosis of metastatic colorectal cancer from Norwegian register data on time to death or censoring binned into 30 day intervals. All events occurring in the first three months (90 days after diagnosis were removed to achieve comparability with a clinical trial. The aim of the paper is to develop and implement a simple, and yet flexible method for analyzing such interval censored and truncated data. Methods Considering interval censoring a missing data problem, we implement a simple multiple imputation strategy that allows flexible sensitivity analyses with respect to the shape of the censoring distribution. To allow identification of appropriate parametric models, a χ2-goodness-of-fit test--also imputation based--is derived and supplemented with diagnostic plots. Uncertainty estimates for mean survival times are obtained via a simulation strategy. The validity and statistical efficiency of the proposed method for varying interval lengths is investigated in a simulation study and compared with simpler alternatives. Results Mean survival times estimated from the register data ranged from 1.2 (SE = 0.09 to 3.2 (0.31 years depending on period of diagnosis and choice of parametric model. The shape of the censoring distribution within intervals did generally not influence results, whereas the choice of parametric model did, even when different models fit the data equally well. In simulation studies both simple midpoint imputation and multiple imputation yielded nearly unbiased analyses (relative biases of -0.6% to 9.4% and confidence intervals with near-nominal coverage probabilities (93.4% to 95.7% for censoring intervals shorter than six months. For 12 month censoring intervals, multiple imputation provided better protection against bias, and coverage probabilities

  15. 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.

  16. 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

  17. Determination of the impact of melanoma surgical timing on survival using the National Cancer Database.

    Science.gov (United States)

    Conic, Ruzica Z; Cabrera, Claudia I; Khorana, Alok A; Gastman, Brian R

    2018-01-01

    The ideal timing for melanoma treatment, predominantly surgery, remains undetermined. Patient concern for receiving immediate treatment often exceeds surgeon or hospital availability, requiring establishment of a safe window for melanoma surgery. To assess the impact of time to definitive melanoma surgery on overall survival. Patients with stage I to III cutaneous melanoma and with available time to definitive surgery and overall survival were identified by using the National Cancer Database (N = 153,218). The t test and chi-square test were used to compare variables. Cox regression was used for multivariate analysis. In a multivariate analysis of patients in all stages who were treated between 90 and 119 days after biopsy (hazard ratio [HR], 1.09; 95% confidence interval [CI], 1.01-1.18) and more than 119 days (HR, 1.12; 95% CI, 1.02-1.22) had a higher risk for mortality compared with those treated within 30 days of biopsy. In a subgroup analysis of stage I, higher mortality risk was found in patients treated within 30 to 59 days (HR, 1.05; 95% CI, 1.01-1.1), 60 to 89 days (HR, 1.16; 95% CI, 1.07-1.25), 90 to 119 days (HR, 1.29; 95% CI, 1.12-1.48), and more than 119 days after biopsy (HR, 1.41; 95% CI, 1.21-1.65). Surgical timing did not affect survival in stages II and III. Melanoma-specific survival was not available. Expeditious treatment of stage I melanoma is associated with improved outcomes. Copyright © 2017 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

  18. Correlation between Pulmonary Function Indexes and Survival Time 
in Patients with Advanced Lung Cancer

    OpenAIRE

    Ge, Hui; Jiang, Zhenghua; Huang, Qian; Muyun ZHU; Yang, Jie

    2013-01-01

    Background and objective To those patients with advanced lung cancer, the ultimate objective is to improve the curative effect and quality of life, lung function indexes are an important factor. We investigate the change of lung function and the relationship between pulmonary function indexs and survival time in patients with advanced lung cancer. Methods Lung function was detected in 59 cases with lung cancer and 63 normal controls. The relationship between pulmonary function indexs and surv...

  19. Timing analysis by model checking

    Science.gov (United States)

    Naydich, Dimitri; Guaspari, David

    2000-01-01

    The safety of modern avionics relies on high integrity software that can be verified to meet hard real-time requirements. The limits of verification technology therefore determine acceptable engineering practice. To simplify verification problems, safety-critical systems are commonly implemented under the severe constraints of a cyclic executive, which make design an expensive trial-and-error process highly intolerant of change. Important advances in analysis techniques, such as rate monotonic analysis (RMA), have provided a theoretical and practical basis for easing these onerous restrictions. But RMA and its kindred have two limitations: they apply only to verifying the requirement of schedulability (that tasks meet their deadlines) and they cannot be applied to many common programming paradigms. We address both these limitations by applying model checking, a technique with successful industrial applications in hardware design. Model checking algorithms analyze finite state machines, either by explicit state enumeration or by symbolic manipulation. Since quantitative timing properties involve a potentially unbounded state variable (a clock), our first problem is to construct a finite approximation that is conservative for the properties being analyzed-if the approximation satisfies the properties of interest, so does the infinite model. To reduce the potential for state space explosion we must further optimize this finite model. Experiments with some simple optimizations have yielded a hundred-fold efficiency improvement over published techniques.

  20. Modelling of Attentional Dwell Time

    DEFF Research Database (Denmark)

    Petersen, Anders; Kyllingsbæk, Søren; Bundesen, Claus

    2009-01-01

    Studies of the time course of visual attention have identified a temporary functional blindness to the second of two spatially separated targets: attending to one visual stimulus may lead to impairments in identifying a second stimulus presented between 200 to 500 ms after the first. This phenome......Studies of the time course of visual attention have identified a temporary functional blindness to the second of two spatially separated targets: attending to one visual stimulus may lead to impairments in identifying a second stimulus presented between 200 to 500 ms after the first....... This phenomenon is known as attentional dwell time (e.g. Duncan, Ward, Shapiro, 1994). All Previous studies of the attentional dwell time have looked at data averaged across subjects. In contrast, we have succeeded in running subjects for 3120 trials which has given us reliable data for modelling data from...... individual subjects. Our new model is based on the Theory of Visual Attention (TVA; Bundesen, 1990). TVA has previously been successful in explaining results from experiments where stimuli are presented simultaneously in the spatial domain (e.g. whole report and partial report) but has not yet been extended...

  1. A flexible alternative to the Cox proportional hazards model for assessing the prognostic accuracy of hospice patient survival.

    Directory of Open Access Journals (Sweden)

    Branko Miladinovic

    Full Text Available Prognostic models are often used to estimate the length of patient survival. The Cox proportional hazards model has traditionally been applied to assess the accuracy of prognostic models. However, it may be suboptimal due to the inflexibility to model the baseline survival function and when the proportional hazards assumption is violated. The aim of this study was to use internal validation to compare the predictive power of a flexible Royston-Parmar family of survival functions with the Cox proportional hazards model. We applied the Palliative Performance Scale on a dataset of 590 hospice patients at the time of hospice admission. The retrospective data were obtained from the Lifepath Hospice and Palliative Care center in Hillsborough County, Florida, USA. The criteria used to evaluate and compare the models' predictive performance were the explained variation statistic R(2, scaled Brier score, and the discrimination slope. The explained variation statistic demonstrated that overall the Royston-Parmar family of survival functions provided a better fit (R(2 =0.298; 95% CI: 0.236-0.358 than the Cox model (R(2 =0.156; 95% CI: 0.111-0.203. The scaled Brier scores and discrimination slopes were consistently higher under the Royston-Parmar model. Researchers involved in prognosticating patient survival are encouraged to consider the Royston-Parmar model as an alternative to Cox.

  2. Association of serum lipid levels over time with survival in incident peritoneal dialysis patients.

    Science.gov (United States)

    Park, Cheol Ho; Kang, Ea Wha; Park, Jung Tak; Han, Seung Hyeok; Yoo, Tae-Hyun; Kang, Shin-Wook; Chang, Tae Ik

    The association of dyslipidemia with mortality has not been fully evaluated in patients on peritoneal dialysis (PD). Moreover, changes in lipids levels over time and associated death risk have not yet been studied in this population. We studied the association of time-updated serum lipid concentrations with all-cause and cardiovascular (CV) mortalities in a 10-year cohort of 749 incident PD patients. Association was assessed using time-varying Cox proportional hazard regression models with adjustment for multiple variables including statin therapy. During a median follow-up of 36 (interquartile range, 21-61) months, 273 all-cause and 107 CV deaths occurred. Compared with those with total cholesterol (TC) of 180 to <210 or low-density lipoprotein cholesterol (LDL-C) of 100 to <130 mg/dL, hazard ratios (95% confidence interval) of the lowest TC (<150 mg/dL) and LDL-C (<70 mg/dL) were 2.32 (1.61-3.35) and 2.02 (1.45-2.83) for all-cause mortality and 1.87 (1.04-3.37) and 1.92 (1.13-3.26) for CV mortality, respectively. Lower triglyceride (<100 mg/dL) and high-density lipoprotein cholesterol (<30 mg/dL) levels were associated with higher all-cause mortality (1.66 [1.11-2.47] and 1.57 [1.08-2.29]) but not with CV mortality. Contrary to the general population, lower TC and LDL-C levels over time were significantly associated with both worse survival and increased CV mortality in incident PD patients. Although lower triglyceride and high-density lipoprotein cholesterol concentrations were associated with significantly higher all-cause mortality, they failed to show any clear association with CV mortality. The underlying mechanisms responsible for this apparent paradox await further investigations. Copyright © 2017 National Lipid Association. Published by Elsevier Inc. All rights reserved.

  3. Modeling fecundity in the presence of a sterile fraction using a semi-parametric transformation model for grouped survival data.

    Science.gov (United States)

    McLain, Alexander C; Sundaram, Rajeshwari; Buck Louis, Germaine M

    2016-02-01

    The analysis of fecundity data is challenging and requires consideration of both highly timed and interrelated biologic processes in the context of essential behaviors such as sexual intercourse during the fertile window. Understanding human fecundity is further complicated by presence of a sterile population, i.e. couples unable to achieve pregnancy. Modeling techniques conducted to date have largely relied upon discrete time-to-pregnancy survival or day-specific probability models to estimate the determinants of time-to-pregnancy or acute effects, respectively. We developed a class of semi-parametric grouped transformation cure models that capture day-level variates purported to affect the cycle-level hazards of conception and, possibly, sterility. Our model's performance is assessed using simulation and longitudinal data from one of the few prospective cohort studies with preconception enrollment of women followed for 12 menstrual cycles at risk for pregnancy. © The Author(s) 2012.

  4. Modeling the long-term kinetics of Salmonella survival on dry pet food.

    Science.gov (United States)

    Lambertini, Elisabetta; Mishra, Abhinav; Guo, Miao; Cao, Huilin; Buchanan, Robert L; Pradhan, Abani K

    2016-09-01

    Due to multiple outbreaks and large-scale product recalls, Salmonella has emerged as a priority pathogen in dry pet food and treats. However, little data are available to quantify risks posed by these classes of products to both pets and their owners. Specifically, the kinetics of Salmonella survival on complex pet food matrices are not available. This study measured the long-term kinetics of Salmonella survival on a dry pet food under storage conditions commonly encountered during production, retail, and in households (aw Salmonella enterica cocktail of 12 strains isolated from dry pet foods and treats was used to inoculate commercial dry dog food. Salmonella was enumerated on non-selective (BHI) and selective (XLD and BS) media. Results at 570 days indicated an initial relatively rapid decline (up to 54 days), followed by a much slower extended decline phase. The Weibull model provided a satisfactory fit for time series of Log-transformed Salmonella counts from all three media (δ: mean 4.65 day/Log (CFU/g); p: mean 0.364 on BHI). This study provides a survival model that can be applied in quantitative risk assessment models. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Associations of red and processed meat with survival after colorectal cancer and differences according to timing of dietary assessment.

    Science.gov (United States)

    Carr, Prudence R; Jansen, Lina; Walter, Viola; Kloor, Matthias; Roth, Wilfried; Bläker, Hendrik; Chang-Claude, Jenny; Brenner, Hermann; Hoffmeister, Michael

    2016-01-01

    Little is known about the prognostic impact of red and processed meat intake or about changes in consumption after a diagnosis of colorectal cancer (CRC). We investigated associations of baseline red and processed meat with survival outcomes and explored changes in intake among CRC survivors 5 y after diagnosis. A total of 3122 patients diagnosed with CRC between 2003 and 2010 were followed for a median of 4.8 y [DACHS (Darmkrebs: Chancen der Verhütung durch Screening) study]. Patients provided information on diet and other factors in standardized questionnaires at baseline and at the 5-y follow-up. Cox proportional hazards regression models were used to estimate HRs and 95% CIs. Among patients with stage I-III CRC, baseline red and processed meat intake was not associated with overall (>1 time/d compared with processed meat at the 5-y follow-up than at baseline (concordance rate: 39%; κ-value: 0.10; 95% CI: 0.07, 0.13). Our findings suggest that baseline red and processed meat intake is not associated with poorer survival among patients with CRC. The potential interaction with KRAS mutation status warrants further evaluation. Major changes in consumption measured at the 5-y follow-up may have had an impact on our survival estimates. © 2016 American Society for Nutrition.

  6. The Association between Phase Angle of Bioelectrical Impedance Analysis and Survival Time in Advanced Cancer Patients: Preliminary Study.

    Science.gov (United States)

    Lee, So Yeon; Lee, Yong Joo; Yang, Jung-Hwa; Kim, Chul-Min; Choi, Whan-Seok

    2014-09-01

    A frequent manifestation of advanced cancer patients is malnutrition, which is correlated with poor prognosis and high mortality. Bioelectrical impedance analysis (BIA) is an easy-to-use and non-invasive technique to evaluate changes in body composition and nutritional status. We investigated BIA-derived phase angle as a prognostic indicator for survival in advanced cancer patients. Twenty-eight patients treated at the hospice center of Seoul St. Mary's Hospital underwent BIA measurements from January, 2013 to May, 2013. We also evaluated palliative prognostic index (PPI) and palliative performance scale to compare with the prognostic value of phase angle. Cox's proportional hazard models were constructed to evaluate the prognostic effect of phase angle. The Kaplan Meier method was used to calculate survival. Using univariate Cox analysis, phase angle (hazard ratio [HR], 0.61/per degree increase; 95% confidence interval [CI], 0.42 to 0.89; P = 0.010), PPI (HR, 1.21; 95% CI, 1.00 to 1.47; P = 0.048) were found to be significantly associated with survival. Adjusting age, PPI, body mass index, phase angle significantly showed association with survival in multivariate analysis (HR, 0.64/per degree increase; 95% CI, 0.42 to 0.95; P = 0.028). Survival time of patients with phase angle ≥ 4.4° was longer than patients with phase angle < 4.4° (log rank, 6.208; P-value = 0.013). Our data suggest BIA-derived phase angle may serve as an independent prognostic indicator in advanced cancer patients.

  7. 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.

  8. 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.

  9. Gene-gene interaction analysis for the survival phenotype based on the Cox model.

    Science.gov (United States)

    Lee, Seungyeoun; Kwon, Min-Seok; Oh, Jung Mi; Park, Taesung

    2012-09-15

    For the past few decades, many statistical methods in genome-wide association studies (GWAS) have been developed to identify SNP-SNP interactions for case-control studies. However, there has been less work for prospective cohort studies, involving the survival time. Recently, Gui et al. (2011) proposed a novel method, called Surv-MDR, for detecting gene-gene interactions associated with survival time. Surv-MDR is an extension of the multifactor dimensionality reduction (MDR) method to the survival phenotype by using the log-rank test for defining a binary attribute. However, the Surv-MDR method has some drawbacks in the sense that it needs more intensive computations and does not allow for a covariate adjustment. In this article, we propose a new approach, called Cox-MDR, which is an extension of the generalized multifactor dimensionality reduction (GMDR) to the survival phenotype by using a martingale residual as a score to classify multi-level genotypes as high- and low-risk groups. The advantages of Cox-MDR over Surv-MDR are to allow for the effects of discrete and quantitative covariates in the frame of Cox regression model and to require less computation than Surv-MDR. Through simulation studies, we compared the power of Cox-MDR with those of Surv-MDR and Cox regression model for various heritability and minor allele frequency combinations without and with adjusting for covariate. We found that Cox-MDR and Cox regression model perform better than Surv-MDR for low minor allele frequency of 0.2, but Surv-MDR has high power for minor allele frequency of 0.4. However, when the effect of covariate is adjusted for, Cox-MDR and Cox regression model perform much better than Surv-MDR. We also compared the performance of Cox-MDR and Surv-MDR for a real data of leukemia patients to detect the gene-gene interactions with the survival time. leesy@sejong.ac.kr; tspark@snu.ac.kr.

  10. Entrainment and Survival Time of the Llsvps: Effects of Flow in the Thermochemical Pile Interior

    Science.gov (United States)

    Mulyukova, E.; Steinberger, B. M.; Dabrowski, M.; Sobolev, S. V.

    2013-12-01

    , it increases the amount of heat flow into the overlying ambient material, and thus leads to more vigorous convection in the ambient layer. This change in ambient flow has a feedback on the dense layer through both thermal and mechanical coupling. An additional effect of convection in the interior of the dense layer is the entrainment of the ambient material into that layer, and thus more efficient mechanical mixing between the two materials. This results in the decrease of the effective buoyancy ratio over time. Finally, the stresses acting along the chemical interface due to flow within the dense layer have a direct effect on the geometry of the layer, including its topography. We present the results of numerical simulations that illustrate the convective flow in the interior of the thermochemical layer or piles. We discuss in detail the effects of this flow on the entrainment and survival time of the dense material. As a possible outlook, these results, in addition to illuminating some of the complexity of deep Earth dynamics, can predict possible structures that may develop in the interior of the large scale heterogeneities, such as the LLSVPs. These predictions can then be tested against the results of seismological studies, and thus serve as an additional constraint on models of the deep mantle.

  11. Impact of Treatment Time on the Survival of Patients Suffering from Invasive Fungal Rhinosinusitis

    Directory of Open Access Journals (Sweden)

    Patorn Piromchai

    2014-01-01

    Full Text Available Background Invasive fungal rhinosinusitis is an uncommon disease with high mortality rates. There is currently no consensus on the best treatment timing. We studied the impact of the treatment timing on the survival of patients experiencing invasive fungal rhinosinusitis. Methods We conducted a retrospective study of patients suffering from invasive fungal rhinosinusitis. The duration of symptoms, clinical presentations, clinical signs, diagnoses, treatments, and outcomes were collected. Results It was observed that more than 70% of the mortalities occurred within the subgroup of patients who exhibited symptoms of the disease within 14 days before admission. After adjusting for the confounders, the time taken to treat the patients was the most statistically significant predictor for mortality ( P = 0.045. We found no significant relationships between mortality and its significant covariates, which included the underlying diseases ( P = 0.91 or complications ( P = 0.55. Conclusions Our study demonstrates that the time taken to treat the patients is an important determinant for the survival of patients who are afflicted with invasive fungal rhinosinusitis. The appropriate treatments should be administered within 14 days from the time the symptoms begin to manifest.

  12. Impact of treatment time on the survival of patients suffering from invasive fungal rhinosinusitis.

    Science.gov (United States)

    Piromchai, Patorn; Thanaviratananich, Sanguansak

    2014-01-01

    Invasive fungal rhinosinusitis is an uncommon disease with high mortality rates. There is currently no consensus on the best treatment timing. We studied the impact of the treatment timing on the survival of patients experiencing invasive fungal rhinosinusitis. We conducted a retrospective study of patients suffering from invasive fungal rhinosinusitis. The duration of symptoms, clinical presentations, clinical signs, diagnoses, treatments, and outcomes were collected. It was observed that more than 70% of the mortalities occurred within the subgroup of patients who exhibited symptoms of the disease within 14 days before admission. After adjusting for the confounders, the time taken to treat the patients was the most statistically significant predictor for mortality (P = 0.045). We found no significant relationships between mortality and its significant covariates, which included the underlying diseases (P = 0.91) or complications (P = 0.55). Our study demonstrates that the time taken to treat the patients is an important determinant for the survival of patients who are afflicted with invasive fungal rhinosinusitis. The appropriate treatments should be administered within 14 days from the time the symptoms begin to manifest.

  13. Survival time of dogs with splenic hemangiosarcoma treated by splenectomy with or without adjuvant chemotherapy: 208 cases (2001-2012).

    Science.gov (United States)

    Wendelburg, Kristin M; Price, Lori Lyn; Burgess, Kristine E; Lyons, Jeremiah A; Lew, Felicia H; Berg, John

    2015-08-15

    To determine survival time for dogs with splenic hemangiosarcoma treated with splenectomy alone, identify potential prognostic factors, and evaluate the efficacy of adjuvant chemotherapy. Retrospective case series. 208 dogs. Medical records were reviewed, long-term follow-up information was obtained, and survival data were analyzed statistically. 154 dogs were treated with surgery alone, and 54 were treated with surgery and chemotherapy. Twenty-eight dogs received conventional chemotherapy, 13 received cyclophosphamide-based metronomic chemotherapy, and 13 received both conventional and metronomic chemotherapy. Median survival time of dogs treated with splenectomy alone was 1.6 months. Clinical stage was the only prognostic factor significantly associated with survival time. When the entire follow-up period was considered, there was no significant difference in survival time between dogs treated with surgery alone and dogs treated with surgery and chemotherapy. However, during the first 4 months of follow-up, after adjusting for the effects of clinical stage, survival time was significantly prolonged among dogs receiving any type of chemotherapy (hazard ratio, 0.6) and among dogs receiving both conventional and metronomic chemotherapy (hazard ratio, 0.4). Clinical stage was strongly associated with prognosis for dogs with splenic hemangiosarcoma. Chemotherapy was effective in prolonging survival time during the early portion of the follow-up period. Combinations of doxorubicin-based conventional protocols and cyclophosphamide-based metronomic protocols appeared to be more effective than either type of chemotherapy alone, but prolongations in survival time resulting from current protocols were modest.

  14. Estimation of total genetic effects for survival time in crossbred laying hens showing cannibalism, using pedigree or genomic information.

    Science.gov (United States)

    Brinker, T; Raymond, B; Bijma, P; Vereijken, A; Ellen, E D

    2017-02-01

    Mortality of laying hens due to cannibalism is a major problem in the egg-laying industry. Survival depends on two genetic effects: the direct genetic effect of the individual itself (DGE) and the indirect genetic effects of its group mates (IGE). For hens housed in sire-family groups, DGE and IGE cannot be estimated using pedigree information, but the combined effect of DGE and IGE is estimated in the total breeding value (TBV). Genomic information provides information on actual genetic relationships between individuals and might be a tool to improve TBV accuracy. We investigated whether genomic information of the sire increased TBV accuracy compared with pedigree information, and we estimated genetic parameters for survival time. A sire model with pedigree information (BLUP) and a sire model with genomic information (ssGBLUP) were used. We used survival time records of 7290 crossbred offspring with intact beaks from four crosses. Cross-validation was used to compare the models. Using ssGBLUP did not improve TBV accuracy compared with BLUP which is probably due to the limited number of sires available per cross (~50). Genetic parameter estimates were similar for BLUP and ssGBLUP. For both BLUP and ssGBLUP, total heritable variance (T(2) ), expressed as a proportion of phenotypic variance, ranged from 0.03 ± 0.04 to 0.25 ± 0.09. Further research is needed on breeding value estimation for socially affected traits measured on individuals kept in single-family groups. © 2016 The Authors. Journal of Animal Breeding and Genetics Published by Blackwell Verlag GmbH.

  15. 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.

  16. 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.

  17. Leptin-deficient obesity prolongs survival in a murine model of myelodysplastic syndrome.

    Science.gov (United States)

    Kraakman, Michael J; Kammoun, Helene L; Dragoljevic, Dragana; Al-Sharea, Annas; Lee, Man K S; Flynn, Michelle C; Stolz, Christian J; Guirguis, Andrew A; Lancaster, Graeme I; Chin-Dusting, Jaye; Curtis, David J; Murphy, Andrew J

    2018-01-25

    Obesity enhances the risk of developing myelodysplastic syndromes. However, the effect of obesity on survival is unclear. Obese people present with monocytosis due to inflammatory signals emanating from obese adipose tissue. We hypothesized that obesity-induced myelopoiesis would promote the transition of myelodysplastic syndrome to acute myeloid leukemia and accelerate mortality in obesity. Obese Ob/Ob mice or their lean littermate controls received a bone marrow transplant from NUP98-HOXD13 transgenic mice, a model of myelodysplastic syndrome. The metabolic parameters of the mice were examined throughout the course of the study, as were blood leukocytes. Myeloid cells were analyzed in the bone, spleen, liver and adipose tissue by flow cytometry halfway through the disease progression and at the endpoint. Survival curves were also calculated. Contrary to our hypothesis, transplantation of NUP98-HOXD13 bone marrow into obese recipient mice significantly increased survival time compared with lean recipient controls. While monocyte skewing was exacerbated in obese mice receiving NUP98-HOXD13 bone marrow, transformation to acute myeloid leukemia was not enhanced. Increased survival of obese mice was associated with a preservation of fat mass as well as increased myeloid cell deposition within the adipose tissue and a concomitant reduction in detrimental myeloid cell accumulation within other organs. This study revealed that obesity increases survival in animals with myelodysplastic syndrome. This may be due to the greater fat mass of Ob/Ob mice, which acts as a sink for myeloid cells, preventing their accumulation in other key organs such as the liver. Copyright © 2018, Ferrata Storti Foundation.

  18. Managing hospitals in turbulent times: do organizational changes improve hospital survival?

    Science.gov (United States)

    Lee, S Y; Alexander, J A

    1999-10-01

    To examine (1) the degree to which organizational changes affected hospital survival; (2) whether core and peripheral organizational changes affected hospital survival differently; and (3) how simultaneous organizational changes affected hospital survival. AHA Hospital Surveys, the Area Resource File, and the AHA Hospital Guides, Part B: Multihospital Systems. The study employed a longitudinal panel design. We followed changes in all community hospitals in the continental United States from 1981 through 1994. The dependent variable, hospital closure, was examined as a function of multiple changes in a hospital's core and peripheral structures as well as the hospital's organizational and environmental characteristics. Cox regression models were used to test the expectations that core changes increased closure risk while peripheral changes decreased such risk, and that simultaneous core and peripheral changes would lead to higher risk of closure. Results indicated more peripheral than core changes in community hospitals. Overall, findings contradicted our expectations. Change in specialty, a core change, was beneficial for hospitals, because it reduced closure risk. The two most frequent peripheral changes, downsizing and leadership change, were positively associated with closure. Simultaneous organizational changes displayed a similar pattern: multiple core changes reduced closure risk, while multiple peripheral changes increased the risk. These patterns held regardless of the level of uncertainty in hospital environments. Organizational changes are not all beneficial for hospitals, suggesting that hospital leaders should be both cautious and selective in their efforts to turn their hospitals around.

  19. Meta-analysis of the effects of beta blocker on survival time in cancer patients.

    Science.gov (United States)

    Choi, Chel Hun; Song, Taejong; Kim, Tae Hyun; Choi, Jun Kuk; Park, Jin-Young; Yoon, Aera; Lee, Yoo-Young; Kim, Tae-Joong; Bae, Duk-Soo; Lee, Jeong-Won; Kim, Byoung-Gie

    2014-07-01

    This study was to elucidate the potential benefit of beta blockers on cancer survival. We comprehensively searched PubMed, Embase, and the Cochrane Library from their inception to April 2013. Two authors independently screened and reviewed the eligibility of each study and coded the participants, treatment, and outcome characteristics. The primary outcomes were overall survival (OS) and disease-free survival (DFS). Twelve studies published between 1993 and 2013 were included in the final analysis. Four papers reported results from 10 independent groups, resulting in a total of 18 comparisons based on data obtained from 20,898 subjects. Effect sizes (hazard ratios, HR) were heterogeneous, and random-effects models were used in the analyses. The meta-analysis demonstrated that beta blocker use is associated with improved OS (HR 0.79; 95 % CI 0.67-0.93; p = 0.004) and DFS (HR 0.69; 95 % CI 0.53-0.91; p = 0.009). Although statistically not significant, the effect size was greater in patients with low-stage cancer or cancer treated primarily with surgery than in patients with high-stage cancer or cancer treated primarily without surgery (HR 0.60 vs. 0.78, and 0.60 vs. 0.80, respectively). Although only two study codes were analyzed, the studies using nonselective beta blockers showed that there was no overall effect on OS (HR 0.52, 95 % CI 0.09-3.04). This meta-analysis provides evidence that beta blocker use can be associated with the prolonged survival of cancer patients, especially patients with early-stage cancer treated primarily with surgery.

  20. Analyzing the Survival of Colorectal Cancer Patients of Tehran Taleghani Hospital using Non-Mixture Cure Model

    Directory of Open Access Journals (Sweden)

    Zahra Abdolalian

    2016-12-01

    Full Text Available Abstract Background: 4cure models are a model to analyze survival data that these models exist for long term survivors. Cure models are a special type of survival model where it is assumed that there are a proportion of subjects who had never event, thus, survival curve will eventually reach a plateau. Therefore, standard survival models are not appropriate because they do not account for the possibility of cure.The aim of the present research is to apply non-mixture cure model to analyze survival of patients with colorectal cancer. Materials and Methods: We studied 232 patients with colorectal cancer who were visited and treated at Taleghani Hospital Research Center for Gastroenterology and Liver Disease in Tehran. These patients were diagnosed from 1987 to 2012 and followed up until 2013. The Effect of age, gender, family history, body mass index and site of infection were studied. Kaplan-Meier and Non-Mixture cure Model were used for analzing data. Results: The ten-year survival rate after diagnosis in the studied patients was 64 % .A total of 60 (25.8 % deaths due to colorectal cancer were observed. The mean of age at the time of diagnosis was 51.6 years. Based on non-mixed cure model, the rangs of age was 45-65 years old and BMI were significant. Conclusion: When the population is divided into two groups (susceptible and non- susceptible individuals, using Cox semi-parametric model is not appropriate. Therefore, we should use cure models.

  1. 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.

  2. Evaluation of red blood cell labelling methods based on a statistical model for red blood cell survival.

    Science.gov (United States)

    Korell, Julia; Coulter, Carolyn V; Duffull, Stephen B

    2011-12-21

    The aim of this work is to compare different labelling methods that are commonly used to estimate the lifespan of red blood cells (RBCs), e.g. in anaemia of renal failure, where the effect of treatment with erythropoietin depends on the lifespan of RBCs. A previously developed model for the survival time of RBCs that accounts for plausible physiological processes of RBC destruction was used to simulate ideal random and cohort labelling methods for RBCs, as well as the flaws associated with these methods (e.g. reuse of label and loss of the label from the surviving RBCs). Random labelling with radioactive chromium and cohort labelling using heavy nitrogen were considered. Blood sampling times were determined for RBC survival studies using both labelling methods by applying the theory of optimal design. It was assessed whether the underlying parameter values of the model are estimable from these studies, and the precision of the parameter estimates were calculated. In theory, parameter estimation would be possible for both types of ideal labelling methods without flaws. However, flaws associated with random labelling are significant and not all parameters controlling RBC survival in the model can be estimated with good precision. In contrast, cohort labelling shows good precision in the parameter estimates even in the presence of reuse and prolonged incorporation of the label. A model based analysis of RBC survival studies is recommended in future to account for limitations in methodology as well as likely causes of RBC destruction. Copyright © 2011 Elsevier Ltd. All rights reserved.

  3. The impact of pamidronate and chemotherapy on survival times in dogs with appendicular primary bone tumors treated with palliative radiation therapy.

    Science.gov (United States)

    Oblak, Michelle L; Boston, Sarah E; Higginson, Geraldine; Patten, Steven G; Monteith, Gabrielle J; Woods, J Paul

    2012-04-01

    To assess survival times in dogs that received palliative radiation therapy (RT) alone, and in combination with chemotherapy, pamidronate, or both for primary appendicular bone tumors and determine whether the addition of these adjunctive therapies affects survival. Retrospective case series. Dogs (n = 50) with primary appendicular bone tumors. Dogs were divided into the following treatment groups: RT alone, RT + chemotherapy, RT+ pamidronate, and RT+ chemotherapy + pamidronate. Dogs were considered for analysis if they had a known euthanasia date or follow-up data were available for at least 120 days from the time of diagnosis. Survival time was defined as the time from admission to euthanasia. Cox proportional hazard models and Kaplan-Meier survival functions were used. A P value of less than .05 was considered significant. Fifty dogs were considered for survival analysis. Median survival times (MSTs) were longest for dogs receiving RT and chemotherapy (307 days; 95% CI: 279, 831) and shortest in dogs receiving RT and pamidronate (69 days; 95% CI: 47, 112 days). The difference in MST between dogs who received pamidronate and those who did not in this population was statistically significant in a univariate (P = .039) and multivariate analysis (P = .0015). The addition of chemotherapy into any protocol improved survival (P Chemotherapy should be recommended in addition to a palliative RT protocol to improve survival of dogs with primary appendicular bone tumors. When combined with RT ± chemotherapy, pamidronate decreased MST and should not be included in a standard protocol. © Copyright 2012 by The American College of Veterinary Surgeons.

  4. Modeling growth performances, survival, and feed efficiency of four ...

    African Journals Online (AJOL)

    Survival, feed efficiency and growth performances of four local breeds of chickens in West Cameroon (normally feathered NF, feathered tarsus FT, crested C and naked neck NN,) have been compared from hatch to 16 weeks, to determine which one could be improved by selection. Gompertz equation was used to fit growth ...

  5. Influence of application sequence and timing of eugenol and lauric arginate (LAE) on survival of spoilage organisms.

    Science.gov (United States)

    Manrique, Yudith; Gibis, Monika; Schmidt, Herbert; Weiss, Jochen

    2017-06-01

    The effectiveness of sequential applications of the antimicrobials eugenol and lauric arginate (LAE) was investigated against Staphylococcus carnosus, Listeria innocua, Escherichia coli K12, and Pseudomonas fluorescens. The antimicrobials were applied simultaneously at half of their minimum lethal concentrations (MLC) or sequentially at t = 0 h and t = 3, 4, 6 or 8 h. Bacterial survival was determined by direct plate counts. Survivals kinetic were fitted to a growth and mortality model to obtain characteristic parameters that described time-dependent changes from growth to mortality or vice versa. The most effective was a simultaneous exposure of both antimicrobials to the spoilage organisms at the beginning of the incubation period. Efficiency decreases depending on order and timing of the two antimicrobials were observed upon sequential treatments. These were most effective when antimicrobials where applied within a short time period (3-4 h) and when eugenol was first applied against S. carnosus and P. fluorescens. No sequence effects were observed for L. innocua, and sequential treatments proved to be ineffective against E. coli K12. These results were attributed to cells adapting to the first applied antimicrobial. In some cases, this provided protection against the second antimicrobial rendering the overall treatment less effective. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Survival of hendra virus in the environment: modelling the effect of temperature.

    Science.gov (United States)

    Scanlan, J C; Kung, N Y; Selleck, P W; Field, H E

    2015-03-01

    Hendra virus (HeV), a highly pathogenic zoonotic paramyxovirus recently emerged from bats, is a major concern to the horse industry in Australia. Previous research has shown that higher temperatures led to lower virus survival rates in the laboratory. We develop a model of survival of HeV in the environment as influenced by temperature. We used 20 years of daily temperature at six locations spanning the geographic range of reported HeV incidents to simulate the temporal and spatial impacts of temperature on HeV survival. At any location, simulated virus survival was greater in winter than in summer, and in any month of the year, survival was higher in higher latitudes. At any location, year-to-year variation in virus survival 24 h post-excretion was substantial and was as large as the difference between locations. Survival was higher in microhabitats with lower than ambient temperature, and when environmental exposure was shorter. The within-year pattern of virus survival mirrored the cumulative within-year occurrence of reported HeV cases, although there were no overall differences in survival in HeV case years and non-case years. The model examines the effect of temperature in isolation; actual virus survivability will reflect the effect of additional environmental factors.

  7. The Impact of Radiation Treatment Time on Survival in Patients With Head and Neck Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Shaikh, Talha [Department of Radiation Oncology, Fox Chase Cancer Center, Philadelphia, Pennsylvania (United States); Handorf, Elizabeth A. [Department of Biostatistics, Fox Chase Cancer Center, Philadelphia, Pennsylvania (United States); Murphy, Colin T. [Department of Radiation Oncology, Fox Chase Cancer Center, Philadelphia, Pennsylvania (United States); Mehra, Ranee [Department of Medical Oncology, Fox Chase Cancer Center, Philadelphia, Pennsylvania (United States); Ridge, John A. [Department of Surgical Oncology, Fox Chase Cancer Center, Philadelphia, Pennsylvania (United States); Galloway, Thomas J., E-mail: Thomas.Galloway@fccc.edu [Department of Radiation Oncology, Fox Chase Cancer Center, Philadelphia, Pennsylvania (United States)

    2016-12-01

    Purpose: To assess the impact of radiation treatment time (RTT) in head and neck cancers on overall survival (OS) in the era of chemoradiation. Methods and Materials: Patients with diagnoses of tongue, hypopharynx, larynx, oropharynx, or tonsil cancer were identified by use of the National Cancer Database. RTT was defined as date of first radiation treatment to date of last radiation treatment. In the definitive setting, prolonged RTT was defined as >56 days, accelerated RTT was defined as <47 days, and standard RTT was defined as 47 to 56 days. In the postoperative setting, prolonged RTT was defined as >49 days, accelerated RTT was defined as <40 days, and standard RTT was defined as 40 to 49 days. We used χ{sup 2} tests to identify predictors of RTT. The Kaplan-Meier method was used to compare OS among groups. Cox proportional hazards model was used for OS analysis in patients with known comorbidity status. Results: 19,531 patients were included; 12,987 (67%) had a standard RTT, 4,369 (34%) had an accelerated RTT, and 2,165 (11%) had a prolonged RTT. On multivariable analysis, accelerated RTT (hazard ratio [HR] 0.84; 95% confidence interval [CI] 0.73-0.97) was associated with an improved OS, and prolonged RTT (HR 1.25; 95% CI 1.14-1.37) was associated with a worse OS relative to standard RTT. When the 9,200 (47%) patients receiving definitive concurrent chemoradiation were examined, prolonged RTT (HR 1.29; 95% CI 1.11-1.50) was associated with a worse OS relative to standard RTT, whereas there was no significant association between accelerated RTT and OS (HR 0.76; 95% CI 0.57-1.01). Conclusion: Prolonged RTT is associated with worse OS in patients receiving radiation therapy for head and neck cancer, even in the setting of chemoradiation. Expeditious completion of radiation should continue to be a quality metric for the management of head and neck malignancies.

  8. Molecular clock evidence for survival of Antarctic cyanobacteria (Oscillatoriales, Phormidium autumnale) from Paleozoic times.

    Science.gov (United States)

    Strunecký, Otakar; Elster, Josef; Komárek, Jiří

    2012-11-01

    Cyanobacteria are well adapted to freezing and desiccation; they have been proposed as possible survivors of comprehensive Antarctic glaciations. Filamentous types from the order Oscillatoriales, especially the species Phormidium autumnale Kützing ex Gomont 1892, have widely diverse morphotypes that dominate in Antarctic aquatic microbial mats, seepages, and wet soils. Currently little is known about the dispersion of cyanobacteria in Antarctica and of their population history. We tested the hypothesis that cyanobacteria survived Antarctic glaciations directly on site after the Gondwana breakup by using the relaxed and strict molecular clock in the analysis of the 16S rRNA gene. We estimated that the biogeographic history of Antarctic cyanobacteria belonging to P. autumnale lineages has ancient origins. The oldest go further back in time than the breakup of Gondwana and originated somewhere on the supercontinent between 442 and 297 Ma. Enhanced speciation rate was found around the time of the opening of the Drake Passage (c. 31-45 Ma) with beginning of glaciations (c. 43 Ma). Our results, based primarily on the strains collected in maritime Antarctica, mostly around James Ross Island, support the hypothesis that long-term survival took place in glacial refuges. The high morphological diversification of P. autumnale suggested the coevolution of lineages and formation of complex associations with different morphologies, resulting in a specific endemic Antarctic cyanobacterial flora. © 2012 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

  9. Multivariable model development and internal validation for prostate cancer specific survival and overall survival after whole-gland salvage Iodine-125 prostate brachytherapy.

    Science.gov (United States)

    Peters, Max; van der Voort van Zyp, Jochem R N; Moerland, Marinus A; Hoekstra, Carel J; van de Pol, Sandrine; Westendorp, Hendrik; Maenhout, Metha; Kattevilder, Rob; Verkooijen, Helena M; van Rossum, Peter S N; Ahmed, Hashim U; Shah, Taimur T; Emberton, Mark; van Vulpen, Marco

    2016-04-01

    Whole-gland salvage Iodine-125-brachytherapy is a potentially curative treatment strategy for localised prostate cancer (PCa) recurrences after radiotherapy. Prognostic factors influencing PCa-specific and overall survival (PCaSS & OS) are not known. The objective of this study was to develop a multivariable, internally validated prognostic model for survival after whole-gland salvage I-125-brachytherapy. Whole-gland salvage I-125-brachytherapy patients treated in the Netherlands from 1993-2010 were included. Eligible patients had a transrectal ultrasound-guided biopsy-confirmed localised recurrence after biochemical failure (clinical judgement, ASTRO or Phoenix-definition). Recurrences were assessed clinically and with CT and/or MRI. Metastases were excluded using CT/MRI and technetium-99m scintigraphy. Multivariable Cox-regression was used to assess the predictive value of clinical characteristics in relation to PCa-specific and overall mortality. PCa-specific mortality was defined as patients dying with distant metastases present. Missing data were handled using multiple imputation (20 imputed sets). Internal validation was performed and the C-statistic calculated. Calibration plots were created to visually assess the goodness-of-fit of the final model. Optimism-corrected survival proportions were calculated. All analyses were performed according to the TRIPOD statement. Median total follow-up was 78months (range 5-139). A total of 62 patients were treated, of which 28 (45%) died from PCa after mean (±SD) 82 (±36) months. Overall, 36 patients (58%) patients died after mean 84 (±40) months. PSA doubling time (PSADT) remained a predictive factor for both types of mortality (PCa-specific and overall): corrected hazard ratio's (HR's) 0.92 (95% CI: 0.86-0.98, p=0.02) and 0.94 (95% CI: 0.90-0.99, p=0.01), respectively (C-statistics 0.71 and 0.69, respectively). Calibration was accurate up to 96month follow-up. Over 80% of patients can survive 8years if PSADT>24

  10. 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

    Background: 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. Methods: 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...... 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 response time in this study) to 5 minutes. Conclusions: The absolute survival...

  11. Inhibition of the Mitochondrial Fission Protein Drp1 Improves Survival in a Murine Cardiac Arrest Model

    Science.gov (United States)

    Sharp, Willard W.; Beiser, David G.; Fang, Yong Hu; Han, Mei; Piao, Lin; Varughese, Justin; Archer, Stephen L.

    2015-01-01

    Objectives Survival following sudden cardiac arrest is poor despite advances in cardiopulmonary resuscitation (CPR) and the use of therapeutic hypothermia. Dynamin related protein 1 (Drp1), a regulator of mitochondrial fission, is an important determinant of reactive oxygen species generation, myocardial necrosis, and left ventricular function following ischemia/reperfusion injury, but its role in cardiac arrest is unknown. We hypothesized that Drp1 inhibition would improve survival, cardiac hemodynamics, and mitochondrial function in an in vivo model of cardiac arrest. Design Laboratory investigation. Setting University laboratory Interventions Anesthetized and ventilated adult female C57BL/6 wild-type mice underwent an 8-min KCl induced cardiac arrest followed by 90 seconds of CPR. Mice were then blindly randomized to a single intravenous injection of Mdivi-1 (0.24 mg/kg), a small molecule Drp1 inhibitor or vehicle (DMSO). Measurements and Main Results Following resuscitation from cardiac arrest, mitochondrial fission was evidenced by Drp1 translocation to the mitochondrial membrane and a decrease in mitochondrial size. Mitochondrial fission was associated with increased lactate and evidence of oxidative damage. Mdivi-1 administration during CPR inhibited Drp1 activation, preserved mitochondrial morphology, and decreased oxidative damage. Mdivi-1 also reduced the time to return of spontaneous circulation (ROSC) 116±4 vs. 143±7 sec (pcardiac arrest. Conclusions Post cardiac arrest inhibition of Drp1 improves time to ROSC and myocardial hemodynamics resulting in improved survival and neurological outcomes in a murine model of cardiac arrest. Pharmacological targeting of mitochondrial fission may be a promising therapy for cardiac arrest. PMID:25599491

  12. Incubation media affect the survival, pathway and time of embryo development in Neotropical annual fish Austrolebias nigrofasciatus (Rivulidae).

    Science.gov (United States)

    da Fonseca, A P; Volcan, M V; Robaldo, R B

    2018-01-01

    To analyse the survival, pathway and time of embryo development in the annual fish Austrolebias nigrofasciatus eggs were monitored in four liquid media and two damp media under experimental conditions for 130 days until their development was complete. Eggs kept in the same breeding water from oviposition remained in diapause I (DI) during all experiments. In constrast, up to the stage prior to entering diapause II (DII), the other media had no influence on development. Embryos at this stage (DII), however, show longer development time when treated in medium with water and powdered coconut shell so that about 80% of embryos remained in DII at 100 days. In contrast, all other treatments had a significantly lower proportion of embryos remaining in DII. When treated with Yamamoto's solution in humid media, embryos showed the fastest development. The first fully developed embryos (DIII) were seen at 27 days after oviposition. It took an average of 46-58 days for 50% of eggs in each treatment to reach DIII. Compared with other studies, survival in all incubation media was high at between 70 and 98%. Taken together, it can be concluded that all incubation media were found to be viable for maintaining embryos. Altering developmental trajectories through the manipulation of diapauses in different media makes this species a potential model organism for laboratory studies. © 2017 The Fisheries Society of the British Isles.

  13. Prognostic Model for Survival in Patients With Early Stage Cervical Cancer

    NARCIS (Netherlands)

    Biewenga, Petra; van der Velden, Jacobus; Mol, Ben Willem J.; Stalpers, Lukas J. A.; Schilthuis, Marten S.; van der Steeg, Jan Willem; Burger, Matthé P. M.; Buist, Marrije R.

    2011-01-01

    BACKGROUND: In the management of early stage cervical cancer, knowledge about the prognosis is critical. Although many factors have an impact on survival, their relative importance remains controversial. This study aims to develop a prognostic model for survival in early stage cervical cancer

  14. Prognostic model for survival in patients with metastatic renal cell carcinoma: results from the international kidney cancer working group.

    Science.gov (United States)

    Manola, Judith; Royston, Patrick; Elson, Paul; McCormack, Jennifer Bacik; Mazumdar, Madhu; Négrier, Sylvie; Escudier, Bernard; Eisen, Tim; Dutcher, Janice; Atkins, Michael; Heng, Daniel Y C; Choueiri, Toni K; Motzer, Robert; Bukowski, Ronald

    2011-08-15

    To develop a single validated model for survival in metastatic renal cell carcinoma (mRCC) using a comprehensive international database. A comprehensive database of 3,748 patients including previously reported clinical prognostic factors was established by pooling patient-level data from clinical trials. Following quality control and standardization, descriptive statistics were generated. Univariate analyses were conducted using proportional hazards models. Multivariable analysis using a log-logistic model stratified by center and multivariable fractional polynomials was conducted to identify independent predictors of survival. Missing data were handled using multiple imputation methods. Three risk groups were formed using the 25th and 75th percentiles of the resulting prognostic index. The model was validated using an independent data set of 645 patients treated with tyrosine kinase inhibitor (TKI) therapy. Median survival in the favorable, intermediate and poor risk groups was 26.9 months, 11.5 months, and 4.2 months, respectively. Factors contributing to the prognostic index included treatment, performance status, number of metastatic sites, time from diagnosis to treatment, and pretreatment hemoglobin, white blood count, lactate dehydrogenase, alkaline phosphatase, and serum calcium. The model showed good concordance when tested among patients treated with TKI therapy (C statistic = 0.741, 95% CI: 0.714-0.768). Nine clinical factors can be used to model survival in mRCC and form distinct prognostic groups. The model shows utility among patients treated in the TKI era. ©2011 AACR.

  15. Time trend analysis of primary tumor resection for stage IV colorectal cancer: less surgery, improved survival.

    Science.gov (United States)

    Hu, Chung-Yuan; Bailey, Christina E; You, Y Nancy; Skibber, John M; Rodriguez-Bigas, Miguel A; Feig, Barry W; Chang, George J

    2015-03-01

    With the advent of effective modern chemotherapeutic and biologic agents, primary tumor resection for patients with stage IV colorectal cancer (CRC) may not be routinely necessary. To evaluate the secular patterns of primary tumor resection use in stage IV CRC in the United States. A retrospective cohort study using data from the National Cancer Institute's Surveillance, Epidemiology, and End Results CRC registry. Demographic and clinical factors were compared for 64,157 patients diagnosed with stage IV colon or rectal cancer from January 1, 1988, through December 31, 2010, who had undergone primary tumor resection and those who had not. Rates of primary tumor resection and median relative survival were calculated for each year. Joinpoint regression analysis was used to determine when a significant change in trend in the primary tumor resection rate had occurred. Logistic regression analysis was used to assess factors associated with primary tumor resection. Difference in primary tumor resection rates over time. Of the 64,157 patients with stage IV CRC, 43,273 (67.4%) had undergone primary tumor resection. The annual rate of primary tumor resection decreased from 74.5% in 1988 to 57.4% in 2010 (Ptrend toward fewer primary tumor resections was seen. Despite the decreasing primary tumor resection rate, patient survival rates improved. However, primary tumor resection may still be overused, and current treatment practices lag behind evidence-based treatment guidelines.

  16. 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

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

    DEFF Research Database (Denmark)

    Martinussen, T.; Vansteelandt, S.; Gerster, M.

    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......-stage estimates). We give the large sample properties of the estimator proposed and investigate its small sample properties by Monte Carlo simulation. A real data example is provided for illustration....

  18. 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.

  19. 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.

  20. Environmental enrichment extends photoreceptor survival and visual function in a mouse model of retinitis pigmentosa.

    Directory of Open Access Journals (Sweden)

    Ilaria Barone

    Full Text Available Slow, progressive rod degeneration followed by cone death leading to blindness is the pathological signature of all forms of human retinitis pigmentosa (RP. Therapeutic schemes based on intraocular delivery of neuroprotective agents prolong the lifetime of photoreceptors and have reached the stage of clinical trial. The success of these approaches depends upon optimization of chronic supply and appropriate combination of factors. Environmental enrichment (EE, a novel neuroprotective strategy based on enhanced motor, sensory and social stimulation, has already been shown to exert beneficial effects in animal models of various disorders of the CNS, including Alzheimer and Huntington disease. Here we report the results of prolonged exposure of rd10 mice, a mutant strain undergoing progressive photoreceptor degeneration mimicking human RP, to such an enriched environment from birth. By means of microscopy of retinal tissue, electrophysiological recordings, visual behaviour assessment and molecular analysis, we show that EE considerably preserves retinal morphology and physiology as well as visual perception over time in rd10 mutant mice. We find that protective effects of EE are accompanied by increased expression of retinal mRNAs for CNTF and mTOR, both factors known as instrumental to photoreceptor survival. Compared to other rescue approaches used in similar animal models, EE is highly effective, minimally invasive and results into a long-lasting retinal protection. These results open novel perspectives of research pointing to environmental strategies as useful tools to extend photoreceptor survival.

  1. 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 (pyogurt stored under temperature range from 3 to 15°C, however, the polynomial model gave a better fit to the experimental data.

  2. The effect of timing of the first kidney transplantation on survival in children initiating renal replacement therapy

    DEFF Research Database (Denmark)

    Kramer, Anneke; Stel, Vianda S; Geskus, Ronald B

    2012-01-01

    Controversy exists concerning the timing of the first kidney transplantation for children who need to start renal replacement therapy (RRT). Our aim was to estimate the effect of timing of the first transplantation on patient survival in children, for the first time also taking into account...

  3. Hydroxocobalamin and epinephrine both improve survival in a swine model of cyanide-induced cardiac arrest.

    Science.gov (United States)

    Bebarta, Vikhyat S; Pitotti, Rebecca L; Dixon, Patricia S; Valtier, Sandra; Esquivel, Luis; Bush, Anneke; Little, Charles M

    2012-10-01

    To determine whether hydroxocobalamin will improve survival compared with epinephrine and saline solution controls in a model of cyanide-induced cardiac arrest. Forty-five swine (38 to 42 kg) were tracheally intubated, anesthetized, and central venous and arterial continuous cardiovascular monitoring catheters were inserted. Potassium cyanide was infused until cardiac arrest developed, defined as mean arterial pressure less than 30 mm Hg. Animals were treated with standardized mechanical chest compressions and were randomly assigned to receive one of 3 intravenous bolus therapies: hydroxocobalamin, epinephrine, or saline solution (control). All animals were monitored for 60 minutes after cardiac arrest. Additional epinephrine infusions were used in all arms of the study after return of spontaneous circulation for systolic blood pressure less than 90 mm Hg. A sample size of 15 animals per group was determined according to a power of 80%, a survival difference of 0.5, and an α of 0.05. Repeated-measure ANOVA was used to determine statistically significant changes between groups over time. Baseline weight, time to arrest, and cyanide dose at cardiac arrest were similar in the 3 groups. Coronary perfusion pressures with chest compressions were greater than 15 mm Hg in both treatment groups indicating sufficient compression depth. Zero of 15 (95% confidence interval [CI] 0% to 25%) animals in the control group, 11 of 15 (73%; 95% CI 48% to 90%) in the hydroxocobalamin group, and 11 of 15 (73%; 95% CI 48% to 90%) in the epinephrine group survived to the conclusion of the study (Pcyanide levels in the hydroxocobalamin group were also lower than that of the epinephrine group from cardiac arrest through the conclusion of the study. Intravenous hydroxocobalamin and epinephrine both independently improved survival compared with saline solution control in our swine model of cyanide-induced cardiac arrest. Hydroxocobalamin improved mean arterial pressure and pH, decreased

  4. Modeling age and nest-specific survival using a hierarchical Bayesian approach.

    Science.gov (United States)

    Cao, Jing; He, Chong Z; Suedkamp Wells, Kimberly M; Millspaugh, Joshua J; Ryan, Mark R

    2009-12-01

    Recent studies have shown that grassland birds are declining more rapidly than any other group of terrestrial birds. Current methods of estimating avian age-specific nest survival rates require knowing the ages of nests, assuming homogeneous nests in terms of nest survival rates, or treating the hazard function as a piecewise step function. In this article, we propose a Bayesian hierarchical model with nest-specific covariates to estimate age-specific daily survival probabilities without the above requirements. The model provides a smooth estimate of the nest survival curve and identifies the factors that are related to the nest survival. The model can handle irregular visiting schedules and it has the least restrictive assumptions compared to existing methods. Without assuming proportional hazards, we use a multinomial semiparametric logit model to specify a direct relation between age-specific nest failure probability and nest-specific covariates. An intrinsic autoregressive prior is employed for the nest age effect. This nonparametric prior provides a more flexible alternative to the parametric assumptions. The Bayesian computation is efficient because the full conditional posterior distributions either have closed forms or are log concave. We use the method to analyze a Missouri dickcissel dataset and find that (1) nest survival is not homogeneous during the nesting period, and it reaches its lowest at the transition from incubation to nestling; and (2) nest survival is related to grass cover and vegetation height in the study area.

  5. Model of white oak flower survival and maturation

    Science.gov (United States)

    David R. Larsen; Robert A. Cecich

    1997-01-01

    A stochastic model of oak flower dynamics is presented that integrates a number of factors which appear to affect the oak pistillate flower development process. The factors are modeled such that the distribution of the predicted flower populations could have come from the same distribution as the observed flower populations. Factors included in the model are; the range...

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

    CERN Document Server

    Nikulin, M; Mesbah, M; Limnios, N

    2004-01-01

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

  7. Gene–gene interaction analysis for the survival phenotype based on the Cox model

    OpenAIRE

    Lee, Seungyeoun; Kwon, Min-Seok; Oh, Jung Mi; Park, Taesung

    2012-01-01

    Motivation: For the past few decades, many statistical methods in genome-wide association studies (GWAS) have been developed to identify SNP–SNP interactions for case-control studies. However, there has been less work for prospective cohort studies, involving the survival time. Recently, Gui et al. (2011) proposed a novel method, called Surv-MDR, for detecting gene–gene interactions associated with survival time. Surv-MDR is an extension of the multifactor dimensionality reduction (MDR) metho...

  8. Survival analysis of gastric cancer patients using Cox model: a five year study

    Directory of Open Access Journals (Sweden)

    Biglarian A

    2009-08-01

    Full Text Available "n Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi;} Background: Gastric cancer is the second most common cancer and known as the second cause of death due to cancers worldwide. Adenocarcinoma is the most fatal cancer in Iran and a patient with this kind of cancer, has a lower lifetime than others. In this research, the survival of patients with gastric carcinoma who were registered at Taleghani Hospital, were studied."n"nMethods: 291 patients with Gastric carcinoma who had received care, chemotherapy or chemoradiotherapy, at Taleghani Hospital in Tehran from 2002 to 2007 were studied as a historical cohort. Their survival rates and its relationship with 12 risk factors were assessed."n"nResults: Of the 291 patients with Gastric carcinoma, 70.1 percent were men and others (29.9% were women. The mean age of men was 62.26 years and of women was 59.32 years at the time of diagnosis. Most of patients (93.91% were advanced stage and metastasis. The Cox proportional hazards model showed that age at diagnosis, tumor stage and histology type with survival time had significant relationships (p=0.039, p=0.042 and p=0.032 respectively."n"n Conclusion: The five-year survival rate and median lifetime of gastric cancer patients who underwent chemotherapy or chemoradiotherapy are very

  9. Impact of collaborative care on survival time for dogs with congestive heart failure and revenue for attending primary care veterinarians.

    Science.gov (United States)

    Lefbom, Bonnie K; Peckens, Neal K

    2016-07-01

    OBJECTIVE To assess the effects of in-person collaborative care by primary care veterinarians (pcDVMs) and board-certified veterinary cardiologists (BCVCs) on survival time of dogs after onset of congestive heart failure (CHF) and on associated revenue for the attending pcDVMs. DESIGN Retrospective cohort study. ANIMALS 26 small-breed dogs treated for naturally occurring CHF secondary to myxomatous mitral valve disease at a multilocation primary care veterinary hospital between 2008 and 2013. PROCEDURES Electronic medical records were reviewed to identify dogs with confirmed CHF secondary to myxomatous mitral valve disease and collect information on patient care, survival time, and pcDVM revenue. Data were compared between dogs that received collaborative care from the pcDVM and a BCVC and dogs that received care from the pcDVM alone. RESULTS Dogs that received collaborative care had a longer median survival time (254 days) than did dogs that received care from the pcDVM alone (146 days). A significant positive correlation was identified between pcDVM revenue and survival time for dogs that received collaborative care (ie, the longer the dog survived, the greater the pcDVM revenue generated from caring for that patient). CONCLUSIONS AND CLINICAL RELEVANCE Findings suggested that collaborative care provided to small-breed dogs with CHF by a BCVC and pcDVM could result in survival benefits for affected dogs and increased revenue for pcDVMs, compared with care provided by a pcDVM alone.

  10. The development of simple survival prediction models for blunt trauma victims treated at Asian emergency centers.

    Science.gov (United States)

    Kimura, Akio; Nakahara, Shinji; Chadbunchachai, Witaya

    2012-02-02

    For real-time assessment of the probability of survival (Ps) of blunt trauma victims at emergency centers, this study aimed to establish regression models for estimating Ps using simplified coefficients. The data of 10,210 blunt trauma patients not missing both the binary outcome data about survival and the data necessary for Ps calculation by The Trauma and Injury Severity Score (TRISS) method were extracted from the Japan Trauma Data Bank (2004-2007) and analyzed. Half (5,113) of the data was allocated to a derivation data set, with the other half (5,097) allocated to a validation data set. The data of 6,407 blunt trauma victims from the trauma registry of Khon Kaen Regional Hospital in Thailand were analyzed for validation. The logistic regression models included age, the Injury Severity Score (ISS), the Glasgow Coma Scale score (GCS), systolic blood pressure (SBP), respiratory rate (RR), and their coded values (cAGE, 0-1; cISS, 0-4; cSBP, 0-4; cGCS, 0-4; cRR, 0-4) as predictor variables. The coefficients were simplified by rounding off after the decimal point or choosing 0.5 if the coefficients varied across 0.5. The area under the receiver-operating characteristic curve (AUROCC) was calculated for each model to measure discriminant ability. A group of formulas (log (Ps/1-Ps) = logit (Ps) = -9 + cISS - cAGE + cSBP + cGCS + cRR/2, where -9 becomes -7 if the predictor variable of cRR or cISS is missing) was developed. Using these formulas, the AUROCCs were between 0.950 and 0.964. When these models were applied to the Khon Kean data, their AUROCCs were greater than 0.91. These equations allow physicians to perform real-time assessments of survival by easy mental calculations at Asian emergency centers, which are overcrowded with blunt injury victims of traffic accidents. © 2012 Kimura et al; licensee BioMed Central Ltd.

  11. The development of simple survival prediction models for blunt trauma victims treated at Asian emergency centers

    Directory of Open Access Journals (Sweden)

    Kimura Akio

    2012-02-01

    Full Text Available Abstract Background For real-time assessment of the probability of survival (Ps of blunt trauma victims at emergency centers, this study aimed to establish regression models for estimating Ps using simplified coefficients. Methods The data of 10,210 blunt trauma patients not missing both the binary outcome data about survival and the data necessary for Ps calculation by The Trauma and Injury Severity Score (TRISS method were extracted from the Japan Trauma Data Bank (2004-2007 and analyzed. Half (5,113 of the data was allocated to a derivation data set, with the other half (5,097 allocated to a validation data set. The data of 6,407 blunt trauma victims from the trauma registry of Khon Kaen Regional Hospital in Thailand were analyzed for validation. The logistic regression models included age, the Injury Severity Score (ISS, the Glasgow Coma Scale score (GCS, systolic blood pressure (SBP, respiratory rate (RR, and their coded values (cAGE, 0-1; cISS, 0-4; cSBP, 0-4; cGCS, 0-4; cRR, 0-4 as predictor variables. The coefficients were simplified by rounding off after the decimal point or choosing 0.5 if the coefficients varied across 0.5. The area under the receiver-operating characteristic curve (AUROCC was calculated for each model to measure discriminant ability. Results A group of formulas (log (Ps/1-Ps = logit (Ps = -9 + cISS - cAGE + cSBP + cGCS + cRR/2, where -9 becomes -7 if the predictor variable of cRR or cISS is missing was developed. Using these formulas, the AUROCCs were between 0.950 and 0.964. When these models were applied to the Khon Kean data, their AUROCCs were greater than 0.91. Conclusion: These equations allow physicians to perform real-time assessments of survival by easy mental calculations at Asian emergency centers, which are overcrowded with blunt injury victims of traffic accidents.

  12. Survival data analyses in ecotoxicology: critical effect concentrations, methods and models. What should we use?

    Science.gov (United States)

    Forfait-Dubuc, Carole; Charles, Sandrine; Billoir, Elise; Delignette-Muller, Marie Laure

    2012-05-01

    In ecotoxicology, critical effect concentrations are the most common indicators to quantitatively assess risks for species exposed to contaminants. Three types of critical effect concentrations are classically used: lowest/ no observed effect concentration (LOEC/NOEC), LC( x) (x% lethal concentration) and NEC (no effect concentration). In this article, for each of these three types of critical effect concentration, we compared methods or models used for their estimation and proposed one as the most appropriate. We then compared these critical effect concentrations to each other. For that, we used nine survival data sets corresponding to D. magna exposition to nine different contaminants, for which the time-course of the response was monitored. Our results showed that: (i) LOEC/NOEC values at day 21 were method-dependent, and that the Cochran-Armitage test with a step-down procedure appeared to be the most protective for the environment; (ii) all tested concentration-response models we compared gave close values of LC50 at day 21, nevertheless the Weibull model had the lowest global mean deviance; (iii) a simple threshold NEC-model both concentration and time dependent more completely described whole data (i.e. all timepoints) and enabled a precise estimation of the NEC. We then compared the three critical effect concentrations and argued that the use of the NEC might be a good option for environmental risk assessment.

  13. Time lags in biological models

    CERN Document Server

    MacDonald, Norman

    1978-01-01

    In many biological models it is necessary to allow the rates of change of the variables to depend on the past history, rather than only the current values, of the variables. The models may require discrete lags, with the use of delay-differential equations, or distributed lags, with the use of integro-differential equations. In these lecture notes I discuss the reasons for including lags, especially distributed lags, in biological models. These reasons may be inherent in the system studied, or may be the result of simplifying assumptions made in the model used. I examine some of the techniques available for studying the solution of the equations. A large proportion of the material presented relates to a special method that can be applied to a particular class of distributed lags. This method uses an extended set of ordinary differential equations. I examine the local stability of equilibrium points, and the existence and frequency of periodic solutions. I discuss the qualitative effects of lags, and how these...

  14. 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

  15. Connecting single-stock assessment models through correlated survival

    DEFF Research Database (Denmark)

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

    2017-01-01

    Fisheries management is mainly conducted via single-stock assessment models assuming that fish stocks do not interact, except through assumed natural mortalities. Currently, the main alternative is complex ecosystem models which require extensive data, are difficult to calibrate, and have long ru...

  16. Predicting time to recall in patients conditionally released from a secure forensic hospital: A survival analysis.

    Science.gov (United States)

    Jewell, Amelia; Cocks, Christopher; Cullen, Alexis E; Fahy, Tom; Dean, Kimberlie

    2018-01-17

    The recall of conditionally discharged forensic patients in England is a formal order from the Ministry of Justice under the Mental Health Act (1983) which has the power to revoke conditional release and direct readmission to hospital. Recall has significant implications for the individual and for hospital services, but despite this, little is known about predictors of recall for forensic patients. We examined the rate of recall for 101 patients conditionally discharged from medium secure forensic inpatient services between 2007 and 2013. Demographic, clinical, and forensic factors were examined as possible predictors of time to recall using Cox regression survival techniques. Conditionally discharged patients were followed for an average of 811 days, during which 45 (44.5%) were recalled to hospital. Younger age (HR 1.89; 95% CI 1.02-3.49; p = 0.04), non-white ethnicity (HR 3.44; 95% CI 1.45-8.13), substance abuse history (HR 2.52; 95% CI 1.17-5.43), early violence (HR 1.90; 95% CI 1.03-3.50), early childhood maladjustment (HR 1.92; 95% CI 1.01-3.68), treatment with a depot medication (HR 2.17; 95% CI 1.14-4.11), being known to mental health services (HR 3.44; 95% CI 1.06-11.16), and a psychiatric admission prior to the index admission (HR 2.44; 95% CI 1.08-5.52) were significantly associated with a shorter time to recall. Treatment with clozapine reduced the risk of recall to hospital (HR 0.40; 95% CI 0.20-0.79). Time to recall can be predicted by a range of factors that are readily available to clinical teams. Further research is required to determine if targeted interventions can modify the likelihood or time to recall for conditionally released forensic patients. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  17. Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models.

    Science.gov (United States)

    Yousefi, Safoora; Amrollahi, Fatemeh; Amgad, Mohamed; Dong, Chengliang; Lewis, Joshua E; Song, Congzheng; Gutman, David A; Halani, Sameer H; Velazquez Vega, Jose Enrique; Brat, Daniel J; Cooper, Lee A D

    2017-09-15

    Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general high-dimensional prediction tasks can be adapted to the problem of predicting cancer outcomes. We perform an extensive comparison of Bayesian optimized deep survival models and other state of the art machine learning methods for survival analysis, and describe a framework for interpreting deep survival models using a risk backpropagation technique. Finally, we illustrate that deep survival models can successfully transfer information across diseases to improve prognostic accuracy. We provide an open-source software implementation of this framework called SurvivalNet that enables automatic training, evaluation and interpretation of deep survival models.

  18. Modeling of pathogen survival during simulated gastric digestion.

    Science.gov (United States)

    Koseki, Shige; Mizuno, Yasuko; Sotome, Itaru

    2011-02-01

    The objective of the present study was to develop a mathematical model of pathogenic bacterial inactivation kinetics in a gastric environment in order to further understand a part of the infectious dose-response mechanism. The major bacterial pathogens Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. were examined by using simulated gastric fluid adjusted to various pH values. To correspond to the various pHs in a stomach during digestion, a modified logistic differential equation model and the Weibull differential equation model were examined. The specific inactivation rate for each pathogen was successfully described by a square-root model as a function of pH. The square-root models were combined with the modified logistic differential equation to obtain a complete inactivation curve. Both the modified logistic and Weibull models provided a highly accurate fitting of the static pH conditions for every pathogen. However, while the residuals plots of the modified logistic model indicated no systematic bias and/or regional prediction problems, the residuals plots of the Weibull model showed a systematic bias. The modified logistic model appropriately predicted the pathogen behavior in the simulated gastric digestion process with actual food, including cut lettuce, minced tuna, hamburger, and scrambled egg. Although the developed model enabled us to predict pathogen inactivation during gastric digestion, its results also suggested that the ingested bacteria in the stomach would barely be inactivated in the real digestion process. The results of this study will provide important information on a part of the dose-response mechanism of bacterial pathogens.

  19. Modeling of Pathogen Survival during Simulated Gastric Digestion ▿

    Science.gov (United States)

    Koseki, Shige; Mizuno, Yasuko; Sotome, Itaru

    2011-01-01

    The objective of the present study was to develop a mathematical model of pathogenic bacterial inactivation kinetics in a gastric environment in order to further understand a part of the infectious dose-response mechanism. The major bacterial pathogens Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. were examined by using simulated gastric fluid adjusted to various pH values. To correspond to the various pHs in a stomach during digestion, a modified logistic differential equation model and the Weibull differential equation model were examined. The specific inactivation rate for each pathogen was successfully described by a square-root model as a function of pH. The square-root models were combined with the modified logistic differential equation to obtain a complete inactivation curve. Both the modified logistic and Weibull models provided a highly accurate fitting of the static pH conditions for every pathogen. However, while the residuals plots of the modified logistic model indicated no systematic bias and/or regional prediction problems, the residuals plots of the Weibull model showed a systematic bias. The modified logistic model appropriately predicted the pathogen behavior in the simulated gastric digestion process with actual food, including cut lettuce, minced tuna, hamburger, and scrambled egg. Although the developed model enabled us to predict pathogen inactivation during gastric digestion, its results also suggested that the ingested bacteria in the stomach would barely be inactivated in the real digestion process. The results of this study will provide important information on a part of the dose-response mechanism of bacterial pathogens. PMID:21131530

  20. 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.

  1. Modelling Tradescantia fluminensis to assess long term survival

    Directory of Open Access Journals (Sweden)

    Alex James

    2015-06-01

    Full Text Available We present a simple Poisson process model for the growth of Tradescantia fluminensis, an invasive plant species that inhibits the regeneration of native forest remnants in New Zealand. The model was parameterised with data derived from field experiments in New Zealand and then verified with independent data. The model gave good predictions which showed that its underlying assumptions are sound. However, this simple model had less predictive power for outputs based on variance suggesting that some assumptions were lacking. Therefore, we extended the model to include higher variability between plants thereby improving its predictions. This high variance model suggests that control measures that promote node death at the base of the plant or restrict the main stem growth rate will be more effective than those that reduce the number of branching events. The extended model forms a good basis for assessing the efficacy of various forms of control of this weed, including the recently-released leaf-feeding tradescantia leaf beetle (Neolema ogloblini.

  2. Classification Models to Predict Survival of Kidney Transplant Recipients Using Two Intelligent Techniques of Data Mining and Logistic Regression.

    Science.gov (United States)

    Nematollahi, M; Akbari, R; Nikeghbalian, S; Salehnasab, C

    2017-01-01

    Kidney transplantation is the treatment of choice for patients with end-stage renal disease (ESRD). Prediction of the transplant survival is of paramount importance. The objective of this study was to develop a model for predicting survival in kidney transplant recipients. In a cross-sectional study, 717 patients with ESRD admitted to Nemazee Hospital during 2008-2012 for renal transplantation were studied and the transplant survival was predicted for 5 years. The multilayer perceptron of artificial neural networks (MLP-ANN), logistic regression (LR), Support Vector Machine (SVM), and evaluation tools were used to verify the determinant models of the predictions and determine the independent predictors. The accuracy, area under curve (AUC), sensitivity, and specificity of SVM, MLP-ANN, and LR models were 90.4%, 86.5%, 98.2%, and 49.6%; 85.9%, 76.9%, 97.3%, and 26.1%; and 84.7%, 77.4%, 97.5%, and 17.4%, respectively. Meanwhile, the independent predictors were discharge time creatinine level, recipient age, donor age, donor blood group, cause of ESRD, recipient hypertension after transplantation, and duration of dialysis before transplantation. SVM and MLP-ANN models could efficiently be used for determining survival prediction in kidney transplant recipients.

  3. Restricted mean survival time over 15 years for patients starting renal replacement therapy.

    Science.gov (United States)

    Couchoud, Cécile; Dantony, Emmanuelle; Elsensohn, Mad-Hélénie; Villar, Emmanuel; Vigneau, Cécile; Moranne, Olivier; Rabilloud, Muriel; Ecochard, René

    2017-04-01

    The restricted mean survival time (RMST) estimates life expectancy up to a given time horizon and can thus express the impact of a disease. The aim of this study was to estimate the 15-year RMST of a hypothetical cohort of incident patients starting renal replacement therapy (RRT), according to their age, gender and diabetes status, and to compare it with the expected RMST of the general population. Using data from 67 258 adult patients in the French Renal Epidemiology and Information Network (REIN) registry, we estimated the RMST of a hypothetical patient cohort (and its subgroups) for the first 15 years after starting RRT (cRMST) and used the general population mortality tables to estimate the expected RMST (pRMST). Results were expressed in three different ways: the cRMST, which calculates the years of life gained under the hypothesis of 100% death without RRT treatment, the difference between the pRMST and the cRMST (the years lost), and a ratio expressing the percentage reduction of the expected RMST: (pRMST - cRMST)/pRMST. Over their first 15 years of RRT, the RMST of end-stage renal disease (ESRD) patients decreased with age, ranging from 14.3 years in patients without diabetes aged 18 years at ESRD to 1.8 years for those aged 90 years, and from 12.7 to 1.6 years, respectively, for those with diabetes; expected RMST varied from 15.0 to 4.1 years between 18 and 90 years. The number of years lost in all subgroups followed a bell curve that was highest for patients aged 70 years. After the age of 55 years in patients with and 70 years in patients without diabetes, the reduction of the expected RMST was >50%. While neither a clinician nor a survival curve can predict with absolute certainty how long a patient will live, providing estimates on years gained or lost, or percentage reduction of expected RMST, may improve the accuracy of the prognostic estimates that influence clinical decisions and information given to patients.

  4. Comparison of Cox Model and K-Nearest Neighbor to Estimation of Survival in Kidney Transplant Patients

    Directory of Open Access Journals (Sweden)

    J. Faradmal

    2016-01-01

    Full Text Available Introduction & Objective: Cox model is a common method to estimate survival and validity of the results is dependent on the proportional hazards assumption. K- Nearest neighbor is a nonparametric method for survival probability in heterogeneous communities. The purpose of this study was to compare the performance of k- nearest neighbor method (K-NN with Cox model. Materials & Methods: This retrospective cohort study was conducted in Hamadan Province, on 475 patients who had undergone kidney transplantation from 1994 to 2011. Data were extracted from patients’ medical records using a checklist. The duration of the time between kidney transplantation and rejection was considered as the surviv­al time. Cox model and k- nearest neighbor method were used for Data modeling. The prediction error Brier score was used to compare the performance models. Results: Out of 475 transplantations, 55 episodes of rejection occurred. 5, 10 and 15 year survival rates of transplantation were 91.70 %, 84.90% and 74.50%, respectively. The number of neighborhood optimized using cross validation method was 45. Cumulative Brier score of k-NN algorithm for t=5, 10 and 15 years were 0.003, 0.006 and 0.007, respectively. Cumulative Brier of score Cox model for t=5, 10 and 15 years were 0.036, 0.058 and 0.058, respectively. Prediction error of k-NN algorithm for t=5, 10 and 15 years was less than Cox model that shows that the k-NN method outperforms. Conclusions: The results of this study show that the predictions of KNN has higher accuracy than the Cox model when sample sizes and the number of predictor variables are high. Sci J Hamadan Univ Med Sci . 2016; 22 (4 :300-308

  5. Time preference and its relationship with age, health, and survival probability

    Directory of Open Access Journals (Sweden)

    Li-Wei Chao

    2009-02-01

    Full Text Available Although theories from economics and evolutionary biology predict that one's age, health, and survival probability should be associated with one's subjective discount rate (SDR, few studies have empirically tested for these links. Our study analyzes in detail how the SDR is related to age, health, and survival probability, by surveying a sample of individuals in townships around Durban, South Africa. In contrast to previous studies, we find that age is not significantly related to the SDR, but both physical health and survival expectations have a U-shaped relationship with the SDR. Individuals in very poor health have high discount rates, and those in very good health also have high discount rates. Similarly, those with expected survival probability on the extremes have high discount rates. Therefore, health and survival probability, and not age, seem to be predictors of one's SDR in an area of the world with high morbidity and mortality.

  6. Modeling longitudinal data and its impact on survival in observational nephrology studies: tools and considerations.

    Science.gov (United States)

    Streja, Elani; Goldstein, Leanne; Soohoo, Melissa; Obi, Yoshitsugu; Kalantar-Zadeh, Kamyar; Rhee, Connie M

    2017-04-01

    Nephrologists and kidney disease researchers are often interested in monitoring how patients' clinical and laboratory measures change over time, what factors may impact these changes, and how these changes may lead to differences in morbidity, mortality, and other outcomes. When longitudinal data with repeated measures over time in the same patients are available, there are a number of analytical approaches that could be employed to describe the trends and changes in these measures, and to explore the associations of these changes with outcomes. Researchers may choose a streamlined and simplified analytic approach to examine trajectories with subsequent outcomes such as estimating deltas (subtraction of the last observation from the first observation) or estimating per patient slopes with linear regression. Conversely, they could more fully address the data complexity by using a longitudinal mixed model to estimate change as a predictor or employ a joint model, which can simultaneously model the longitudinal effect and its impact on an outcome such as survival. In this review, we aim to assist nephrologists and clinical researchers by reviewing these approaches in modeling the association of longitudinal change in a marker with outcomes, while appropriately considering the data complexity. Namely, we will discuss the use of simplified approaches for creating predictor variables representing change in measurements including deltas and patient slopes, as well more sophisticated longitudinal models including joint models, which can be used in addition to simplified models based on the indications and objectives of the study as warranted. © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

  7. Introduction of a prediction model to assigning periodontal prognosis based on survival rates.

    Science.gov (United States)

    Martinez-Canut, Pedro; Alcaraz, Jaime; Alcaraz, Jaime; Alvarez-Novoa, Pablo; Alvarez-Novoa, Carmen; Marcos, Ana; Noguerol, Blas; Noguerol, Fernando; Zabalegui, Ion

    2017-09-04

    To develop a prediction model for tooth loss due to periodontal disease (TLPD) in patients following periodontal maintenance (PM), and assess its performance using a multicentre approach. A multilevel analysis of eleven predictors of TLPD in 500 patients following PM was carried out to calculate the probability of TLPD. This algorithm was applied to three different TLPD samples (369 teeth) gathered retrospectively by nine periodontist, associating several intervals of probability with the corresponding survival rates, based on significant differences in the mean survival rates. The reproducibility of these associations was assessed in each sample (One-way ANOVA and pair-wise comparison with Bonferroni corrections). The model presented high specificity and moderate sensitivity, with optimal calibration and discrimination measurements. Seven intervals of probability were associated with seven survival rates and these associations contained close to 80% of the cases: the probability predicted the survival rate at this percentage. The model performed well in the three samples, since the mean survival rates of each association were significantly different within each sample, while no significant differences between the samples were found in pair-wise comparisons of means. This model might be useful for predicting survival rates in different TLPD samples This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  8. A Bayesian semiparametric multilevel survival modelling of age at first birth in Nigeria

    Directory of Open Access Journals (Sweden)

    Ezra Gayawan

    2013-06-01

    Full Text Available BACKGROUND The age at which childbearing begins influences the total number of children a woman bears throughout her reproductive period, in the absence of any active fertility control. For countries in sub-Saharan Africa where contraceptive prevalence rate is still low, younger ages at first birth tend to increase the number of children a woman will have thereby hindering the process of fertility decline. Research has also shown that early childbearing can endanger the health of the mother and her offspring, which can in turn lead to high child and maternal mortality. OBJECTIVE In this paper, an attempt was made to explore possible trends, geographical variation and determinants of timing of first birth in Nigeria, using the 1999 - 2008 Nigeria Demographic and Health Survey data sets. METHODS A structured additive survival model for continuous time data, an approach that simultaneously estimates the nonlinear effect of metrical covariates, fixed effects, spatial effects and smoothing parameters within a Bayesian context in one step is employed for all estimations. All analyses were carried out using BayesX - a software package for Bayesian modelling techniques. RESULTS Results from this paper reveal that variation in age at first birth in Nigeria is determined more by individual household than by community, and that substantial geographical variations in timing of first birth also exist. COMMENTS These findings can guide policymakers in identifying states or districts that are associated with significant risk of early childbirth, which can in turn be used in designing effective strategies and in decision making.

  9. The association between timing of initiation of adjuvant therapy and the survival of early stage ovarian cancer patients - An analysis of NRG Oncology/Gynecologic Oncology Group trials.

    Science.gov (United States)

    Chan, John K; Java, James J; Fuh, Katherine; Monk, Bradley J; Kapp, Daniel S; Herzog, Thomas; Bell, Jeffrey; Young, Robert

    2016-12-01

    To determine the association between timing of adjuvant therapy initiation and survival of early stage ovarian cancer patients. Data were obtained from women who underwent primary surgical staging followed by adjuvant therapy from two Gynecologic Oncology Group trials (protocols # 95 and 157). Kaplan-Meier estimates and Cox proportional hazards model adjusted for covariates were used for analyses. Of 497 stage I-II epithelial ovarian cancer patients, the median time between surgery and initiation of adjuvant therapy was 23days (25th-75th%: 12-33days). The time interval from surgery to initiation of adjuvant therapy was categorized into three groups: 4weeks. The corresponding 5-year recurrence-free survival rates were 72.8%, 73.9%, and 79.5% (p=0.62). The 5-year overall survival rates were 79.4%, 81.9%, and 82.8%, respectively (p=0.51; p=0.33 - global test). As compared to 4weeks. Age, stage, grade, and cytology were important prognostic factors. Timing of adjuvant therapy initiation was not associated with survival in early stage epithelial ovarian cancer patients. Copyright © 2016 Elsevier Inc. All rights reserved.

  10. Global and national TIMES models

    DEFF Research Database (Denmark)

    Grohnheit, Poul Erik; Karlsson, Kenneth Bernard; Balyk, Olexandr

    of the ETSAP tools is linked to many other projects focusing on model application worldwide. This includes the organisations and institutions gathering in the annual International Energy Workshops (IEW), which are held back-to-back with one of the ETSAP semi-annual workshops. In recent years the ETSAP......An important part of the cooperation within the IEA (International Energy Agency) is organised through national contributions to ”Implementing Agreements” on energy technology and energy analyses. One of them is ETSAP (Energy Technology Systems Analysis Programme), started in 1976. Denmark has...... signed the agreement and contributed to some early annexes. This document is the final report of the project ”Danish participation in IEA-ETSAP, Annex XII, 2011-2013” under the Danish Energy Technology Development and Demonstration Programme (EUDP) 2010. A first complete draft of the ETSAP final report...

  11. Multilevel modelling of clustered grouped survival data using Cox regression model: an application to ART dental restorations.

    Science.gov (United States)

    Wong, May C M; Lam, K F; Lo, Edward C M

    2006-02-15

    In some controlled clinical trials in dental research, multiple failure time data from the same patient are frequently observed that result in clustered multiple failure time. Moreover, the treatments are often delivered by more than one operator and thus the multiple failure times are clustered according to a multilevel structure when the operator effects are assumed to be random. In practice, it is often too expensive or even impossible to monitor the study subjects continuously, but they are examined periodically at some regular pre-scheduled visits. Hence, discrete or grouped clustered failure time data are collected. The aim of this paper is to illustrate the use of the Monte Carlo Markov chain (MCMC) approach and non-informative prior in a Bayesian framework to mimic the maximum likelihood (ML) estimation in a frequentist approach in multilevel modelling of clustered grouped survival data. A three-level model with additive variance components model for the random effects is considered in this paper. Both the grouped proportional hazards model and the dynamic logistic regression model are used. The approximate intra-cluster correlation of the log failure times can be estimated when the grouped proportional hazards model is used. The statistical package WinBUGS is adopted to estimate the parameter of interest based on the MCMC method. The models and method are applied to a data set obtained from a prospective clinical study on a cohort of Chinese school children that atraumatic restorative treatment (ART) restorations were placed on permanent teeth with carious lesions. Altogether 284 ART restorations were placed by five dentists and clinical status of the ART restorations was evaluated annually for 6 years after placement, thus clustered grouped failure times of the restorations were recorded. Results based on the grouped proportional hazards model revealed that clustering effect among the log failure times of the different restorations from the same child was

  12. Dietary magnesium and copper affect survival time and neuroinflammation in chronic wasting disease.

    Science.gov (United States)

    Nichols, Tracy A; Spraker, Terry R; Gidlewski, Thomas; Cummings, Bruce; Hill, Dana; Kong, Qingzhong; Balachandran, Aru; VerCauteren, Kurt C; Zabel, Mark D

    2016-05-03

    Chronic wasting disease (CWD), the only known wildlife prion disease, affects deer, elk and moose. The disease is an ongoing and expanding problem in both wild and captive North American cervid populations and is difficult to control in part due to the extreme environmental persistence of prions, which can transmit disease years after initial contamination. The role of exogenous factors in CWD transmission and progression is largely unexplored. In an effort to understand the influence of environmental and dietary constituents on CWD, we collected and analyzed water and soil samples from CWD-negative and positive captive cervid facilities, as well as from wild CWD-endozootic areas. Our analysis revealed that, when compared with CWD-positive sites, CWD-negative sites had a significantly higher concentration of magnesium, and a higher magnesium/copper (Mg/Cu) ratio in the water than that from CWD-positive sites. When cevidized transgenic mice were fed a custom diet devoid of Mg and Cu and drinking water with varied Mg/Cu ratios, we found that higher Mg/Cu ratio resulted in significantly longer survival times after intracerebral CWD inoculation. We also detected reduced levels of inflammatory cytokine gene expression in mice fed a modified diet with a higher Mg/Cu ratio compared to those on a standard rodent diet. These findings indicate a role for dietary Mg and Cu in CWD pathogenesis through modulating inflammation in the brain.

  13. Long-Time Survival of a Patient with Metastatic Pancreatic Cancer: A Case Report

    Directory of Open Access Journals (Sweden)

    Željko Soldić

    2011-08-01

    Full Text Available Pancreatic cancer is a malignant neoplasm of the pancreas. It does not cause any symptoms in the early stage, and later symptoms are nonspecific, thus the disease is usually diagnosed when already advanced. In 2008, pancreatic cancer ranked eighth on the list of the 10 most common cancers among men in Croatia and tenth on the list of the most common cancers among Croatian women. Pancreatic cancer has a poor prognosis, with a survival time of only 6–8 months for metastatic disease. Gemcitabine is the standard chemotherapeutic option. Other chemotherapeutic agents include5-fluorouracil and leucovorin. In this paper, we present a case of a patient diagnosed with locally advanced and metastatic pancreatic cancer, who is still alive and currently receives his fourth line of chemotherapy 5 years after the diagnosis. Following disease progression on gemcitabine chemotherapy, he was treated with chemoradiotherapy which, however, had no effect. We then applied cisplatin monochemotherapy which offered excellent disease control, was well tolerated by the patient and, although somewhat obsolete in this form, showed to be a valuable chemotherapeutic option.

  14. ANP AFFECTS CARDIAC REMODELING, FUNCTION, HEART FAILURE AND SURVIVAL IN A MOUSE MODEL OF DILATED CARDIOMYOPATHY

    OpenAIRE

    Wang, Dong; Gladysheva, Inna P.; Fan, Tai-Hwang M.; Sullivan, Ryan; Houng, Aiilyan K.; Reed, Guy L.

    2013-01-01

    Dilated cardiomyopathy is a frequent cause of heart failure and death. Atrial natriuretic peptide (ANP) is a biomarker of dilated cardiomyopathy, but there is controversy whether ANP modulates the development of heart failure. Therefore we examined whether ANP affects heart failure, cardiac remodeling, function and survival in a well-characterized, transgenic model of dilated cardiomyopathy. Mice with dilated cardiomyopathy with normal ANP levels survived longer than mice with partial ANP (p

  15. Time trends in population-based breast cancer survival in Estonia: analysis by age and stage.

    Science.gov (United States)

    Baburin, Aleksei; Aareleid, Tiiu; Padrik, Peeter; Valvere, Vahur; Innos, Kaire

    2014-02-01

    Survival from breast cancer (BC) in Estonia has been consistently among the lowest in Europe. The aim of this study was to examine most recent trends in BC survival in Estonia by age and stage. The trends in overall BC incidence and mortality are also shown in the paper. Estonian Cancer Registry data on all cases of BC, diagnosed in women in Estonia during 1995-2007 (n = 7424) and followed up for vital status through 2009, were used to estimate relative survival ratios (RSR). Period hybrid approach was used to obtain the most recent estimates (2005-2009). Stage was classified as localized, local/regional spread or distant. BC incidence continued to rise throughout the study period, but mortality has been in steady decline since 2000. The distribution of patients shifted towards older age and earlier stage at diagnosis. Overall age-standardized five-year RSR increased from 63% in 1995-1999 to 74% in 2005-2009. Younger age groups experienced a more rapid improvement compared to women over 60. Significant survival increase was observed for both localized and locally/regionally spread BC with five-year RSRs reaching 96% and 70% in 2005-2009, respectively; the latest five-year RSR for distant BC was 11%. Survival for T4 tumors was poor and large age difference was seen for locally/regionally spread BC. Considerable improvement in BC survival was observed over the study period. Women under 60 benefited most from both earlier diagnosis and treatment advances of locally/regionally spread cancers. However, the survival gap with more developed countries persists. Further increase in survival, but also decline in BC mortality in Estonia could be achieved by facilitating early diagnosis in all age groups, but particularly among women over 60. Investigations should continue to clarify the underlying mechanisms of the stage-specific survival deficit in Estonia.

  16. 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.

  17. 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

    concentrations is regarded important for survival. Intense aggregations of zooplankton in near-surface waters provide these conditions for larval fish. Simulation studies by individual-based modeling can help understanding of the mechanisms for survival during early life-stages. In this study, we examined how...... growth and survival of larvae and early juveniles of Lesser Sandeel (Ammodytes marinus) in the North Sea are influenced by availability and patchiness of the planktonic prey by adapting and applying a generic bioenergetic individual-based model for larval fish. Input food conditions were generated...... 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...

  18. Surviving the present: Modeling tools for organizational change

    Energy Technology Data Exchange (ETDEWEB)

    Pangaro, P. (Pangaro Inc., Washington, DC (United States))

    1992-01-01

    The nuclear industry, like the rest of modern American business, is beset by a confluence of economic, technological, competitive, regulatory, and political pressures. For better or worse, business schools and management consultants have leapt to the rescue, offering the most modern conveniences that they can purvey. Recent advances in the study of organizations have led to new tools for their analysis, revision, and repair. There are two complementary tools that do not impose values or injunctions in themselves. One, called the organization modeler, captures the hierarchy of purposes that organizations and their subparts carry out. Any deficiency or pathology is quickly illuminated, and requirements for repair are made clear. The second, called THOUGHTSTICKER, is used to capture the semantic content of the conversations that occur across the interactions of parts of an organization. The distinctions and vocabulary in the language of an organization, and the relations within that domain, are elicited from the participants so that all three are available for debate and refinement. The product of the applications of these modeling tools is not the resulting models but rather the enhancement of the organization as a consequence of the process of constructing them.

  19. Time to adjuvant chemotherapy following colorectal cancer resection is associated with an improved survival.

    Science.gov (United States)

    Day, A R; Middleton, G; Smith, R V P; Jourdan, I C; Rockall, T A

    2014-05-01

    Multicentre randomized trials have demonstrated equivalent long-term outcomes for open and laparoscopic resection of colon cancer. Some studies have indicated a possible survival advantage in certain patients undergoing laparoscopic resection. Patients who receive adjuvant chemotherapy in improved survival. Data were collated for patients having an elective laparoscopic or open resection for non-metastatic colorectal cancer between October 2003 and December 2010 and subsequently having adjuvant chemotherapy. Survival analysis was conducted. In all, 209 patients received adjuvant chemotherapy following open (n = 76) or laparoscopic (n = 133) surgery. Median length of stay was 3 days with laparoscopic resection and 6 days with open resection (P chemotherapy was 52 with laparoscopic resection and 58 with open resection (P = 0.008). The 5-year overall survival was 89.6% in patients receiving chemotherapy in chemotherapy chemotherapy. Colorectal Disease © 2014 The Association of Coloproctology of Great Britain and Ireland.

  20. 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....

  1. Formal Modeling and Analysis of Timed Systems

    DEFF Research Database (Denmark)

    Larsen, Kim Guldstrand; Niebert, Peter

    of two invited talks were carefully selected from 36 submissions during two rounds of reviewing and improvement. All current aspects of formal method for modeling and analyzing timed systems are addressed; among the timed systems dealt with are timed automata, timed Petri nets, max-plus algebras, real...

  2. Quasifiltering for time-series modeling

    OpenAIRE

    Tsyplakov, Alexander

    2015-01-01

    In the paper a method for constructing new varieties of time-series models is proposed. The idea is to start from an unobserved components model in a state-space form and use it as an inspiration for development of another time-series model, in which time-varying underlying variables are directly observed. The goal is to replace a state-space model with an intractable likelihood function by another model, for which the likelihood function can be written in a closed form. If state transition e...

  3. Breast cancer and leptomeningeal disease (LMD): hormone receptor status influences time to development of LMD and survival from LMD diagnosis.

    Science.gov (United States)

    Yust-Katz, S; Garciarena, P; Liu, D; Yuan, Y; Ibrahim, N; Yerushalmi, R; Penas-Prado, M; Groves, M D

    2013-09-01

    Leptomeningeal disease (LMD) occurs in 5 % of breast cancer patients. The aim of this study was to identify risk factors related to survival and time to development of LMD in breast cancer patients. A retrospective analysis of breast cancer patients with LMD, evaluated in MDACC between 1995 and 2011. 103 patients with diagnosis of breast cancer and LMD were identified (one male). The median age at LMD diagnosis was 49.2 years. 78.2 % had invasive ductal carcinoma. Hormone receptors (HRs) were positive in 55.3 % of patients, 47.4 % were human epidermal growth factor receptor 2-positive and 22.8 % were triple negative. 52 % of the patients were treated with WBRT, 19 % with spinal radiation, 36 % with systemic chemotherapy and 55 % with intrathecal chemotherapy. Estimated median overall survival from time of breast cancer diagnosis was 3.66 years. Median survival from time of LMD diagnosis was 4.2 months. Time from breast cancer diagnosis to LMD was 2.48 years. In multivariate analysis, HR status and stage at diagnosis were significantly associated with time to LMD diagnosis (p < 0.05). In triple negative patients, time to LMD was shorter. In patients who were HR positive, time to LMD was longer. Survival from LMD diagnosis was significantly associated with both treatment, as well as positive HR status (multivariate analysis p < 0.05). In conclusion LMD has dismal prognosis in breast cancer patients. HR status contributes to time to LMD diagnosis and survival from LMD diagnosis. The impact of treatment aimed at LMD cannot be ascertained in our retrospective study due to the inherent bias associated with the decision to treat.

  4. 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 (Pdiabetes mellitus in the German Statutory Health Insurance showed a significant benefit in survival time. They also incurred lower costs compared to propensity score matched insured in routine care.

  5. Development of prognostic model for predicting survival after retrograde placement of ureteral stent in advanced gastrointestinal cancer patients and its evaluation by decision curve analysis.

    Science.gov (United States)

    Kawano, Shingo; Komai, Yoshinobu; Ishioka, Junichiro; Sakai, Yasuyuki; Fuse, Nozomu; Ito, Masaaki; Kihara, Kazunori; Saito, Norio

    2016-10-01

    The aim of this study was to determine risk factors for survival after retrograde placement of ureteral stents and develop a prognostic model for advanced gastrointestinal tract (GIT: esophagus, stomach, colon and rectum) cancer patients. We examined the clinical records of 122 patients who underwent retrograde placement of a ureteral stent against malignant extrinsic ureteral obstruction. A prediction model for survival after stenting was developed. We compared its clinical usefulness with our previous model based on the results from nephrostomy cases by decision curve analysis. Median follow-up period was 201 days (8-1490) and 97 deaths occurred. The 1-year survival rate in this cohort was 29%. Based on multivariate analysis, primary site of colon origin, absence of retroperitoneal lymph node metastasis and serum albumin >3g/dL were significantly associated with a prolonged survival time. To develop a prognostic model, we divided the patients into 3 risk groups of favorable: 0-1 factors (N.=53), intermediate: 2 risk factors (N.=54), and poor: 3 risk factors (N.=15). There were significant differences in the survival profiles of these 3 risk groups (P<0.0001). Decision curve analyses revealed that the current model has a superior net benefit than our previous model for most of the examined probabilities. We have developed a novel prognostic model for GIT cancer patients who were treated with retrograde placement of a ureteral stent. The current model should help urologists and medical oncologists to predict survival in cases of malignant extrinsic ureteral obstruction.

  6. Predictability of survival models for waiting list and transplant patients: calculating LYFT.

    Science.gov (United States)

    Wolfe, R A; McCullough, K P; Leichtman, A B

    2009-07-01

    'Life years from transplant' (LYFT) is the extra years of life that a candidate can expect to achieve with a kidney transplant as compared to never receiving a kidney transplant at all. The LYFT component survival models (patient lifetimes with and without transplant, and graft lifetime) are comparable to or better predictors of long-term survival than are other predictive equations currently in use for organ allocation. Furthermore, these models are progressively more successful at predicting which of two patients will live longer as their medical characteristics (and thus predicted lifetimes) diverge. The C-statistics and the correlations for the three LYFT component equations have been validated using independent, nonoverlapping split-half random samples. Allocation policies based on these survival models could lead to substantial increases in the number of life years gained from the current donor pool.

  7. 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

  8. Mental health selection and income support dynamics: multiple spell discrete-time survival analyses of welfare receipt.

    Science.gov (United States)

    Kiely, Kim M; Butterworth, Peter

    2014-04-01

    The higher occurrence of common psychiatric disorders among welfare recipients has been attributed to health selection, social causation and underlying vulnerability. The aims of this study were to test for the selection effects of mental health problems on entry and re-entry to working-age welfare payments in respect to single parenthood, unemployment and disability. Nationally representative longitudinal data were drawn from the Household Income and Labour Dynamics in Australia survey. Multiple spell discrete-time survival analyses were conducted using multinomial logistic regression models to test if pre-existing mental health problems predicted transitions to welfare. Analyses were stratified by sex and multivariate adjusted for mental health problems, father's occupation, socioeconomic position, marital status, employment history, smoking status and alcohol consumption, physical function and financial hardship. All covariates were modelled as either lagged effects or when a respondent was first observed to be at risk of income support. Mental health problems were associated with increased risk of entry and re-entry to disability, unemployment and single parenting payments for women, and disability and unemployment payments for men. These associations were attenuated but remained significant after adjusting for contemporaneous risk factors. Although we do not control for reciprocal causation, our findings are consistent with a health selection hypothesis and indicate that mental illness may be a contributing factor to later receipt of different types of welfare payments. We argue that mental health warrants consideration in the design and targeting of social and economic policies.

  9. Differences in the timing of cardio-respiratory development determine whether marine gastropod embryos survive or die in hypoxia.

    Science.gov (United States)

    Rudin-Bitterli, Tabitha S; Spicer, John I; Rundle, Simon D

    2016-04-01

    Physiological plasticity of early developmental stages is a key way by which organisms can survive and adapt to environmental change. We investigated developmental plasticity of aspects of the cardio-respiratory physiology of encapsulated embryos of a marine gastropod, Littorina obtusata, surviving exposure to moderate hypoxia (PO2 =8 kPa) and compared the development of these survivors with that of individuals that died before hatching. Individuals surviving hypoxia exhibited a slower rate of development and altered ontogeny of cardio-respiratory structure and function compared with normoxic controls (PO2 >20 kPa). The onset and development of the larval and adult hearts were delayed in chronological time in hypoxia, but both organs appeared earlier in developmental time and cardiac activity rates were greater. The velum, a transient, 'larval' organ thought to play a role in gas exchange, was larger in hypoxia but developed more slowly (in chronological time), and velar cilia-driven, rotational activity was lower. Despite these effects of hypoxia, 38% of individuals survived to hatching. Compared with those embryos that died during development, these surviving embryos had advanced expression of adult structures, i.e. a significantly earlier occurrence and greater activity of their adult heart and larger shells. In contrast, embryos that died retained larval cardio-respiratory features (the velum and larval heart) for longer in chronological time. Surviving embryos came from eggs with significantly higher albumen provisioning than those that died, suggesting an energetic component for advanced development of adult traits. © 2016. Published by The Company of Biologists Ltd.

  10. 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.

  11. Time trends in incidence rates and survival of newly diagnosed stage IV breast cancer by tumor histology: a population-based analysis.

    Science.gov (United States)

    Di Meglio, Antonio; Freedman, Rachel A; Lin, Nancy U; Barry, William T; Metzger-Filho, Otto; Keating, Nancy L; King, Tari A; Sertoli, Mario Roberto; Boccardo, Francesco; Winer, Eric P; Vaz-Luis, Ines

    2016-06-01

    Few contemporary data are available that compare incidence and survival of metastatic breast cancer between ductal and lobular carcinomas. Using the Surveillance, Epidemiology, and End Results-9 registries, we identified 10,639 patients with de novo metastatic breast cancer diagnosed from 1990 to 2011. Annual age-adjusted incidence rates and annual percent changes (APCs) were analyzed. Multivariable Cox regression models were used to investigate the impact of year of diagnosis and histology on overall survival. 9250 (86.9 %) patients had ductal and 1389 (13.1 %) had lobular carcinomas. Metastatic breast cancer incidence increased slightly over time for ductal (APC = +1.7, 95 % confidence interval (CI) = +1.0 to +2.4) and lobular carcinomas (APC = +3.0, 95 % CI = +1.8 to +4.3). Median overall survival was 22 months among the whole cohort. More recent year of diagnosis was associated with better overall survival only for patients with ductal carcinomas (interaction p value = 0.006), with an adjusted hazard ratio of death for every five-year increment in the date of diagnosis of 0.93 (95 % CI =  0.91-0.95) among ductal carcinomas, compared with 1.05 (95 % CI = 0.95-1.10) among lobular carcinomas. Overall survival was longer for lobular versus ductal carcinomas (28 versus 21 months, respectively; adjusted hazard ratio of death = 0.93, 95 % CI = 0.87-0.99), but the magnitude of this effect was attenuated among the cohort restricted to hormone receptor-positive tumors. In this population-based analysis, incidence rates of metastatic breast cancer at presentation increased slightly over time for both histologies, and particularly for lobular tumors. A modest improvement in metastatic breast cancer median overall survival was observed, but was apparently limited to ductal carcinomas.

  12. Moderate exercise training improves survival and ventricular remodeling in an animal model of left ventricular volume overload.

    Science.gov (United States)

    Lachance, Dominic; Plante, Eric; Bouchard-Thomassin, Andrée-Anne; Champetier, Serge; Roussel, Elise; Drolet, Marie-Claude; Arsenault, Marie; Couet, Jacques

    2009-09-01

    Exercise training has beneficial effects in patients with heart failure, although there is still no clear evidence that it may impact on their survival. There are no data regarding the effects of exercise in subjects with chronic left ventricular (LV) volume overload. Using a rat model of severe aortic valve regurgitation (AR), we studied the effects of long-term exercise training on survival, development of heart failure, and LV myocardial remodeling. One hundred sixty male adult rats were divided in 3 groups: sham sedentary (n=40), AR sedentary (n=80), and AR trained (n=40). Training consisted in treadmill running for up to 30 minutes, 5 times per week for 9 months, at a maximal speed of 20 m/minute. All sham-operated animals survived the entire course of the protocol. After 9 months, 65% of trained animals were alive compared with 46% of sedentary ones (P=0.05). Ejection fractions remained in the normal range (all above 60%) and LV masses between AR groups were similar. There was significantly less LV fibrosis in the trained group and lower LV filling pressures and improved echocardiographic diastolic parameters. Heart rate variability was also improved by exercise. Our data show that moderate endurance training is safe, does not increase the rate of developing heart failure, and most importantly, improves survival in this animal model of chronic LV volume overload. Exercise improved LV diastolic function, heart rate variability, and reduced myocardial fibrosis.

  13. Gamma Knife Surgery as Monotherapy with Clinically Relevant Doses Prolongs Survival in a Human GBM Xenograft Model

    Directory of Open Access Journals (Sweden)

    Bente Sandvei Skeie

    2013-01-01

    Full Text Available Object. Gamma knife surgery (GKS may be used for recurring glioblastomas (GBMs. However, patients have then usually undergone multimodal treatment, which makes it difficult to specifically validate GKS independent of established treatments. Thus, we developed an experimental brain tumor model to assess the efficacy and radiotoxicity associated with GKS. Methods. GBM xenografts were implanted intracerebrally in nude rats, and engraftment was confirmed with MRI. The rats were allocated to GKS, with margin doses of 12 Gy or 18 Gy, or to no treatment. Survival time was recorded, tumor sections were examined, and radiotoxicity was evaluated in a behavioral open field test. Results. In the first series, survival from the time of implantation was 96 days in treated rats and 72 days in controls (P<0.001. In a second experiment, survival was 72 days in the treatment group versus 54 days in controls (P<0.006. Polynuclear macrophages and fibrosis was seen in groups subjected to GKS. Untreated rats with GBM xenografts displayed less mobility than GKS-treated animals in the open field test 4 weeks after treatment (P=0.04. Conclusion. GKS administered with clinically relevant doses prolongs survival in rats harboring GBM xenografts, and the associated toxicity is mild.

  14. Identifying an automaton model for timed data

    NARCIS (Netherlands)

    Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.

    2006-01-01

    A model for discrete event systems (DES) can be learned from observations. We propose a simple type of timed automaton to model DES where the timing of the events is important. Learning such an automaton is proven to be NP-complete by a reduction from the problem of learning deterministic finite

  15. Discounting Models for Outcomes over Continuous Time

    DEFF Research Database (Denmark)

    Harvey, Charles M.; Østerdal, Lars Peter

    Events that occur over a period of time can be described either as sequences of outcomes at discrete times or as functions of outcomes in an interval of time. This paper presents discounting models for events of the latter type. Conditions on preferences are shown to be satisfied if and only...... if the preferences are represented by a function that is an integral of a discounting function times a scale defined on outcomes at instants of time....

  16. 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

  17. Interleukin-7 ameliorates immune dysfunction and improves survival in a 2-hit model of fungal sepsis.

    Science.gov (United States)

    Unsinger, Jacqueline; Burnham, Carey-Ann D; McDonough, Jacquelyn; Morre, Michel; Prakash, Priya S; Caldwell, Charles C; Dunne, W Michael; Hotchkiss, Richard S

    2012-08-15

    Secondary hospital-acquired fungal infections are common in critically-ill patients and mortality remains high despite antimicrobial therapy. Interleukin-7 (IL-7) is a potent immunotherapeutic agent that improves host immunity and has shown efficacy in bacterial and viral models of infection. This study examined the ability of IL-7, which is currently in multiple clinical trials (including hepatitis and human immunodeficiency virus), to improve survival in a clinically relevant 2-hit model of fungal sepsis. Mice underwent cecal ligation and puncture to induce peritonitis. Four days later, surviving mice had intravenous injection with Candida albicans. Following Candida infection, mice were treated with IL-7 or saline control. The effect of IL-7 on host immunity and survival was recorded. IL-7 ameliorated the loss of immune effector cells and increased lymphocyte functions, including activation, proliferation, expression of adhesion molecules, and interferon-γ production. These beneficial effects of IL-7 were associated with an increase in global immunity as reflected by an enhanced delayed type hypersensitivity response and a 1.7-fold improvement in survival. The present findings showing that IL-7 improves survival in fungal sepsis, together with its previously reported efficacy in bacterial and viral infectious models, further supports its use as a novel immunotherapeutic in sepsis.

  18. The NEAT Predictive Model for Survival in Patients with Advanced Cancer.

    Science.gov (United States)

    Zucker, Amanda; Tsai, Chiaojung Jillian; Loscalzo, John; Calves, Pedro; Kao, Johnny

    2018-01-24

    We previously developed a model to more accurately predict life expectancy for stage IV cancer patients referred to radiation oncology. The goals of this study are to validate this model and to compare competing published models. From May 2012 to March 2015, 280 consecutive patients with stage IV cancer were prospectively evaluated by a single radiation oncologist. Patients were separated into training, validation and combined sets. The NEAT model evaluated number of active tumors ("N"), Eastern Cooperative Oncology Group (ECOG) performance status ("E"), albumin ("A") and primary tumor site ("T"). The Odette Cancer Center model validated performance status, bone only metastases and primary tumor site. The Harvard TEACHH model investigated primary tumor type, performance status, age, prior chemotherapy courses, liver metastases, and hospitalization within 3 months. Cox multivariable analyses and logistical regression were utilized to compare model performance. Number of active tumors, performance status, albumin, primary tumor site, prior hospitalization within the last 3 months and liver metastases predicted overall survival on uinvariate and multivariable analysis (pNEAT model separated patients into 4 prognostic groups with median survivals of 24.9, 14.8, 4.0, and 1.2 months, respectively (pNEAT model had a C-index of 0.76 with a Nagelkerke's R2 of 0.54 suggesting good discrimination, calibration and total performance. The NEAT model warrants further investigation as a clinically useful approach to predict survival in patients with stage IV cancer.

  19. Selective Maintenance Model Considering Time Uncertainty

    OpenAIRE

    Le Chen; Zhengping Shu; Yuan Li; Xuezhi Lv

    2012-01-01

    This study proposes a selective maintenance model for weapon system during mission interval. First, it gives relevant definitions and operational process of material support system. Then, it introduces current research on selective maintenance modeling. Finally, it establishes numerical model for selecting corrective and preventive maintenance tasks, considering time uncertainty brought by unpredictability of maintenance procedure, indetermination of downtime for spares and difference of skil...

  20. 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.

  1. 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.

  2. Integrated population modeling reveals the impact of climate on the survival of juvenile emperor penguins.

    Science.gov (United States)

    Abadi, Fitsum; Barbraud, Christophe; Gimenez, Olivier

    2017-03-01

    Early-life demographic traits are poorly known, impeding our understanding of population processes and sensitivity to climate change. Survival of immature individuals is a critical component of population dynamics and recruitment in particular. However, obtaining reliable estimates of juvenile survival (i.e., from independence to first year) remains challenging, as immatures are often difficult to observe and to monitor individually in the field. This is particularly acute for seabirds, in which juveniles stay at sea and remain undetectable for several years. In this work, we developed a Bayesian integrated population model to estimate the juvenile survival of emperor penguins (Aptenodytes forsteri), and other demographic parameters including adult survival and fecundity of the species. Using this statistical method, we simultaneously analyzed capture-recapture data of adults, the annual number of breeding females, and the number of fledglings of emperor penguins collected at Dumont d'Urville, Antarctica, for the period 1971-1998. We also assessed how climate covariates known to affect the species foraging habitats and prey [southern annular mode (SAM), sea ice concentration (SIC)] affect juvenile survival. Our analyses revealed that there was a strong evidence for the positive effect of SAM during the rearing period (SAMR) on juvenile survival. Our findings suggest that this large-scale climate index affects juvenile emperor penguins body condition and survival through its influence on wind patterns, fast ice extent, and distance to open water. Estimating the influence of environmental covariates on juvenile survival is of major importance to understand the impacts of climate variability and change on the population dynamics of emperor penguins and seabirds in general and to make robust predictions on the impact of climate change on marine predators. © 2016 John Wiley & Sons Ltd.

  3. External validation of a 5-year survival prediction model after elective abdominal aortic aneurysm repair.

    Science.gov (United States)

    DeMartino, Randall R; Huang, Ying; Mandrekar, Jay; Goodney, Philip P; Oderich, Gustavo S; Kalra, Manju; Bower, Thomas C; Cronenwett, Jack L; Gloviczki, Peter

    2017-08-11

    The benefit of prophylactic repair of abdominal aortic aneurysms (AAAs) is based on the risk of rupture exceeding the risk of death from other comorbidities. The purpose of this study was to validate a 5-year survival prediction model for patients undergoing elective repair of asymptomatic AAA .05 indicating goodness of fit). Across different populations of patients, assessment of age and level of cardiac, pulmonary, and renal disease can accurately predict 5-year survival in patients with AAA <6.5 cm undergoing repair. This risk prediction model is a valid method to assess mortality risk in determining potential overall survival benefit from elective AAA repair. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

  4. Evaluation of the Risk of Relapse in Classical Hodgkin Lymphoma at Event-Free Survival Time Points and Survival Comparison With the General Population in British Columbia.

    Science.gov (United States)

    Hapgood, Greg; Zheng, Yvonne; Sehn, Laurie H; Villa, Diego; Klasa, Richard; Gerrie, Alina S; Shenkier, Tamara; Scott, David W; Gascoyne, Randy D; Slack, Graham W; Parsons, Christina; Morris, James; Pickles, Tom; Connors, Joseph M; Savage, Kerry J

    2016-07-20

    Studies in classical Hodgkin lymphoma (cHL) typically measure the time to events from diagnosis. We evaluated the risk of relapse at event-free survival time points in cHL and compared the risk of death to expected mortality rates in British Columbia (BC). The BC Cancer Agency Lymphoid Cancer Database was screened to identify all patients age 16 to 69 years diagnosed with cHL between 1989 and 2012 treated with the chemotherapy regimen of doxorubicin, bleomycin, vinblastine, and dacarbazine (or equivalent). We compared the observed mortality to the general population using age-, sex-, and calendar period-generated expected mortality rates from BC life-tables. Relative survival was calculated using a conditional approach and expressed as a standardized mortality ratio of observed-to-expected deaths. One thousand four hundred two patients were identified; 749 patients were male (53%), the median age was 32 years, and 68% had advanced-stage disease. The median follow-up time was 8.4 years. Seventy-two percent of relapses occurred within the first 2 years of diagnosis. For all patients, the 5-year risk of relapse from diagnosis was 18.1% but diminished to 5.6% for patients remaining event free at 2 years. For advanced-stage patients who were event free at 2 years, the 5-year risk of relapse was only 7.6%, and for those who were event free at 3 years, it was comparable to that of limited-stage patients (4.1% v 2.5%, respectively; P = .07). Furthermore, international prognostic score ≥ 4 and bulky disease were no longer prognostic in patients who were event free at 1 year. Although the relative survival improved as patients remained in remission, it did not normalize compared with the general population. Patients with cHL who are event free at 2 years have an excellent outcome regardless of baseline prognostic factors. All patients with cHL had an enduring increased risk of death compared with the general population. © 2016 by American Society of Clinical Oncology.

  5. 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.

  6. Prognostic value of a decreased tongue strength for survival time in patients with amyotrophic lateral sclerosis

    NARCIS (Netherlands)

    Alexander Geurts; J. Weikamp; J. Hendriks; J. Schelhaas; Bert de Swart

    2012-01-01

    Decreased tongue strength (TS) might herald bulbar involvement in patients with amyotrophic lateral sclerosis (ALS) well before dysarthria or dysphagia occur, and as such might be prognostic of short survival. The purpose of this study was to investigate the prognostic value of a decreased TS, in

  7. Prognostic value of decreased tongue strength on survival time in patients with amyotrophic lateral sclerosis

    NARCIS (Netherlands)

    Weikamp, J.G.; Schelhaas, H.J.; Hendriks, J.C.M.; Swart, B.J.M. de; Geurts, A.C.H.

    2012-01-01

    Decreased tongue strength (TS) might herald bulbar involvement in patients with amyotrophic lateral sclerosis (ALS) well before dysarthria or dysphagia occur, and as such might be prognostic of short survival. The purpose of this study was to investigate the prognostic value of a decreased TS, in

  8. Genomic prediction of survival time in a population of brown laying hens showing cannibalistic behavior

    NARCIS (Netherlands)

    Alemu, Setegn W.; Calus, Mario P.L.; Muir, William M.; Peeters, Katrijn; Vereijken, Addie; Bijma, Piter

    2016-01-01

    Background: Mortality due to cannibalism causes both economic and welfare problems in laying hens. To limit mortality due to cannibalism, laying hens are often beak-trimmed, which is undesirable for animal welfare reasons. Genetic selection is an alternative strategy to increase survival and is

  9. Computer based prognosis model with dimensionality reduction and validation of attributes for prolonged survival prediction

    Directory of Open Access Journals (Sweden)

    C.G. Raji

    2017-01-01

    Full Text Available Medical databases contain large volume of data about patients and their clinical information. For extracting the features and their relationships from a huge database, various data mining techniques need to be employed. As Liver transplantation is the curative surgical procedure for the patients suffering from end stage liver disease, predicting the survival rate after Liver transplantation has a big impact. Appropriate selection of attributes and methods are necessary for the survival prediction. Liver transplantation data with 256 attributes were collected from 389 attributes of the United Nations Organ Sharing registry for the survival prediction. Initially 59 attributes were filtered manually, and then Principal Component Analysis (PCA was applied for reducing the dimensionality of the data. After performing PCA, 197 attributes were obtained and they were ranked into 27 strong/relevant attributes. Using association rule mining techniques, the association between the selected attributes was identified and verified. Comparison of rules generated by various association rules mining algorithm before and after PCA was also carried out for affirming the results. The various rule mining algorithms used were Apriori, Treap mining and Tertius algorithms. Among these algorithms, Treap mining algorithm generated the rules with high accuracy. A Multilayer Perceptron model was built for predicting the long term survival of patients after Liver transplantation which produced high accuracy prediction result. The model performance was compared with Radial Basis Function model to prove the accuracy of survival of liver patients'. The top ranked attributes obtained from rule mining were fed to the models for effective training. This ensures that Treap mining generated associations of high impact attributes which in-turn made the survival prediction flawless.

  10. Long-term time trends in incidence, survival and mortality of lymphomas by subtype among adults in Manitoba, Canada: a population-based study using cancer registry data.

    Science.gov (United States)

    Ye, Xibiao; Mahmud, Salaheddin; Skrabek, Pamela; Lix, Lisa; Johnston, James B

    2017-07-17

    To examine 30-year time trends in incidence, survival and mortality of lymphomas by subtype in Manitoba, Canada. Lymphoma cases diagnosed between 1984 and 2013 were classified according to the 2008 WHO classification system for lymphoid neoplasms. Death data (1984-2014) were obtained from the Manitoba Vital Statistics Agency. To examine time trends in incidence and mortality, we used joinpoint regression to estimate annual percentage change and average annual percentage change. Age-period-cohort modelling was conducted to measure the effects of age, period and cohort on incidence and mortality time trends. We estimated age-specific and standardised 5-year relative survival and used Poisson regression model to test time trends in relative survival. Total Hodgkin lymphoma (HL) incidence in men and women was stable during the study period. Age-standardised total non-Hodgkin lymphoma (NHL) incidence increased by 4% annually until around 2000, and the trend varied by sex and NHL subtype. Total HL mortality continuously declined (by 2.5% annually in men and by 2.7% annually in women), while total NHL mortality increased (by 4.4% annually in men until 1998 and by 3.2% annually in women until 2001) and then declined (by 3.6% annually in men and by 2.5% annually in women). Age-standardised 5-year relative survival for HL improved from 72.6% in 1984-1993 to 85.8% in 2004-2013, and for NHL from 57.0% in 1984-1993 to 67.5% in 2004-2013. Survival improvement was also noted for NHL subtypes, although the extent varied, with the greatest improvement for follicular lymphoma (from 65.3% in 1984-1993 to 87.6% in 2004-2013). Time trends were generally consistent with those reported in other jurisdictions in total HL and NHL incidence, but were unique in incidence for HL and for NHL subtypes chronic/small lymphocytic leukaemia/lymphoma, diffuse large B cell lymphoma and follicular lymphoma. Survival improvements and mortality reductions were seen for HL and NHL in both sexes.

  11. The dwell time and survival rates of PICC placement after balloon angioplasty in patient with unexpected central venous obstruction.

    Science.gov (United States)

    Kim, Ki Hyun; Park, Sang Woo; Chang, Il Soo; Yim, Younghee

    2016-09-21

    To evaluate the dwell time and actual survival rates of peripherally inserted central catheter (PICC) placements after balloon angioplasty in patients with unexpected central venous obstructions. Data were obtained on all PICC insertions performed in a tertiary care hospital from August 2008 to December 2013. Thirty-five PICCs attempted after balloon angioplasty in 25 patients (15 male and 10 female patients; mean age, 63 years). Fisher's exact test was used to test for differences in reasons for catheter removal between the groups of patients with stenosis or obstructions. Survival curves for PICC dwell time of all patients, stenosis group, and obstruction group were generated separately using Kaplan-Meier survival analysis and compared with log-rank tests. There were a total 21 obstructions and 14 stenoses. The overall technical success rate of PICC placement after balloon angioplasty was 94% (33 of 35 procedures). The PICC dwell time was determined for 27 PICCs and ranged from 4 to 165 days (mean, 39.6 days). Among all PICCs, 16 were removed early, resulting in an actual survival rate of 40.7% (11 of 27 PICCs). There were no significant differences in reasons for catheter removal between the stenosis and obstruction groups (p = 0.24). The dwell times for both groups were not significantly different by Kaplan-Meier analysis (p = 0.54). PICC placement after balloon angioplasty is a good treatment option for patients with unexpected central venous lesions, and offers high technical success rates. The actual survival rate was relatively lower (40.7%) than that from previous studies.

  12. 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.

  13. [A new perspective of survival data on clinical epidemiology: introduction of competitive risk model].

    Science.gov (United States)

    Nie, Z Q; Ou, Y Q; Qu, Y J; Yuan, H Y; Liu, X Q

    2017-08-10

    Competing risks occur frequently in the analysis of survival data that should be dealt with competing risk models. Competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. Previous commonly used Kaplan-Meier method tends to overestimate the cumulative survival functions, while the traditional Cox proportional hazards model falsely evaluates the effects of covariates on the hazard related to the occurrence of the event. There are few domestic reports mentioning the concept, application and methodology of competing risk model as well as the implementation procedures or resolution of model conditions and parameters. The current work aims to explain the core concept and methodology of the competing risk model and to illustrate the process of analysis on cumulative incidence rate, using both the cause-specific hazard function model and the sub-distribution hazard function model. Software macro code in SAS 9.4 is also provided to assist clinical researchers to further understand the application of the model so to properly analyze the survival data.

  14. Effect of length biased sampling of unobserved sojourn times on the survival distribution when disease is screen detected.

    Science.gov (United States)

    Kafadar, Karen; Prorok, Philip C

    2009-07-20

    Data can arise as a length-biased sample rather than as a random sample; e.g. a sample of patients in hospitals or of network cable lines (experimental units with longer stays or longer lines have greater likelihoods of being sampled). The distribution arising from a single length-biased sampling (LBS) time has been derived (e.g. (The Statistical Analysis of Discrete Time Events. Oxford Press: London, 1972)) and applies when the observed outcome relates to the random variable subjected to LBS. Zelen (Breast Cancer: Trends in Research and Treatment. Raven Press: New York, 1976; 287-301) noted that cases of disease detected from a screening program likewise form a length-biased sample among all cases, since longer sojourn times afford greater likelihoods of being screen detected. In contrast to the samples on hospital stays and cable lines, however, the length-biased sojourns (preclinical durations) cannot be observed, although their subsequent clinical durations (survival times) are. This article quantifies the effect of LBS of the sojourn times (or pre-clinical durations) on the distribution of the observed clinical durations when cases undergo periodic screening for the early detection of disease. We show that, when preclinical and clinical durations are positively correlated, the mean, median, and quartiles of the distribution of the clinical duration from screen-detected cases can be substantially inflated-even in the absence of any benefit on survival from the screening procedure. Screening studies that report mean survival time need to take account of the fact that, even in the absence of any real benefit, the mean survival among cases in the screen-detected group will be longer than that among interval cases or among cases that arise in the control arm, above and beyond lead time bias, simply by virtue of the LBS phenomenon

  15. Conceptual Modeling of Time-Varying Information

    DEFF Research Database (Denmark)

    Gregersen, Heidi; Jensen, Christian Søndergaard

    2004-01-01

    A wide range of database applications manage information that varies over time. Many of the underlying database schemas of these were designed using the Entity-Relationship (ER) model. In the research community as well as in industry, it is common knowledge that the temporal aspects of the mini-world...... are important, but difficult to capture using the ER model. Several enhancements to the ER model have been proposed in an attempt to support the modeling of temporal aspects of information. Common to the existing temporally extended ER models, few or no specific requirements to the models were given...

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

    Science.gov (United States)

    Jacques Regniere; James Powell; Barbara Bentz; Vincent Nealis

    2012-01-01

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

  17. Modeling the Impact of Breast-Feeding by HIV-Infected Women on Child Survival.

    Science.gov (United States)

    Heymann, Sally Jody

    1990-01-01

    Models the survival outcomes of children in developing countries born to women infected with human immunodeficiency virus (HIV) who are breast-fed, bottle-fed, and wet-nursed. Uses decision analysis to assess the relative risk of child mortality from HIV transmission and non-HIV causes associated with different methods of feeding. (FMW)

  18. Time-Weighted Balanced Stochastic Model Reduction

    DEFF Research Database (Denmark)

    Tahavori, Maryamsadat; Shaker, Hamid Reza

    2011-01-01

    A new relative error model reduction technique for linear time invariant (LTI) systems is proposed in this paper. Both continuous and discrete time systems can be reduced within this framework. The proposed model reduction method is mainly based upon time-weighted balanced truncation and a recently...... developed inner-outer factorization technique. Compared to the other analogous counterparts, the proposed method shows to provide more accurate results in terms of time weighted norms, when applied to different practical examples. The results are further illustrated by a numerical example....

  19. Jointly modeling the relationship between longitudinal and survival data subject to left truncation with applications to cystic fibrosis.

    Science.gov (United States)

    Piccorelli, Annalisa V; Schluchter, Mark D

    2012-12-20

    Numerous methods for joint analysis of longitudinal measures of a continuous outcome y and a time to event outcome T have recently been developed either to focus on the longitudinal data y while correcting for nonignorable dropout, to predict the survival outcome T using the longitudinal data y, or to examine the relationship between y and T. The motivating problem for our work is in joint modeling of the serial measurements of pulmonary function (FEV1% predicted) and survival in cystic fibrosis (CF) patients using registry data. Within the CF registry data, an additional complexity is that not all patients have been followed from birth; therefore, some patients have delayed entry into the study while others may have been missed completely, giving rise to a left truncated distribution. This paper shows in joint modeling situations where y and T are not independent, that it is necessary to account for this left truncation to obtain valid parameter estimates related to both survival and the longitudinal marker. We assume a linear random effects model for FEV1% predicted, where the random intercept and slope of FEV1% predicted, along with a specified transformation of the age at death follow a trivariate normal distribution. We develop an expectation-maximization algorithm for maximum likelihood estimation of parameters, which takes left truncation and right censoring of survival times into account. The methods are illustrated using simulation studies and using data from CF patients in a registry followed at Rainbow Babies and Children's Hospital, Cleveland, OH. Copyright © 2012 John Wiley & Sons, Ltd.

  20. Mitochondrial-Based Treatments that Prevent Post-Traumatic Osteoarthritis in a Translational Large Animal Intraarticular Fracture Survival Model

    Science.gov (United States)

    2016-09-01

    Animal Intraarticular Fracture Survival Model PRINCIPAL INVESTIGATOR: James A. Martin, PhD CONTRACTING ORGANIZATION: University of Iowa Iowa City, IA...Post-Traumatic Osteoarthritis in a Translational Large Animal Intraarticular Fracture Survival Model 5b. GRANT NUMBER W81XWH-11-1-0583 5c...traumatic osteoarthritis, large animal model, oxidative stress, mitochondria, mechanotransduction, amobarbital, n-acetyl cysteine 16. SECURITY

  1. Complex karyotype in mantle cell lymphoma is a strong prognostic factor for the time to treatment and overall survival, independent of the MCL international prognostic index.

    Science.gov (United States)

    Sarkozy, Clémentine; Terré, Christine; Jardin, Fabrice; Radford, Isabelle; Roche-Lestienne, Catherine; Penther, Dominique; Bastard, Christian; Rigaudeau, Sophie; Pilorge, Sylvain; Morschhauser, Franck; Bouscary, Didier; Delarue, Richard; Farhat, Hassan; Rousselot, Philippe; Hermine, Olivier; Tilly, Hervé; Chevret, Sylvie; Castaigne, Sylvie

    2014-01-01

    Mantle cell lymphoma (MCL) is usually an aggressive disease. However, a few patients do have an "indolent" evolution (iMCL) defined by a long survival time without intensive therapy. Many studies highlight the prognostic role of additional genetic abnormalities, but these abnormalities are not routinely tested for and do not yet influence the treatment decision. We aimed to evaluate the prognostic impact of these additional abnormalities detected by conventional cytogenetic testing, as well as their relationships with the clinical characteristics and their value in identifying iMCL. All consecutive MCL cases diagnosed between 1995 and 2011 at four institutions were retrospectively selected on the basis of an informative karyotype with a t(11;14) translocation at the time of diagnosis. A total of 125 patients were included and followed for an actual median time of 35 months. The median overall survival (OS) and survival without treatment (TFS) were 73.7 and 1.3 months, respectively. In multivariable Cox models, a high mantle cell lymphoma international prognostic index score, a complex karyotype, and blastoid morphology were independently associated with a shortened OS. Spleen enlargement, nodal presentation, extra-hematological involvement, and complex karyotypes were associated with shorter TFS. A score based on these factors allowed for the identification of "indolent" patients (median TFS 107 months) from other patients (median TFS: 1 month). In conclusion, in this multicentric cohort of MCL patients, a complex karyotype was associated with a shorter survival time and allowed for the identification of iMCL at the time of diagnosis. Copyright © 2013 Wiley Periodicals, Inc.

  2. Simple models based on gamma-glutamyl transpeptidase and platelets for predicting survival in hepatitis B-associated hepatocellular carcinoma.

    Science.gov (United States)

    Pang, Qing; Bi, Jian-Bin; Wang, Zhi-Xin; Xu, Xin-Sen; Qu, Kai; Miao, Run-Chen; Chen, Wei; Zhou, Yan-Yan; Liu, Chang

    2016-01-01

    Several hepatic cirrhosis-derived noninvasive models have been developed to predict the incidence and outcomes of hepatocellular carcinoma (HCC). We aimed to investigate the prognostic significance of the two novel established cirrhosis-associated models based on gamma-glutamyl transpeptidase (GGT) and platelets in hepatitis B-associated HCC. We retrospectively evaluated 182 HCC patients with positive hepatitis B surface antigen who received radical therapy at a single institution between 2002 and 2012. Laboratory data prior to operation were collected to calculate the GGT to platelets ratio (GPR) and the S-index. Predictive factors associated with overall survival and recurrence-free survival were assessed using log-rank test and multivariate Cox analysis. Additional analyses were performed after patients were stratified based on cirrhosis status, tumor size, therapy methods, and so forth, to investigate the prognostic significance in different subgroups. During a median follow-up time of 45.0 months, a total of 88 (48.4%) patients died and 79 (43.4%) patients recurred. The cut-off points for GPR and S-index in predicting death were determined to be 0.76 and 0.56, respectively. Compared with patients with a lower GPR, those with GPR ≥0.76 had a higher probability of cirrhosis and a larger tumor (both P<0.05). GPR and S-index were both found to be significantly associated with survival by univariate log-rank test. Multivariate analysis identified tumor size ≥5 and high level of GPR, but not high Barcelona Clinic Liver Cancer stage or S-index, as independent factors for predicting poor overall survival and recurrence-free survival. The GPR is an effective preoperative predictor for outcomes in hepatitis B-associated HCC.

  3. [Relationships between Serum Albumin and Urea Level and the Clinical Pathological Characteristics and Survival Time in Patients with Lung Cancer].

    Science.gov (United States)

    Li, Yalun; Li, Lei; Zhang, Li; Li, Weimin

    2017-03-20

    Lung cancer is the most common malignancy and is the leading cause of cancer-related death worldwide. Thus, this disease severely threatens human health. This study aims to identify the relationships between serum albumin and urea level and the clinical pathological characteristics and survival time in patients with lung cancer. A total of 1,098 patients with lung cancer were diagnosed by pathology and tested the serum albumin and urea level in West China Hospital of Sichuan University during January 2008 to December 2013. According to the levels of albumin and urea, patients were divided into the normal level group (negative group) and abnormal level group (positive group). The differences of patients' clinical pathological characteristics and survival time in the two groups were analyzed. Differences in age, sex, histological classification, liver metastasis and pleural metastasis were statistically significant between the two groups of serum albumin (Purea. In different histological classification between the two groups of serum albumin, the median survival period of squamous cell carcinoma was 36 months and 19 monthes, adenocarcinoma was 35 months and 15 monthes, the abnormal group were all significantly lower than those in the normal group. The median survival period was no significant difference between the two groups of urea. The level of serum albumin is an important indicator for prognosis.

  4. Role of adipose-derived stromal cells in pedicle skin flap survival in experimental animal models

    Science.gov (United States)

    Foroglou, Pericles; Karathanasis, Vasileios; Demiri, Efterpi; Koliakos, George; Papadakis, Marios

    2016-01-01

    The use of skin flaps in reconstructive surgery is the first-line surgical treatment for the reconstruction of skin defects and is essentially considered the starting point of plastic surgery. Despite their excellent usability, their application includes general surgical risks or possible complications, the primary and most common is necrosis of the flap. To improve flap survival, researchers have used different methods, including the use of adipose-derived stem cells, with significant positive results. In our research we will report the use of adipose-derived stem cells in pedicle skin flap survival based on current literature on various experimental models in animals. PMID:27022440

  5. Survival time outcomes in randomized, controlled trials and meta-analyses: the parallel universes of efficacy and cost-effectiveness.

    Science.gov (United States)

    Guyot, Patricia; Welton, Nicky J; Ouwens, Mario J N M; Ades, A E

    2011-01-01

    Many regulatory agencies require that manufacturers establish both efficacy and cost-effectiveness. The statistical analysis of the randomized, controlled trial (RCT) outcomes should be the same for both purposes. The question addressed by this article is the following: for survival outcomes, what is the relationship between the statistical analyses used to support inference and the statistical model used to support decision making based on cost-effectiveness analysis (CEA)? We performed a review of CEAs alongside trials and CEAs based on a synthesis of RCT results, which were submitted to the National Institute for Health and Clinical Excellence (NICE) Technology Appraisal program and included survival outcomes. We recorded the summary statistics and the statistical models used in both efficacy and cost-effectiveness analyses as well as procedures for model diagnosis and selection. In no case was the statistical model for efficacy and CEA the same. For efficacy, relative risks or Cox regression was used. For CEA, the common practice was to fit a parametric model to the control arm, then to apply the hazard ratio from the efficacy analysis to predict the treatment arm. The proportional hazards assumption was seldom checked; the choice of model was seldom based on formal criteria, and uncertainty in model choice was seldom addressed and never propagated through the model. Both inference and decisions based on CEAs should be based on the same statistical model. This article shows that for survival outcomes, this is not the case. In the interests of transparency, trial protocols should specify a common procedure for model choice for both purposes. Further, the sufficient statistics and the life tables for each arm should be reported to improve transparency and to facilitate secondary analyses of results of RCTs. Copyright © 2011 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

  6. Analysis of error-prone survival data under additive hazards models: measurement error effects and adjustments.

    Science.gov (United States)

    Yan, Ying; Yi, Grace Y

    2016-07-01

    Covariate measurement error occurs commonly in survival analysis. Under the proportional hazards model, measurement error effects have been well studied, and various inference methods have been developed to correct for error effects under such a model. In contrast, error-contaminated survival data under the additive hazards model have received relatively less attention. In this paper, we investigate this problem by exploring measurement error effects on parameter estimation and the change of the hazard function. New insights of measurement error effects are revealed, as opposed to well-documented results for the Cox proportional hazards model. We propose a class of bias correction estimators that embraces certain existing estimators as special cases. In addition, we exploit the regression calibration method to reduce measurement error effects. Theoretical results for the developed methods are established, and numerical assessments are conducted to illustrate the finite sample performance of our methods.

  7. Fluid Regulation and Time Course of Erythropoietin during Multifactorial Strain of Austrian Special Forces Survival Training

    Science.gov (United States)

    2001-03-01

    It seems that EPO production and release is diminished by nutritional factors, i.e. mainly caloric intake, during prolonged physical strain. In the...sign for a haemolytic anaemia with impaired erythropoiesis (20) which would be in line with decreased EPO values found during the survival training...total body iron stores. The classical biochemical constellation of an haemolytic anaemia might be misleading under the described conditions. The body

  8. Bayesian Variable Selection in High Dimensional Survival Time Cancer Genomic Datasets using Nonlocal Priors

    OpenAIRE

    Nikooienejad, Amir; Wang, Wenyi; Johnson, Valen E.

    2017-01-01

    Variable selection in high dimensional cancer genomic studies has become very popular in the past decade, due to the interest in discovering significant genes pertinent to a specific cancer type. Censored survival data is the main data structure in such studies and performing variable selection for such data type requires certain methodology. With recent developments in computational power, Bayesian methods have become more attractive in the context of variable selection. In this article we i...

  9. Application of Survival Analysis and Multistate Modeling to Understand Animal Behavior: Examples from Guide Dogs

    OpenAIRE

    Lucy Asher; Harvey, Naomi D.; Martin Green; England, Gary C.W.

    2017-01-01

    Epidemiology is the study of patterns of health-related states or events in populations. Statistical models developed for epidemiology could be usefully applied to behavioral states or events. The aim of this study is to present the application of epidemiological statistics to understand animal behavior where discrete outcomes are of interest, using data from guide dogs to illustrate. Specifically, survival analysis and multistate modeling are applied to data on guide dogs comparing dogs that...

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

    CERN Document Server

    Ha, Il Do; Lee, Youngjo

    2017-01-01

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

  11. Eight-Year Retrospective Study of the Critical Time Lapse between Root Canal Completion and Crown Placement: Its Influence on the Survival of Endodontically Treated Teeth.

    Science.gov (United States)

    Pratt, Isaac; Aminoshariae, Anita; Montagnese, Thomas A; Williams, Kristin A; Khalighinejad, Navid; Mickel, Andre

    2016-11-01

    The purpose of this study was to investigate the effects of factors associated with various coronal restorative modalities after root canal treatment (RCT) on the survival of endodontically treated teeth (ETT) and to assess the effect of time lapse between RCT and crown placement after RCT to form a tooth loss hazard model. Computerized analysis was performed for all patients who received posterior RCT from 2008 to 2016 in the graduate endodontic department. Data collected included dates of RCT, type of post-endodontic restoration, and time of extraction if extracted. Teeth that received crown after RCT were also divided into 2 groups: receiving crown before 4 months and after 4 months after RCT. Data were analyzed by using Kaplan-Meier log-rank test and Cox regression model (α = 0.05) by using SPPS Statistic 21. Type of restoration after RCT significantly affected the survival of ETT (P = .001). ETT that received composite/amalgam buildup restorations were 2.29 times more likely to be extracted compared with ETT that received crown (hazard ratio, 2.29; confidence interval, 1.29-4.06; P = .005). Time of crown placement after RCT was also significantly correlated with survival rate of ETT (P = .001). Teeth that received crown 4 months after RCT were almost 3 times more likely to get extracted compared with teeth that received crown within 4 months of RCT (hazard ratio, 3.38; confidence interval, 1.56-6.33; P = .002). Patients may benefit by maintaining their natural dentition by timely placement of crown after RCT, which otherwise may have been extracted and replaced by implant because of any delay in crown placement. Copyright © 2016 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

  12. A Twin Protection Effect? Explaining Twin Survival Advantages with a Two-Process Mortality Model.

    Directory of Open Access Journals (Sweden)

    David J Sharrow

    Full Text Available Twin studies that focus on the correlation in age-at-death between twin pairs have yielded important insights into the heritability and role of genetic factors in determining lifespan, but less attention is paid to the biological and social role of zygosity itself in determining survival across the entire life course. Using data from the Danish Twin Registry and the Human Mortality Database, we show that monozygotic twins have greater cumulative survival proportions at nearly every age compared to dizygotic twins and the Danish general population. We examine this survival advantage by fitting these data with a two-process mortality model that partitions survivorship patterns into extrinsic and intrinsic mortality processes roughly corresponding to acute, environmental and chronic, biological origins. We find intrinsic processes confer a survival advantage at older ages for males, while at younger ages, all monozygotic twins show a health protection effect against extrinsic death akin to a marriage protection effect. While existing research suggests an increasingly important role for genetic factors at very advanced ages, we conclude that the social closeness of monozygotic twins is a plausible driver of the survival advantage at ages <65.

  13. A Twin Protection Effect? Explaining Twin Survival Advantages with a Two-Process Mortality Model.

    Science.gov (United States)

    Sharrow, David J; Anderson, James J

    2016-01-01

    Twin studies that focus on the correlation in age-at-death between twin pairs have yielded important insights into the heritability and role of genetic factors in determining lifespan, but less attention is paid to the biological and social role of zygosity itself in determining survival across the entire life course. Using data from the Danish Twin Registry and the Human Mortality Database, we show that monozygotic twins have greater cumulative survival proportions at nearly every age compared to dizygotic twins and the Danish general population. We examine this survival advantage by fitting these data with a two-process mortality model that partitions survivorship patterns into extrinsic and intrinsic mortality processes roughly corresponding to acute, environmental and chronic, biological origins. We find intrinsic processes confer a survival advantage at older ages for males, while at younger ages, all monozygotic twins show a health protection effect against extrinsic death akin to a marriage protection effect. While existing research suggests an increasingly important role for genetic factors at very advanced ages, we conclude that the social closeness of monozygotic twins is a plausible driver of the survival advantage at ages <65.

  14. Modeling Response Signal and Response Time Data

    Science.gov (United States)

    Ratcliff, Roger

    2006-01-01

    The diffusion model (Ratcliff, 1978) and the leaky competing accumulator model (LCA, Usher & McClelland, 2001) were tested against two-choice data collected from the same subjects with the standard response time procedure and the response signal procedure. In the response signal procedure, a stimulus is presented and then, at one of a number of…

  15. Electricity price modeling with stochastic time change

    NARCIS (Netherlands)

    Borovkova, Svetlana; Schmeck, Maren Diane

    2017-01-01

    In this paper, we develop a novel approach to electricity price modeling, based on the powerful technique of stochastic time change. This technique allows us to incorporate the characteristic features of electricity prices (such as seasonal volatility, time varying mean reversion and seasonally

  16. Timed Model Checking of Security Protocols

    NARCIS (Netherlands)

    Corin, R.J.; Etalle, Sandro; Hartel, Pieter H.; Mader, Angelika H.

    We propose a method for engineering security protocols that are aware of timing aspects. We study a simplified version of the well-known Needham Schroeder protocol and the complete Yahalom protocol. Timing information allows the study of different attack scenarios. We illustrate the attacks by model

  17. Prognostic factors for survival in adult patients with recurrent glioblastoma: a decision-tree-based model.

    Science.gov (United States)

    Audureau, Etienne; Chivet, Anaïs; Ursu, Renata; Corns, Robert; Metellus, Philippe; Noel, Georges; Zouaoui, Sonia; Guyotat, Jacques; Le Reste, Pierre-Jean; Faillot, Thierry; Litre, Fabien; Desse, Nicolas; Petit, Antoine; Emery, Evelyne; Lechapt-Zalcman, Emmanuelle; Peltier, Johann; Duntze, Julien; Dezamis, Edouard; Voirin, Jimmy; Menei, Philippe; Caire, François; Dam Hieu, Phong; Barat, Jean-Luc; Langlois, Olivier; Vignes, Jean-Rodolphe; Fabbro-Peray, Pascale; Riondel, Adeline; Sorbets, Elodie; Zanello, Marc; Roux, Alexandre; Carpentier, Antoine; Bauchet, Luc; Pallud, Johan

    2017-11-20

    We assessed prognostic factors in relation to OS from progression in recurrent glioblastomas. Retrospective multicentric study enrolling 407 (training set) and 370 (external validation set) adult patients with a recurrent supratentorial glioblastoma treated by surgical resection and standard combined chemoradiotherapy as first-line treatment. Four complementary multivariate prognostic models were evaluated: Cox proportional hazards regression modeling, single-tree recursive partitioning, random survival forest, conditional random forest. Median overall survival from progression was 7.6 months (mean, 10.1; range, 0-86) and 8.0 months (mean, 8.5; range, 0-56) in the training and validation sets, respectively (p = 0.900). Using the Cox model in the training set, independent predictors of poorer overall survival from progression included increasing age at histopathological diagnosis (aHR, 1.47; 95% CI [1.03-2.08]; p = 0.032), RTOG-RPA V-VI classes (aHR, 1.38; 95% CI [1.11-1.73]; p = 0.004), decreasing KPS at progression (aHR, 3.46; 95% CI [2.10-5.72]; p < 0.001), while independent predictors of longer overall survival from progression included surgical resection (aHR, 0.57; 95% CI [0.44-0.73]; p < 0.001) and chemotherapy (aHR, 0.41; 95% CI [0.31-0.55]; p < 0.001). Single-tree recursive partitioning identified KPS at progression, surgical resection at progression, chemotherapy at progression, and RTOG-RPA class at histopathological diagnosis, as main survival predictors in the training set, yielding four risk categories highly predictive of overall survival from progression both in training (p < 0.0001) and validation (p < 0.0001) sets. Both random forest approaches identified KPS at progression as the most important survival predictor. Age, KPS at progression, RTOG-RPA classes, surgical resection at progression and chemotherapy at progression are prognostic for survival in recurrent glioblastomas and should inform the treatment decisions.

  18. Evaluation of different approaches for modeling Escherichia coli O157:H7 survival on field lettuce.

    Science.gov (United States)

    McKellar, Robin C; Peréz-Rodríguez, Fernando; Harris, Linda J; Moyne, Anne-Laure; Blais, Burton; Topp, Ed; Bezanson, Greg; Bach, Susan; Delaquis, Pascal

    2014-08-01

    The ability to predict the behavior of Escherichia coli O157:H7 on contaminated field lettuce is essential for the development of accurate quantitative microbial risk assessments. The survival pattern of the species was assessed from several data sets derived from field-based experiments, which were analyzed by regression analysis fitting one monophasic model (log-linear) and two biphasic (Weibull and Cerf's model) models. Probabilistic models were also simulated with @RISK™, integrating the fitted monophasic and biphasic models in order to analyze their impact on the estimate of the extent of die-off subsequent to a contamination event in the field. Regression analysis indicated that E. coli O157:H7 followed a biphasic decay pattern in most cases, with the Weibull and Cerf's model showing similar good fit to individual and pooled survival data. Furthermore, results from the stochastic analysis demonstrated that using the log-linear model could lead to different risk estimates from those obtained with biphasic models, with a lower prevalence in the former scenario as no tailing is assumed in this model. The models and results derived from this work provide the first suitable mathematical base upon which to build probabilistic models to predict the fate of E. coli O157:H7 on field-grown leafy green vegetable. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.

  19. Effects of interspecific competition, predation, and their interaction on survival and development time of immature Anopheles quadrimaculatus.

    Science.gov (United States)

    Knight, Tiffany M; Chase, Jonathan M; Goss, Charles W; Knight, Jennifer J

    2004-12-01

    We examined the effect of predation by the backswimmer (Notonecta undulata; Hemiptera: Notonectidae), competition by zooplankton and snails, and both predation and competition on the survival and development time of larval Anopheles quadrimaculatus mosquitoes in experimental mesocosms. We found that both predation and interspecific competition greatly reduced the survivorship of larvae and the number of larvae emerging into adulthood. Treatments with both predators and competitors had fewer larvae transitioning among instars and into adulthood but not in an additive way. In addition, mosquito larvae in the presence of predators and competitors took two days longer to emerge than where predators and competitions were absent. Our work provides evidence that biotic interactions, such as predation and competition, can strongly regulate the number of mosquito larvae by reducing the number of larvae that survive through instars and to emergence and by increasing the generation time.

  20. 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...

  1. Rat Strains Bred for Low and High Aerobic Running Capacity do not Differ in Their Survival Time to Hemorrhage

    Science.gov (United States)

    2009-12-01

    maximal oxygen consumption ( Vo2max ) was 12% greater during normoxia and 20% greater during hypoxia in the HCR vs LCR (12). This enhanced Vo2max ...mitochondria) rather than differences in cardiac output, and also occurred in the absence of differences in oxygen delivery. By generation 15, VO2max ...survival time of HCR and LCR lines to the same controlled hemorrhage. We hypothesized that a strain with improved VO2max (i.e., HCR) would demonstrate an

  2. Recombinant human erythropoietin increases survival and reduces neuronal apoptosis in a murine model of cerebral malaria

    DEFF Research Database (Denmark)

    Wiese, Lothar; Hempel, Casper; Penkowa, Milena

    2008-01-01

    BACKGROUND: Cerebral malaria (CM) is an acute encephalopathy with increased pro-inflammatory cytokines, sequestration of parasitized erythrocytes and localized ischaemia. In children CM induces cognitive impairment in about 10% of the survivors. Erythropoietin (Epo) has - besides of its well known...... with recombinant human Epo (rhEpo; 50-5000 U/kg/OD, i.p.) at different time points. The effect on survival was measured. Brain pathology was investigated by TUNEL (Terminal deoxynucleotidyl transferase (TdT)-mediated deoxyuridine triphosphate (dUTP)-digoxigenin nick end labelling), as a marker of apoptosis. Gene...... expression in brain tissue was measured by real time PCR. RESULTS: Treatment with rhEpo increased survival in mice with CM in a dose- and time-dependent manner and reduced apoptotic cell death of neurons as well as the expression of pro-inflammatory cytokines in the brain. This neuroprotective effect...

  3. Time Varying Parameterization of Hydrological Models

    Science.gov (United States)

    Bardossy, A.; Singh, S. K.

    2007-12-01

    Hydrological models are frequently used for forecasting, water management or design to provide information for decision making. Due to the simplification of the complex natural processes and the limited availability of observations the parameters of these models cannot be identified perfectly. Usually the parameters of the models are assumed to be time independent. However some properties of the catchments might change in from one event to another in an unpredictable manner. The purpose of this paper is to develop a methodology to estimate selected model parameters as random variables changing in time. The distribution of the model parameter is assessed in calibration phase using different assumptions. During the application of the model these distributions are used to estimate the expected hydrological behavior and the uncertainty too. The methodology will be demonstrated on mezo-scale catchments in the Neckar basin in South-West Germany. The systematic differences between model behavior and observations are demonstrated using a set of selected events. Calibration and uncertainty estimation are demonstrated by an example application to a distributed HBV model. The model residual distributions are presented and compared to a standard calibration method. Further, it is shown that the new methodology leads to more realistic confidence intervals for model simulations.

  4. Nonlinear group survival in Kimura's model for the evolution of altruism.

    Science.gov (United States)

    Fontanari, José F; Serva, Maurizio

    2014-03-01

    Establishing the conditions that guarantee the spreading or the sustenance of altruistic traits in a population is the main goal of intergroup selection models. Of particular interest is the balance of the parameters associated to group size, migration and group survival against the selective advantage of the non-altruistic individuals. Here we use Kimura's diffusion model of intergroup selection to determine those conditions in the case the group survival rate is a nonlinear non-decreasing function of the proportion of altruists in a group. In the case this function is linear, there are two possible steady states which correspond to the non-altruistic and the altruistic phases. At the discontinuous transition line separating these phases there is a non-ergodic coexistence phase. For a continuous concave survival function, we find an ergodic coexistence phase that occupies a finite region of the parameter space in between the altruistic and the non-altruistic phases, and is separated from these phases by continuous transition lines. For a convex survival function, the coexistence phase disappears altogether but a bistable phase appears for which the choice of the initial condition determines whether the evolutionary dynamics leads to the altruistic or the non-altruistic steady state. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. 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.

  6. 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

    is for improved susceptibility rather than endurance, the error of applying a classical survival model was nonnegligible. The difference was most pronounced for scenarios with substantial underlying genetic variation in endurance and when the 2 underlying traits were lowly genetically correlated. In the presence...... 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...

  7. A reproducible, clinically relevant, intensively managed, pig model of acute liver failure for testing of therapies aimed to prolong survival.

    Science.gov (United States)

    Lee, Karla C L; Palacios Jimenez, Carolina; Alibhai, Hatim; Chang, Yu-Mei; Leckie, Pamela J; Baker, Luisa A; Stanzani, Giacomo; L Priestnall, Simon; Mookerjee, Rajeshwar P; Jalan, Rajiv; Davies, Nathan A

    2013-04-01

    A clinically relevant, translational large animal model of acute liver failure (ALF) is required for testing of novel therapies to prolong survival in acute liver failure, to permit spontaneous liver recovery or to act as a bridge to transplantation. The aim was to establish a pig model of acetaminophen-induced ALF that mimics the human clinical syndrome, is managed as in a human intensive care unit and has a predictable survival time. Nine female pigs were anaesthetised and instrumented for continuous intensive care monitoring and management using: target-driven protocols for treatment of cardiovascular collapse, metabolic acidosis and electrolyte abnormalities; intermittent positive pressure ventilation; and continuous renal replacement therapy. Six animals were induced to ALF with acetaminophen (paracetamol). Three animals acted as controls. Irreversible acute liver failure, defined as rise in prothrombin time >3 times normal, occurred 19.3 ± 1.8 h after the onset of acetaminophen administration. Death occurred predictably 12.6 ± 2.7 h thereafter, with acute hepatocellular necrosis in all animals. Clinical progression of liver failure mimicked the human condition including development of coagulopathy, intracranial hypertension, hyperammonaemia, cardiovascular collapse, elevation in creatinine, metabolic acidosis and hyperlactataemia. In addition, cardiovascular monitoring clearly demonstrated progressive cardiac dysfunction in ALF. A reproducible, clinically relevant, intensively managed, large animal model of acute liver failure, with death as a result of multi-organ failure, has been successfully validated for translational studies of disease progression and therapies designed to prolong survival in man. © 2012 John Wiley & Sons A/S.

  8. 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

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

    Science.gov (United States)

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

    2017-10-01

    Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (P<.001). Learning 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

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

    DEFF Research Database (Denmark)

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

    2012-01-01

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

  11. Modeling biological pathway dynamics with timed automata.

    Science.gov (United States)

    Schivo, Stefano; Scholma, Jetse; Wanders, Brend; Urquidi Camacho, Ricardo A; van der Vet, Paul E; Karperien, Marcel; Langerak, Rom; van de Pol, Jaco; Post, Janine N

    2014-05-01

    Living cells are constantly subjected to a plethora of environmental stimuli that require integration into an appropriate cellular response. This integration takes place through signal transduction events that form tightly interconnected networks. The understanding of these networks requires capturing their dynamics through computational support and models. ANIMO (analysis of Networks with Interactive Modeling) is a tool that enables the construction and exploration of executable models of biological networks, helping to derive hypotheses and to plan wet-lab experiments. The tool is based on the formalism of Timed Automata, which can be analyzed via the UPPAAL model checker. Thanks to Timed Automata, we can provide a formal semantics for the domain-specific language used to represent signaling networks. This enforces precision and uniformity in the definition of signaling pathways, contributing to the integration of isolated signaling events into complex network models. We propose an approach to discretization of reaction kinetics that allows us to efficiently use UPPAAL as the computational engine to explore the dynamic behavior of the network of interest. A user-friendly interface hides the use of Timed Automata from the user, while keeping the expressive power intact. Abstraction to single-parameter kinetics speeds up construction of models that remain faithful enough to provide meaningful insight. The resulting dynamic behavior of the network components is displayed graphically, allowing for an intuitive and interactive modeling experience.

  12. Relationships among the cervical mucus urea and acetone, accuracy of insemination timing, and sperm survival in Holstein cows.

    Science.gov (United States)

    Beran, J; Stádník, L; Ducháček, J; Okrouhlá, M; Doležalová, M; Kadlecová, V; Ptáček, M

    2013-11-01

    The objectives of this study were to evaluate the relationships among urea and acetone content in cows' cervical mucus (CM), its crystallization type (CT) and sperm survival (SS) after timed AI. Samples of CM were collected from 192 Holstein cows treated by Ovsynch(®) protocol. Analysis of the urea and acetone content for identification of the metabolic status, the arborization test for evaluation of insemination timing and the short-term heat test of SS for assessment of its suitability as a biological matrix were performed. The data set was analyzed by the GLM procedure using SAS(®). The results documented the existence of substantial differences in individual response to the Ovsynch(®) protocol causing insemination of 55.2% cows at an inappropriate time. The urea content was found as a possible indicator of a cow's metabolism and/or of insemination timing, concentrations of less than 500 mg/L corresponded (Pacetone content on SS were determined. The greatest values of SS were detected in cows with an expected response to precisely timed oestrus documented by the corresponding CT. Greater values of urea (>260 mg/L) and acetone (>5mg/L) negatively affected SS as well (P<0.05-0.01). The results confirmed that the accuracy of insemination timing can be affected by the metabolism intensity, just as CM quality directly influences sperm survival. Copyright © 2013 Elsevier B.V. All rights reserved.

  13. RTMOD: Real-Time MODel evaluation

    DEFF Research Database (Denmark)

    Graziani, G.; Galmarini, S.; Mikkelsen, Torben

    2000-01-01

    The 1998 - 1999 RTMOD project is a system based on an automated statistical evaluation for the inter-comparison of real-time forecasts produced by long-range atmospheric dispersion models for national nuclear emergency predictions of cross-boundaryconsequences. The background of RTMOD was the 1994...... ETEX project that involved about 50 models run in several Institutes around the world to simulate two real tracer releases involving a large part of the European territory. In the preliminary phase ofETEX, three dry runs (i.e. simulations in real-time of fictitious releases) were carried out...... would be recalculated to include the influence by all available predictions. The new web-based RTMOD concept has proven useful as a practical decision-making tool for real-time communicationbetween dispersion modellers around the World and for fast and standardised information exchange on the most...

  14. Interdependent multi-layer networks: modeling and survivability analysis with applications to space-based networks.

    Science.gov (United States)

    Castet, Jean-Francois; Saleh, Joseph H

    2013-01-01

    This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the

  15. Interdependent multi-layer networks: modeling and survivability analysis with applications to space-based networks.

    Directory of Open Access Journals (Sweden)

    Jean-Francois Castet

    Full Text Available This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also

  16. Survival analysis of stochastic competitive models in a polluted environment and stochastic competitive exclusion principle.

    Science.gov (United States)

    Liu, Meng; Wang, Ke; Wu, Qiong

    2011-09-01

    Stochastic competitive models with pollution and without pollution are proposed and studied. For the first system with pollution, sufficient criteria for extinction, nonpersistence in the mean, weak persistence in the mean, strong persistence in the mean, and stochastic permanence are established. The threshold between weak persistence in the mean and extinction for each population is obtained. It is found that stochastic disturbance is favorable for the survival of one species and is unfavorable for the survival of the other species. For the second system with pollution, sufficient conditions for extinction and weak persistence are obtained. For the model without pollution, a partial stochastic competitive exclusion principle is derived. © Society for Mathematical Biology 2010

  17. Diabetes, hypertension and hyperlipidemia: prevalence over time and impact on long-term survival after liver transplantation.

    Science.gov (United States)

    Parekh, J; Corley, D A; Feng, S

    2012-08-01

    With increasing short-term survival, the transplant community has turned its focus to delineating the impact of medical comorbidities on long-term outcomes. Unfortunately, conditions such as diabetes, hypertension and hyperlipidemia are difficult to track and often managed outside of the transplant center by primary care providers. We collaborated with Kaiser Permanente Northern California to create a database of 598 liver transplant recipients, which incorporates diagnostic codes along with laboratory and pharmacy data. Specifically, we determined the prevalence of diabetes, hypertension and hyperlipidemia both before and after transplant and evaluated the influence of disease duration as a time-dependent covariate on posttransplant survival. The prevalence of these comorbidities increased steadily from the time of transplant to 7 years after transplant. The estimated risk for all-cause mortality (hazard ratio = 1.07 per year increment, 95% CI 1.01-1.13, p hyperlipidemia. Greater attention to management of diabetes may mitigate its negative impact on long-term survival in liver transplant recipients. © Copyright 2012 The American Society of Transplantation and the American Society of Transplant Surgeons.

  18. Alcohol use at time of injury and survival following traumatic brain injury: results from the National Trauma Data Bank.

    Science.gov (United States)

    Chen, Chiung M; Yi, Hsiao-Ye; Yoon, Young-Hee; Dong, Chuanhui

    2012-07-01

    Premised on biological evidence from animal research, recent clinical studies have, for the most part, concluded that elevated blood alcohol concentration levels are independently associated with higher survival or decreased mortality in patients with moderate to severe traumatic brain injury (TBI). This study aims to provide some counterevidence to this claim and to further future investigations. Incident data were drawn from the largest U.S. trauma registry, the National Trauma Data Bank, for emergency department admission years 2002-2006. TBI was identified according to the National Trauma Data Bank's definition using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), codes. To eliminate confounding, the exact matching method was used to match alcohol-positive with alcohol-negative incidents on sex, age, race/ethnicity, and facility. Logistic regression compared in-hospital mortality between 44,043 alcohol-positive and 59,817 matched alcohol-negative TBI incidents, with and without causes and intents of TBI and Injury Severity Score as covariates. A sensitivity analysis was performed within a subsample of isolated moderate to severe TBI incidents. Alcohol use at the time of injury was found to be significantly associated with an increased risk for TBI. Including varied causes and intents of TBI and Injury Severity Score as potential confounders in the regression model explained away the statistical significance of the seemingly protective effect of alcohol against TBI mortality for all TBIs and for isolated moderate to severe TBIs. The null finding shows that the purported reduction in TBI mortality attributed to positive blood alcohol likely is attributable to residual confounding. Accordingly, the risk of TBI associated with alcohol use should not be overlooked.

  19. Modeling of the time sharing for lecturers

    Directory of Open Access Journals (Sweden)

    E. Yu. Shakhova

    2017-01-01

    Full Text Available In the context of modernization of the Russian system of higher education, it is necessary to analyze the working time of the university lecturers, taking into account both basic job functions as the university lecturer, and others.The mathematical problem is presented for the optimal working time planning for the university lecturers. The review of the documents, native and foreign works on the study is made. Simulation conditions, based on analysis of the subject area, are defined. Models of optimal working time sharing of the university lecturers («the second half of the day» are developed and implemented in the system MathCAD. Optimal solutions have been obtained.Three problems have been solved:1 to find the optimal time sharing for «the second half of the day» in a certain position of the university lecturer;2 to find the optimal time sharing for «the second half of the day» for all positions of the university lecturers in view of the established model of the academic load differentiation;3 to find the volume value of the non-standardized part of time work in the department for the academic year, taking into account: the established model of an academic load differentiation, distribution of the Faculty number for the positions and the optimal time sharing «the second half of the day» for the university lecturers of the department.Examples are given of the analysis results. The practical application of the research: the developed models can be used when planning the working time of an individual professor in the preparation of the work plan of the university department for the academic year, as well as to conduct a comprehensive analysis of the administrative decisions in the development of local university regulations.

  20. 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

  1. A geographic study of West Nile virus in humans, dead corvids and mosquitoes in Ontario using spatial scan statistics with a survival time application.

    Science.gov (United States)

    Thomas-Bachli, A L; Pearl, D L; Berke, O; Parmley, E J; Barker, I K

    2017-11-01

    Surveillance of West Nile virus (WNv) in Ontario has included passive reporting of human cases and testing of trapped mosquitoes and dead birds found by the public. The dead bird surveillance programme was limited to testing within a public health unit (PHU) until a small number of birds test positive. These dead corvid and mosquito surveillance programmes have not been compared for their ability to provide early warning in geographic areas where human cases occur each year. Spatial scan statistics were applied to time-to-event survival data based on first cases of WNv in found dead corvids, mosquitoes and humans. Clusters identified using raw data were compared to clusters based on model-adjusted survival times to evaluate whether geographic and sociodemographic factors influenced their distribution. Statistically significant (p space-time clusters of PHUs with faster time to detection were found using each surveillance data stream. During 2002-2004, the corvid surveillance programme outperformed the mosquito programme in terms of time to WNv detection, while the clusters of first-positive mosquito pools were more spatially similar to first human cases. In 2006, a cluster of first-positive dead corvids was located in northern PHUs and preceded a cluster of early human cases that was identified after controlling for the influence of geographic region and sociodemographic profile. © 2017 Blackwell Verlag GmbH.

  2. Wind speed during migration influences the survival, timing of breeding, and productivity of a neotropical migrant, Setophaga petechia.

    Directory of Open Access Journals (Sweden)

    Anna Drake

    Full Text Available Over the course of the annual cycle, migratory bird populations can be impacted by environmental conditions in regions separated by thousands of kilometers. We examine how climatic conditions during discrete periods of the annual cycle influence the demography of a nearctic-neotropical migrant population of yellow warblers (Setophaga petechia, that breed in western Canada and overwinter in Mexico. We demonstrate that wind conditions during spring migration are the best predictor of apparent annual adult survival, male arrival date, female clutch initiation date and, via these timing effects, annual productivity. We find little evidence that conditions during the wintering period influence breeding phenology and apparent annual survival. Our study emphasizes the importance of climatic conditions experienced by migrants during the migratory period and indicates that geography may play a role in which period most strongly impacts migrant populations.

  3. Interleukin-7 Ameliorates Immune Dysfunction and Improves Survival in a 2-Hit Model of Fungal Sepsis

    OpenAIRE

    Unsinger, Jacqueline; Burnham, Carey-Ann D.; McDonough, Jacquelyn; Morre, Michel; Prakash, Priya S.; Caldwell, Charles C.; Dunne, W. Michael; Hotchkiss, Richard S.

    2012-01-01

    Background. Secondary hospital-acquired fungal infections are common in critically-ill patients and mortality remains high despite antimicrobial therapy. Interleukin-7 (IL-7) is a potent immunotherapeutic agent that improves host immunity and has shown efficacy in bacterial and viral models of infection. This study examined the ability of IL-7, which is currently in multiple clinical trials (including hepatitis and human immunodeficiency virus), to improve survival in a clinically relevant 2-...

  4. Determination of a Change Point in the Age at Diagnosis of Breast Cancer Using a Survival Model.

    Science.gov (United States)

    Abdollahi, Mahbubeh; Hajizadeh, Ebrahim; Baghestani, Ahmad Reza; Haghighat, Shahpar

    2016-01-01

    Breast cancer, the second cause of cancer-related death after lung cancer and the most common cancer in women after skin cancer, is curable if detected in early stages of clinical presentation. Knowledge as to any age cut-off points which might have significance for prognostic groups is important in screening and treatment planning. Therefore, determining a change-point could improve resource allocation. This study aimed to determine if a change point for survival might exist in the age of breast cancer diagnosis. This study included 568 cases of breast cancer that were registered in Breast Cancer Research Center, Tehran, Iran, during the period 1986-2006 and were followed up to 2012. In the presence of curable cases of breast cancer, a change point in the age of breast cancer diagnosis was estimated using a mixture survival cure model. The data were analyzed using SPSS (versions 20) and R (version 2.15.0) software. The results revealed that a change point in the age of breast cancer diagnosis was at 50 years age. Based on our estimation, 35% of the patients diagnosed with breast cancer at age less than or equal to 50 years of age were cured while the figure was 57% for those diagnosed after 50 years of age. Those in the older age group had better survival compared to their younger counterparts during 12 years of follow up. Our results suggest that it is better to estimate change points in age for cancers which are curable in early stages using survival cure models, and that the cure rate would increase with timely screening for breast cancer.

  5. Relationships between Serum Albumin and Urea Level and the Clinical Pathological Characteristics and Survival Time in Patients with Lung Cancer

    Directory of Open Access Journals (Sweden)

    Yalun LI

    2017-03-01

    Full Text Available Background and objective Lung cancer is the most common malignancy and is the leading cause of cancer-related death worldwide. Thus, this disease severely threatens human health. This study aims to identify the relationships between serum albumin and urea level and the clinical pathological characteristics and survival time in patients with lung cancer. Methods A total of 1,098 patients with lung cancer were diagnosed by pathology and tested the serum albumin and urea level in West China Hospital of Sichuan University during January 2008 to December 2013. According to the levels of albumin and urea, patients were divided into the normal level group (negative group and abnormal level group (positive group. The differences of patients' clinical pathological characteristics and survival time in the two groups were analyzed. Results Differences in age, sex, histological classification, liver metastasis and pleural metastasis were statistically significant between the two groups of serum albumin (P<0.05. Differences in age, intrapulmonary metastasis of 312 patients of squamous cell carcinoma and differences in age, sex, stages, pleural metastasis of 612 patients of adenocarcinoma between the two groups of serum albumin (P<0.05. There were no significant differences between the two groups of urea. In different histological classification between the two groups of serum albumin, the median survival period of squamous cell carcinoma was 36 months and 19 monthes, adenocarcinoma was 35 months and 15 monthes, the abnormal group were all significantly lower than those in the normal group. The median survival period was no significant difference between the two groups of urea. Conclusion The level of serum albumin is an important indicator for prognosis.

  6. 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

  7. Real-time modeling of heat distributions

    Energy Technology Data Exchange (ETDEWEB)

    Hamann, Hendrik F.; Li, Hongfei; Yarlanki, Srinivas

    2018-01-02

    Techniques for real-time modeling temperature distributions based on streaming sensor data are provided. In one aspect, a method for creating a three-dimensional temperature distribution model for a room having a floor and a ceiling is provided. The method includes the following steps. A ceiling temperature distribution in the room is determined. A floor temperature distribution in the room is determined. An interpolation between the ceiling temperature distribution and the floor temperature distribution is used to obtain the three-dimensional temperature distribution model for the room.

  8. Simple models based on gamma-glutamyl transpeptidase and platelets for predicting survival in hepatitis B-associated hepatocellular carcinoma

    Directory of Open Access Journals (Sweden)

    Pang Q

    2016-04-01

    Full Text Available Qing Pang, Jian-Bin Bi, Zhi-Xin Wang, Xin-Sen Xu, Kai Qu, Run-Chen Miao, Wei Chen, Yan-Yan Zhou, Chang Liu Department of Hepatobiliary Surgery, the First Affiliated Hospital of Medical College, Xi’an Jiaotong University, Shaanxi Province, People’s Republic of China Background: Several hepatic cirrhosis-derived noninvasive models have been developed to predict the incidence and outcomes of hepatocellular carcinoma (HCC. We aimed to investigate the prognostic significance of the two novel established cirrhosis-associated models based on gamma-glutamyl transpeptidase (GGT and platelets in hepatitis B-associated HCC. Methods: We retrospectively evaluated 182 HCC patients with positive hepatitis B surface antigen who received radical therapy at a single institution between 2002 and 2012. Laboratory data prior to operation were collected to calculate the GGT to platelets ratio (GPR and the S-index. Predictive factors associated with overall survival and recurrence-free survival were assessed using log-rank test and multivariate Cox analysis. Additional analyses were performed after patients were stratified based on cirrhosis status, tumor size, therapy methods, and so forth, to investigate the prognostic significance in different subgroups. Results: During a median follow-up time of 45.0 months, a total of 88 (48.4% patients died and 79 (43.4% patients recurred. The cut-off points for GPR and S-index in predicting death were determined to be 0.76 and 0.56, respectively. Compared with patients with a lower GPR, those with GPR ≥0.76 had a higher probability of cirrhosis and a larger tumor (both P<0.05. GPR and S-index were both found to be significantly associated with survival by univariate log-rank test. Multivariate analysis identified tumor size ≥5 and high level of GPR, but not high Barcelona Clinic Liver Cancer stage or S-index, as independent factors for predicting poor overall survival and recurrence-free survival. Conclusion: The GPR is

  9. Efficiently learning timed system models from observations

    NARCIS (Netherlands)

    Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.

    2008-01-01

    This paper describes an efficient algorithm for learning a timed model from observations. The algorithm is based on the state merging method for learning a deterministic finite state automaton (DFA). This method and its problem have been the subject of many studies within the grammatical inference

  10. Space-time modeling of timber prices

    Science.gov (United States)

    Mo Zhou; Joseph Buongriorno

    2006-01-01

    A space-time econometric model was developed for pine sawtimber timber prices of 21 geographically contiguous regions in the southern United States. The correlations between prices in neighboring regions helped predict future prices. The impulse response analysis showed that although southern pine sawtimber markets were not globally integrated, local supply and demand...

  11. On modeling panels of time series

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans)

    2002-01-01

    textabstractThis paper reviews research issues in modeling panels of time series. Examples of this type of data are annually observed macroeconomic indicators for all countries in the world, daily returns on the individual stocks listed in the S&P500, and the sales records of all items in a

  12. Endoscopic Ultrasound-Guided Direct Portal Pressure Measurement Using a Digital Pressure Wire with Real-Time Remote Display: A Survival Study.

    Science.gov (United States)

    Schulman, Allison R; Thompson, Christopher C; Ryou, Marvin

    2017-10-01

    Portal hypertension is necessary for the development of most clinical complications of cirrhosis. We recently reported a novel, endoscopic ultrasound (EUS)-guided technique for direct portal pressure measurements using a digital pressure wire. The aims of this study were to (1) evaluate safety in an animal survival model and (2) compare direct portal vein (PV) versus transhepatic access of a first-order venule. Yorkshire pigs, weighing 40-55 kg. Procedures were performed under general anesthesia. PV was identified using a linear array echoendoscope and accessed with a 22-G fine needle aspiration needle preloaded with a digital pressure wire. Access was confirmed by portal venography. Mean digital pressure measurements were recorded over 30 seconds, and again after accessing a first-order portal venule in a transhepatic manner. Procedure times and video logs were maintained throughout. Animals were survived for 2 weeks. Repeat portal pressure measurements were performed before euthanasia and necropsy. EUS-guided portal pressure measurements ranged from 3 to 11 mm Hg (mean 6.1) and were performed in a mean time of 214 seconds. There was no difference in measurement between the PV and first-order venule, or between baseline and 2-week follow-up. Five of 5 animals survived without incident. On necropsy, there was no evidence of thrombus or hemorrhage. This study represents the first survival study after EUS-guided direct portal pressure measurements using a digital pressure wire. This method appears safe, straightforward, and precise. Measurements of the PV and a first-order portal venule appear equivalent, and serial measurement seems feasible.

  13. Long-term disability and survival in traumatic brain injury: results from the National Institute on Disability and Rehabilitation Research Model Systems.

    Science.gov (United States)

    Brooks, Jordan C; Strauss, David J; Shavelle, Robert M; Paculdo, David R; Hammond, Flora M; Harrison-Felix, Cynthia L

    2013-11-01

    To document long-term survival in 1-year survivors of traumatic brain injury (TBI); to compare the use of the Disability Rating Scale (DRS) and FIM as factors in the estimation of survival probabilities; and to investigate the effect of time since injury and secular trends in mortality. Cohort study of 1-year survivors of TBI followed up to 20 years postinjury. Statistical methods include standardized mortality ratio, Kaplan-Meier survival curve, proportional hazards regression, and person-year logistic regression. Postdischarge from rehabilitation units. Population-based sample of persons (N=7228) who were admitted to a TBI Model Systems facility and survived at least 1 year postinjury. These persons contributed 32,505 person-years, with 537 deaths, over the 1989 to 2011 study period. Not applicable. Survival. Survival was poorer than that of the general population (standardized mortality ratio=2.1; 95% confidence interval, 1.9-2.3). Age, sex, and functional disability were significant risk factors for mortality (Pmodels had comparable predictive performance (C index: .80 vs .80; Akaike information criterion: 11,005 vs 11,015). Time since injury and current calendar year were not significant predictors of long-term survival (both P>.05). Long-term survival prognosis in TBI depends on age, sex, and disability. FIM and DRS are useful prognostic measures with comparable statistical performance. Age- and disability-specific mortality rates in TBI have not declined over the last 20 years. A survival prognosis calculator is available online (http://www.LifeExpectancy.org/tbims.shtml). Copyright © 2013 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  14. An ensemble survival model for estimating relative residual longevity following stroke: Application to mortality data in the chronic dialysis population.

    Science.gov (United States)

    Phadnis, Milind A; Wetmore, James B; Shireman, Theresa I; Ellerbeck, Edward F; Mahnken, Jonathan D

    2017-12-01

    Time-dependent covariates can be modeled within the Cox regression framework and can allow both proportional and nonproportional hazards for the risk factor of research interest. However, in many areas of health services research, interest centers on being able to estimate residual longevity after the occurrence of a particular event such as stroke. The survival trajectory of patients experiencing a stroke can be potentially influenced by stroke type (hemorrhagic or ischemic), time of the stroke (relative to time zero), time since the stroke occurred, or a combination of these factors. In such situations, researchers are more interested in estimating lifetime lost due to stroke rather than merely estimating the relative hazard due to stroke. To achieve this, we propose an ensemble approach using the generalized gamma distribution by means of a semi-Markov type model with an additive hazards extension. Our modeling framework allows stroke as a time-dependent covariate to affect all three parameters (location, scale, and shape) of the generalized gamma distribution. Using the concept of relative times, we answer the research question by estimating residual life lost due to ischemic and hemorrhagic stroke in the chronic dialysis population.

  15. Efficacy of Re-188-labelled sulphur colloid on prolongation of survival time in melanoma-bearing animals

    Energy Technology Data Exchange (ETDEWEB)

    Chen, F.D.; Hsieh, B.T. E-mail: bthsieh@iner.gov.tw; Wang, H.E.; Ou, Y.H.; Yang, W.K.; Whang-Peng, J.; Liu, R.S.; Knapp, F.F.; Ting, G.; Yen, S.H

    2001-10-01

    In this study, the effectiveness of a {sup 188}Re labeled sulfur colloid with two particle size ranges was used to evaluate the effectiveness of this agent on melanoma tumors in mice in terms of animal lifespan. Methods: Two separate group of animals were used for investigating biodistribution and survival time. A total of 188 B16F10-melanoma-bearing BDF{sub 1} mice were injected intraperitoneally with 3.7 MBq (0.1mCi)/2mL of radiolabeled sulfur colloid ten days after intraperitoneal inoculation of 5x10{sup 5} B16F10 melanoma cells/2ml. For group 1, 30 mice were sacrificed at 1, 4, 24, 48 and 72 hours for biodistribution studies. In group 2, 158 mice were divided into 9 groups (n=16{approx}18/groups)each receiving respectively tumor alone, tumor with normal saline, cold colloid or hot colloid with 16, 23, 31, 46, 62, or 124 MBq activity. Each of these colloid groups was further divided into two groups, one receiving smaller particle sizes (<3{mu}m:80.4 {+-}7.2%, colloid 1) and the other receiving larger particle sizes (<3{mu}m:12.3{+-}1.0%, colloid 2). The animals were checked daily until death and their survival recorded. Results: Colloid 2 showed higher accumulation in almost all tissues, the highest accumulation organ was tumor ({approx} 40%), then spleen ({approx}20%), stomach ({approx}15%), diaphragm ({approx}3%), and liver ({approx}2%). There was a significant increase in survival time with increasing amount of the larger-particle-size colloid. Administered levels of 16-31 MBq/mouse were most efficacious and with higher amounts the survival times decreased significantly below that of the controls. There was a significant difference in the dose-response curves for the two preparations. Protection factors (1/Relative-risk) of nearly 5 were achieved using the larger colloid size, and nearly 30 using the smaller colloid size. An amount of 16-31 MBq of the colloid 2 was the optimal activity in these studies. On the one hand, the survival data agreed well with the

  16. Factors relating to poor survival rates of aged cervical cancer patients: a population-based study with the relative survival model in Osaka, Japan.

    Science.gov (United States)

    Ioka, Akiko; Ito, Yuri; Tsukuma, Hideaki

    2009-01-01

    Poor survival of older cervical cancer patients has been reported; however, related factors, such as the extent of disease and the competitive risk by aging have not been well evaluated. We applied the relative survival model developed by Dickman et al to resolve this issue. Study subjects were cervical cancer patients retrieved from the Osaka Cancer Registry. They were limited to the 10,048 reported cases diagnosed from 1975 to 1999, based on the quality of data collection on vital status. Age at diagnosis was categorized into or = 65 years. The impact of prognostic factors on 5-year survival was evaluated with the relative survival model, incorporating patients' expected survival in multivariate analysis. The age-specific relative excess risk (RER) of death was significantly higher for older groups as compared with women aged 30-54 years (RER, 1.58 at 55-64 and 2.51 at > or = 65 years). The RER was decreased by 64.8% among the 55-64 year olds as an effect of cancer stage at diagnosis, and by 43.4% among those 65 years old and over. After adding adjustment for treatment modalities, the RER was no longer significantly higher among 55-64 year olds; however, it was still higher among 65 year olds and over. Advanced stage at diagnosis was the main determinant of poor survival among the aged cervical cancer patients, although other factors such as limitations on the combination of treatment were also suggested to have an influence in those aged 65 years and over.

  17. Constitutive model with time-dependent deformations

    DEFF Research Database (Denmark)

    Krogsbøll, Anette

    1998-01-01

    In many geological and Engineering problems it is necessary to transform information from one scale to another. Data collected at laboratory scale are often used to evaluate field problems on a much larger scale. This is certainly true for geological problems where extreme scale differences...... are common in time as well as size. This problem is adressed by means of a new constitutive model for soils. It is able to describe the behavior of soils at different deformation rates. The model defines time-dependent and stress-related deformations separately. They are related to each other and they occur...... simultanelously. The model is based on concepts from elasticity and viscoplasticity theories. In addition to Hooke's law for the elastic behavior, the framework for the viscoplastic behavior consists, in the general case (two-dimensional or three-dimensional), of a yield surface, an associated flow rule...

  18. Spartan random processes in time series modeling

    Science.gov (United States)

    Žukovič, M.; Hristopulos, D. T.

    2008-06-01

    A Spartan random process (SRP) is used to estimate the correlation structure of time series and to predict (interpolate and extrapolate) the data values. SRPs are motivated from statistical physics, and they can be viewed as Ginzburg-Landau models. The temporal correlations of the SRP are modeled in terms of ‘interactions’ between the field values. Model parameter inference employs the computationally fast modified method of moments, which is based on matching sample energy moments with the respective stochastic constraints. The parameters thus inferred are then compared with those obtained by means of the maximum likelihood method. The performance of the Spartan predictor (SP) is investigated using real time series of the quarterly S&P 500 index. SP prediction errors are compared with those of the Kolmogorov-Wiener predictor. Two predictors, one of which is explicit, are derived and used for extrapolation. The performance of the predictors is similarly evaluated.

  19. Time models and cognitive processes: a review

    Directory of Open Access Journals (Sweden)

    Michail eManiadakis

    2014-02-01

    Full Text Available The sense of time is an essential capacity of humans, with a major role in many of the cognitive processes expressed in our daily lifes. So far, in cognitive science and robotics research, mental capacities have been investigated in a theoretical and modelling framework that largely neglects the flow of time. Only recently there has been a small but constantly increasing interest in the temporal aspects of cognition, integrating time into a range of different models of perceptuo-motor capacities. The current paper aims to review existing works in the field and suggest directions for fruitful future work. This is particularly important for the newly developed field of artificial temporal cognition that is expected to significantly contribute in the development of sophisticated artificial agents seamlessly integrated into human societies.

  20. Prognostic model for survival in patients with early stage cervical cancer.

    Science.gov (United States)

    Biewenga, Petra; van der Velden, Jacobus; Mol, Ben Willem J; Stalpers, Lukas J A; Schilthuis, Marten S; van der Steeg, Jan Willem; Burger, Matthé P M; Buist, Marrije R

    2011-02-15

    In the management of early stage cervical cancer, knowledge about the prognosis is critical. Although many factors have an impact on survival, their relative importance remains controversial. This study aims to develop a prognostic model for survival in early stage cervical cancer patients and to reconsider grounds for adjuvant treatment. A multivariate Cox regression model was used to identify the prognostic weight of clinical and histological factors for disease-specific survival (DSS) in 710 consecutive patients who had surgery for early stage cervical cancer (FIGO [International Federation of Gynecology and Obstetrics] stage IA2-IIA). Prognostic scores were derived by converting the regression coefficients for each prognostic marker and used in a score chart. The discriminative capacity was expressed as the area under the curve (AUC) of the receiver operating characteristic. The 5-year DSS was 92%. Tumor diameter, histological type, lymph node metastasis, depth of stromal invasion, lymph vascular space invasion, and parametrial extension were independently associated with DSS and were included in a Cox regression model. This prognostic model, corrected for the 9% overfit shown by internal validation, showed a fair discriminative capacity (AUC, 0.73). The derived score chart predicting 5-year DSS showed a good discriminative capacity (AUC, 0.85). In patients with early stage cervical cancer, DSS can be predicted with a statistical model. Models, such as that presented here, should be used in clinical trials on the effects of adjuvant treatments in high-risk early cervical cancer patients, both to stratify and to include patients. Copyright © 2010 American Cancer Society.

  1. Modelling p-value distributions to improve theme-driven survival analysis of cancer transcriptome datasets

    Directory of Open Access Journals (Sweden)

    Brors Benedikt

    2010-01-01

    Full Text Available Abstract Background Theme-driven cancer survival studies address whether the expression signature of genes related to a biological process can predict patient survival time. Although this should ideally be achieved by testing two separate null hypotheses, current methods treat both hypotheses as one. The first test should assess whether a geneset, independent of its composition, is associated with prognosis (frequently done with a survival test. The second test then verifies whether the theme of the geneset is relevant (usually done with an empirical test that compares the geneset of interest with random genesets. Current methods do not test this second null hypothesis because it has been assumed that the distribution of p-values for random genesets (when tested against the first null hypothesis is uniform. Here we demonstrate that such an assumption is generally incorrect and consequently, such methods may erroneously associate the biology of a particular geneset with cancer prognosis. Results To assess the impact of non-uniform distributions for random genesets in such studies, an automated theme-driven method was developed. This method empirically approximates the p-value distribution of sets of unrelated genes based on a permutation approach, and tests whether predefined sets of biologically-related genes are associated with survival. The results from a comparison with a published theme-driven approach revealed non-uniform distributions, suggesting a significant problem exists with false positive rates in the original study. When applied to two public cancer datasets our technique revealed novel ontological categories with prognostic power, including significant correlations between "fatty acid metabolism" with overall survival in breast cancer, as well as "receptor mediated endocytosis", "brain development", "apical plasma membrane" and "MAPK signaling pathway" with overall survival in lung cancer. Conclusions Current methods of theme

  2. Survival prediction from clinico-genomic models--a comparative study.

    Science.gov (United States)

    Bøvelstad, Hege M; Nygård, Ståle; Borgan, Ornulf

    2009-12-13

    Survival prediction from high-dimensional genomic data is an active field in today's medical research. Most of the proposed prediction methods make use of genomic data alone without considering established clinical covariates that often are available and known to have predictive value. Recent studies suggest that combining clinical and genomic information may improve predictions, but there is a lack of systematic studies on the topic. Also, for the widely used Cox regression model, it is not obvious how to handle such combined models. We propose a way to combine classical clinical covariates with genomic data in a clinico-genomic prediction model based on the Cox regression model. The prediction model is obtained by a simultaneous use of both types of covariates, but applying dimension reduction only to the high-dimensional genomic variables. We describe how this can be done for seven well-known prediction methods: variable selection, unsupervised and supervised principal components regression and partial least squares regression, ridge regression, and the lasso. We further perform a systematic comparison of the performance of prediction models using clinical covariates only, genomic data only, or a combination of the two. The comparison is done using three survival data sets containing both clinical information and microarray gene expression data. Matlab code for the clinico-genomic prediction methods is available at http://www.med.uio.no/imb/stat/bmms/software/clinico-genomic/. Based on our three data sets, the comparison shows that established clinical covariates will often lead to better predictions than what can be obtained from genomic data alone. In the cases where the genomic models are better than the clinical, ridge regression is used for dimension reduction. We also find that the clinico-genomic models tend to outperform the models based on only genomic data. Further, clinico-genomic models and the use of ridge regression gives for all three data sets

  3. Desiccation survival time for eggs of a widespread and invasive Australian mosquito species, Aedes (Finlaya) notoscriptus (Skuse).

    Science.gov (United States)

    Faull, K J; Webb, C; Williams, C R

    2016-06-01

    The Australian native mosquito Aedes (Finlaya) notoscriptus (Skuse) is closely associated with natural and artificial water holding receptacles. Eggs are laid in habitats where they are exposed to drying conditions as water levels fluctuate. Withstanding desiccation enables survival in challenging environments and increases the potential for establishment in non-native habitats. Until now, the desiccation resistance of Ae. notoscriptus eggs has been unknown despite the historical invasive success of this important dog heartworm and arbovirus vector. Viability and mean survival times of eggs from two Ae. notoscriptus populations (metropolitan areas of Sydney, NSW and Adelaide, SA) were evaluated, with eggs stored under three dryness conditions for up to 367 days. Our results revealed that Ae. notoscriptus eggs can withstand desiccation for extended periods, under a variety of conditions, with approximately 9-13% egg viability recorded after one year. This prolonged egg survival reflects the widespread distribution of this mosquito in Australia and its history of incursions and subsequent establishment in non-native habitats. Differences in mean egg volume were recorded in addition to significantly different egg length to width ratios for the two populations, which may reflect adaptation to biotope of origin and an associated likelihood of drought and drying conditions. The results of this study suggest that the desiccation resistant eggs of Ae. notoscriptus make this species highly adaptable, increasing the risk of movement to non-endemic regions of the world. © 2016 The Society for Vector Ecology.

  4. Neurogenesis and the Spacing Effect: Learning over Time Enhances Memory and the Survival of New Neurons

    Science.gov (United States)

    Sisti, Helene M.; Glass, Arnold L.; Shors, Tracey J.

    2007-01-01

    Information that is spaced over time is better remembered than the same amount of information massed together. This phenomenon, known as the spacing effect, was explored with respect to its effect on learning and neurogenesis in the adult dentate gyrus of the hippocampal formation. Because the cells are generated over time and because learning…

  5. The effect of time of planting at stake on cocoa seedling survival ...

    African Journals Online (AJOL)

    Studies were conducted at the Cocoa Research Institute of Ghana, Tafo and its sub-station at Bunso, from 1994 to 1997 to re-appraise the success of cocoa etablishment from seeds sown at stake at specified times of the year before the onset of the dry season. The treatments consisted of four times within the year when ...

  6. Does the benefit on survival from leisure time physical activity depend on physical activity at work?

    DEFF Research Database (Denmark)

    Holtermann, Andreas; Marott, Jacob Louis; Gyntelberg, Finn

    2013-01-01

    To investigate if persons with high physical activity at work have the same benefits from leisure time physical activity as persons with sedentary work.......To investigate if persons with high physical activity at work have the same benefits from leisure time physical activity as persons with sedentary work....

  7. Development of a model to predict breast cancer survival using data from the National Cancer Data Base.

    Science.gov (United States)

    Asare, Elliot A; Liu, Lei; Hess, Kenneth R; Gordon, Elisa J; Paruch, Jennifer L; Palis, Bryan; Dahlke, Allison R; McCabe, Ryan; Cohen, Mark E; Winchester, David P; Bilimoria, Karl Y

    2016-02-01

    With the large amounts of data on patient, tumor, and treatment factors available to clinicians, it has become critically important to harness this information to guide clinicians in discussing a patient's prognosis. However, no widely accepted survival calculator is available that uses national data and includes multiple prognostic factors. Our objective was to develop a model for predicting survival among patients diagnosed with breast cancer using the National Cancer Data Base (NCDB) to serve as a prototype for the Commission on Cancer's "Cancer Survival Prognostic Calculator." A retrospective cohort of patients diagnosed with breast cancer (2003-2006) in the NCDB was included. A multivariable Cox proportional hazards regression model to predict overall survival was developed. Model discrimination by 10-fold internal cross-validation and calibration was assessed. There were 296,284 patients for model development and internal validation. The c-index for the 10-fold cross-validation ranged from 0.779 to 0.788 after inclusion of all available pertinent prognostic factors. A plot of the observed versus predicted 5 year overall survival showed minimal deviation from the reference line. This breast cancer survival prognostic model to be used as a prototype for building the Commission on Cancer's "Cancer Survival Prognostic Calculator" will offer patients and clinicians an objective opportunity to estimate personalized long-term survival based on patient demographic characteristics, tumor factors, and treatment delivered. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. A Guide to Making Stochastic and Single Point Predictions using the Cold Exposure Survival Model (CESM)

    Science.gov (United States)

    2008-01-01

    connaissances nouvelles qui sont acquises. Son avantage par rapport à d’autres modèles de survie tient au fait qu’il peut être ajusté en fonction de la...l’immersion partielle ou totale dans l’eau. L’ajout de la fonction stochastique a permis d’améliorer les capacités prédictives en calculant la...the point at which Functional Time (FT) and Survival Time (ST) are attained. FT is defined by the deep body temperature when cognitive functions

  9. Enhancing the Lasso Approach for Developing a Survival Prediction Model Based on Gene Expression Data

    Directory of Open Access Journals (Sweden)

    Shuhei Kaneko

    2015-01-01

    Full Text Available In the past decade, researchers in oncology have sought to develop survival prediction models using gene expression data. The least absolute shrinkage and selection operator (lasso has been widely used to select genes that truly correlated with a patient’s survival. The lasso selects genes for prediction by shrinking a large number of coefficients of the candidate genes towards zero based on a tuning parameter that is often determined by a cross-validation (CV. However, this method can pass over (or fail to identify true positive genes (i.e., it identifies false negatives in certain instances, because the lasso tends to favor the development of a simple prediction model. Here, we attempt to monitor the identification of false negatives by developing a method for estimating the number of true positive (TP genes for a series of values of a tuning parameter that assumes a mixture distribution for the lasso estimates. Using our developed method, we performed a simulation study to examine its precision in estimating the number of TP genes. Additionally, we applied our method to a real gene expression dataset and found that it was able to identify genes correlated with survival that a CV method was unable to detect.

  10. Effect of natural hirudin on random pattern skin flap survival in a porcine model.

    Science.gov (United States)

    Zhao, H; Shi, Q; Sun, Z Y; Yin, G Q; Yang, H L

    2012-01-01

    The effect of local administration of hirudin on random pattern skin flap survival was investigated in a porcine model. Three random pattern skin flaps (4 × 14 cm) were created on each flank of five Chinese minipigs. The experimental group (10 flaps) received 20 antithrombin units of hirudin, injected subdermally into the distal half immediately after surgery and on days 1 and 2; a control group (10 flaps) was injected with saline and a sham group (10 flaps) was not injected. All flaps were followed for 10 days postoperatively. Macroscopically, the congested/necrotic length in the experimental group was significantly decreased compared with the other two groups by day 3. Histopathological evaluation revealed venous congestion and inflammation in the control and sham groups from day 1, but minimal changes in the experimental group. By day 10, the mean ± SD surviving area was significantly greater in the experimental group (67.6 ± 2.1%) than in the control (45.2 ± 1.4%) or sham (48.3 ± 1.1%) groups. Local administration of hirudin can significantly increase the surviving area in overdimensioned random pattern skin flaps, in a porcine model.

  11. Inelastic cross section and survival probabilities at the LHC in minijet models

    Science.gov (United States)

    Fagundes, Daniel A.; Grau, Agnes; Pancheri, Giulia; Shekhovtsova, Olga; Srivastava, Yogendra N.

    2017-09-01

    Recent results for the total and inelastic hadronic cross sections from LHC experiments are compared with predictions from a single-channel eikonal minijet model driven by parton density functions and from an empirical model. The role of soft gluon resummation in the infrared region in taming the rise of minijets and their contribution to the increase of the total cross sections at high energies are discussed. Survival probabilities at the LHC, whose theoretical estimates range from circa 10% to a few per mille, are estimated in this model and compared with results from QCD-inspired models and from multichannel eikonal models. We revisit a previous calculation and examine the origin of these discrepancies.

  12. Iron-Chelating Drugs Enhance Cone Photoreceptor Survival in a Mouse Model of Retinitis Pigmentosa.

    Science.gov (United States)

    Wang, Ke; Peng, Bo; Xiao, Jia; Weinreb, Orly; Youdim, Moussa B H; Lin, Bin

    2017-10-01

    Retinitis pigmentosa (RP) is a group of hereditary retinal degeneration in which mutations commonly result in the initial phase of rod cell death followed by gradual cone cell death. The mechanisms by which the mutations lead to photoreceptor cell death in RP have not been clearly elucidated. There is currently no effective treatment for RP. The purpose of this work was to explore iron chelation therapy for improving cone survival and function in the rd10 mouse model of RP. Two iron-chelating drugs, 5-(4-(2-hydroxyethyl) piperazin-1-yl (methyl)-8-hydroxyquinoline (VK28) and its chimeric derivative 5-(N-methyl-N-propargyaminomethyl)-quinoline-8-oldihydrochloride (VAR10303), were injected intraperitoneally to rd10 mice every other day starting from postnatal day 14. We investigate the effects of the two compounds on cone rescue at three time points, using a combination of immunocytochemistry, RT-PCR, Western blot analysis, and a series of visual function tests. VK28 and VAR10303 treatments partially rescued cones, and significantly improved visual function in rd10 mice. Moreover, we showed that the neuroprotective effects of VK28 and VAR10303 were correlated to inhibition of neuroinflammation, oxidative stress, and apoptosis. Furthermore, we demonstrated that downregulation of NF-kB and p53 is likely to be the mechanisms by which proinflammatory mediators and apoptosis are reduced in the rd10 retina, respectively. VK28 and VAR10303 provided partial histologic and functional rescue of cones in RD10 mice. Our study demonstrated that iron chelation therapy might represent an effective therapeutic strategy for RP patients.

  13. Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis.

    Science.gov (United States)

    Crowther, Michael J; Look, Maxime P; Riley, Richard D

    2014-09-28

    Multilevel mixed effects survival models are used in the analysis of clustered survival data, such as repeated events, multicenter clinical trials, and individual participant data (IPD) meta-analyses, to investigate heterogeneity in baseline risk and covariate effects. In this paper, we extend parametric frailty models including the exponential, Weibull and Gompertz proportional hazards (PH) models and the log logistic, log normal, and generalized gamma accelerated failure time models to allow any number of normally distributed random effects. Furthermore, we extend the flexible parametric survival model of Royston and Parmar, modeled on the log-cumulative hazard scale using restricted cubic splines, to include random effects while also allowing for non-PH (time-dependent effects). Maximum likelihood is used to estimate the models utilizing adaptive or nonadaptive Gauss-Hermite quadrature. The methods are evaluated through simulation studies representing clinically plausible scenarios of a multicenter trial and IPD meta-analysis, showing good performance of the estimation method. The flexible parametric mixed effects model is illustrated using a dataset of patients with kidney disease and repeated times to infection and an IPD meta-analysis of prognostic factor studies in patients with breast cancer. User-friendly Stata software is provided to implement the methods. Copyright © 2014 John Wiley & Sons, Ltd.

  14. 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...

  15. Generalized Poisson-Lindely Distribution in Promotion Time Cure Model

    Directory of Open Access Journals (Sweden)

    Ahmad Reza Baghestani

    2014-12-01

    Full Text Available 1024x768 Long-term survival analysis has been improved in the last decade and most of the models concentrate on the promotion time cure model that proposed by Chen (1999. These models are based on the distribution of latent variable N, number of initiated node cells. In this paper we proposed a Generalized Poisson-Lindely distribution that is another option instead of Negative Binomial distribution when there is overdispersion. The results indicated a better fitness compared to others, because of its more flexibility. Parameter estimation has been done by Bayesian approach, in a real data set and a simulation study has shown the advantages of proposed model. Normal 0 false false false /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;}

  16. Modular degradable dendrimers enable small RNAs to extend survival in an aggressive liver cancer model.

    Science.gov (United States)

    Zhou, Kejin; Nguyen, Liem H; Miller, Jason B; Yan, Yunfeng; Kos, Petra; Xiong, Hu; Li, Lin; Hao, Jing; Minnig, Jonathan T; Zhu, Hao; Siegwart, Daniel J

    2016-01-19

    RNA-based cancer therapies are hindered by the lack of delivery vehicles that avoid cancer-induced organ dysfunction, which exacerbates carrier toxicity. We address this issue by reporting modular degradable dendrimers that achieve the required combination of high potency to tumors and low hepatotoxicity to provide a pronounced survival benefit in an aggressive genetic cancer model. More than 1,500 dendrimers were synthesized using sequential, orthogonal reactions where ester degradability was systematically integrated with chemically diversified cores, peripheries, and generations. A lead dendrimer, 5A2-SC8, provided a broad therapeutic window: identified as potent [EC50 75 mg/kg dendrimer repeated dosing). Delivery of let-7 g microRNA (miRNA) mimic inhibited tumor growth and dramatically extended survival. Efficacy stemmed from a combination of a small RNA with the dendrimer's own negligible toxicity, therefore illuminating an underappreciated complication in treating cancer with RNA-based drugs.

  17. Vitamin D Depletion in Pregnancy Decreases Survival Time, Oxygen Saturation, Lung Weight and Body Weight in Preterm Rat Offspring.

    Directory of Open Access Journals (Sweden)

    Sine Lykkedegn

    Full Text Available Animal studies suggest a role of vitamin D in fetal lung development although not studied in preterm animals. We tested the hypothesis that vitamin D depletion aggravates respiratory insufficiency in preterm rat offspring. Furthermore, the effects of vitamin D depletion on growth and lung surfactant were investigated. Female Sprague-Dawley rats were randomly assigned low vitamin D (VDL or control diet before mating and followed with serum 25-hydroxyvitamin D (s-25(OHD determinations. After cesarean section at gestational day 19 (E19 or day 22 (E22, placental weight, birth weight, crown-rump-length (CRL, oxygenation (SaO2 at 30 min and survival time were recorded. The pup lungs were analyzed for phospholipid levels, surfactant protein A-D mRNA and the expression of the vitamin D receptor (VDR. S-25(OHD was significantly lower in the VDL group at cesarean section (12 vs. 30nmol/L, p<0.0001. Compared to the controls, E19 VDL pups had lower birth weight (2.13 vs. 2.29g, p<0.001, lung weight (0.09 vs. 0.10g, p = 0.002, SaO2 (54% vs. 69%, p = 0.002 as well as reduced survival time (0.50 vs. 1.25h, p<0.0001. At E22, the VDL-induced pulmonary differences were leveled out, but VDL pups had lower CRL (4.0 vs. 4.5cm, p<0.0001. The phospholipid levels and the surfactant protein mRNA expression did not differ between the dietary groups. In conclusion, Vitamin D depletion led to lower oxygenation and reduced survival time in the preterm offspring, associated with reduced lung weight and birth weight. Further studies of vitamin D depletion in respiratory insufficiency in preterm neonates are warranted.

  18. The effects of aspirated thickened water on survival and pulmonary injury in a rabbit model.

    Science.gov (United States)

    Nativ-Zeltzer, Nogah; Kuhn, Maggie A; Imai, Denise M; Traslavina, Ryan P; Domer, Amanda S; Litts, Juliana K; Adams, Brett; Belafsky, Peter C

    2018-02-01

    Liquid thickeners are one of the most frequently utilized treatment strategies for persons with oropharyngeal swallowing dysfunction. The effect of commercially available thickeners on lung injury is uncertain. The purpose of this study was to compare the effects of aspiration of water alone, xanthan gum (XG)-thickened water, and cornstarch (CS)-thickened water on survival and lung morphology in a rabbit model. Animal model. Prospective small animal clinical trial. Adult New Zealand White rabbits (n = 24) were divided into three groups of eight rabbits. The groups underwent 3 consecutive days of 1.5 mL/kg intratracheal instillation of water (n = 8), XG-thickened water (n = 8), and CS-thickened water (n = 8). The animals were euthanized on day 4, and survival and pulmonary histopathology were compared between groups. In all, 12.5% of rabbits (n = 8) instilled with CS-thickened water survived until the endpoint of the study (day 4). All animals instilled with water (n = 8) or XG-thickened water (n = 8) survived. A mild increase in intra-alveolar hemorrhage was observed for the animals instilled with CS-thickened water compared to the other groups (P thickened with XG resulted in greater pulmonary inflammation, pulmonary interstitial congestion, and alveolar edema than water alone (P thickened water are fatal, and that XG-thickened water is more injurious than aspirated water alone. Additional research is necessary to further delineate the dangers of aspirated thickened liquids. NA. Laryngoscope, 128:327-331, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

  19. Differential presentation and survival of de novo and recurrent metastatic breast cancer over time: 1990-2010.

    Science.gov (United States)

    Malmgren, Judith A; Mayer, Musa; Atwood, Mary K; Kaplan, Henry G

    2017-10-16

    Differences in de novo (dnMBC) and recurrent metastatic breast cancer (rMBC) presentation and survival over time have not been adequately described. A retrospective cohort study, 1990-2010, with follow up through 2015 of dnMBC patients (stage IV at diagnosis) and rMBC patients with subsequent distant metastatic recurrence (stage I-III initial diagnosis) [dnMBC = 247, rMBC = 911)]. Analysis included Chi squared tests of categorical variables, Kaplan-Meier survival estimates, and Cox proportional adjusted hazard ratios (HzR) and 95% confidence intervals (CI). Disease specific survival (DSS) was time from diagnosis or distant recurrence to BC death. Over time, 1990-1998, 1999-2004, and 2005-2010, dnMBC incidence was constant (3%) and rMBC incidence decreased [18% to 7% (p negative breast cancer (HR-negative/HER2-negative) (p = 0.049). Five-year dnMBC DSS was 44% vs. 21% for rMBC (p year dnMBC DSS improved over time [28% to 55% (p = 0.008)] and rMBC worsened [23% to 13%, p = 0.065)]. Worse DSS was associated with HR-negative status (HzR = 1.63; 1.41, 1.89), rMBC (HzR = 1.88; 1.58, 2.23), older age (70 +) (HzR = 1.88; 1.58, 2.24), > 1 distant metastases (HzR 1.39; 1.20, 1.62), and visceral dominant disease (HzR 1.22; 1.05, 1.43). After 1998, HER2-positive disease was associated with better DSS (HzR = 0.72, 95% CI 0.56, 0.93). Factors associated with the widening survival gap and non-equivalence between dnMBC and rMBC and decreased rMBC incidence warrant further study.

  20. Fisher information framework for time series modeling

    Science.gov (United States)

    Venkatesan, R. C.; Plastino, A.

    2017-08-01

    A robust prediction model invoking the Takens embedding theorem, whose working hypothesis is obtained via an inference procedure based on the minimum Fisher information principle, is presented. The coefficients of the ansatz, central to the working hypothesis satisfy a time independent Schrödinger-like equation in a vector setting. The inference of (i) the probability density function of the coefficients of the working hypothesis and (ii) the establishing of constraint driven pseudo-inverse condition for the modeling phase of the prediction scheme, is made, for the case of normal distributions, with the aid of the quantum mechanical virial theorem. The well-known reciprocity relations and the associated Legendre transform structure for the Fisher information measure (FIM, hereafter)-based model in a vector setting (with least square constraints) are self-consistently derived. These relations are demonstrated to yield an intriguing form of the FIM for the modeling phase, which defines the working hypothesis, solely in terms of the observed data. Cases for prediction employing time series' obtained from the: (i) the Mackey-Glass delay-differential equation, (ii) one ECG signal from the MIT-Beth Israel Deaconess Hospital (MIT-BIH) cardiac arrhythmia database, and (iii) one ECG signal from the Creighton University ventricular tachyarrhythmia database. The ECG samples were obtained from the Physionet online repository. These examples demonstrate the efficiency of the prediction model. Numerical examples for exemplary cases are provided.

  1. Neurocomputational Models of Interval and Pattern Timing.

    Science.gov (United States)

    Hardy, Nicholas F; Buonomano, Dean V

    2016-04-01

    Most of the computations and tasks performed by the brain require the ability to tell time, and process and generate temporal patterns. Thus, there is a diverse set of neural mechanisms in place to allow the brain to tell time across a wide range of scales: from interaural delays on the order of microseconds to circadian rhythms and beyond. Temporal processing is most sophisticated on the scale of tens of milliseconds to a few seconds, because it is within this range that the brain must recognize and produce complex temporal patterns-such as those that characterize speech and music. Most models of timing, however, have focused primarily on simple intervals and durations, thus it is not clear whether they will generalize to complex pattern-based temporal tasks. Here, we review neurobiologically based models of timing in the subsecond range, focusing on whether they generalize to tasks that require placing consecutive intervals in the context of an overall pattern, that is, pattern timing.

  2. 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

  3. Modeling and Understanding Time-Evolving Scenarios

    Directory of Open Access Journals (Sweden)

    Riccardo Melen

    2015-08-01

    Full Text Available In this paper, we consider the problem of modeling application scenarios characterized by variability over time and involving heterogeneous kinds of knowledge. The evolution of distributed technologies creates new and challenging possibilities of integrating different kinds of problem solving methods, obtaining many benefits from the user point of view. In particular, we propose here a multilayer modeling system and adopt the Knowledge Artifact concept to tie together statistical and Artificial Intelligence rule-based methods to tackle problems in ubiquitous and distributed scenarios.

  4. Linear Parametric Model Checking of Timed Automata

    DEFF Research Database (Denmark)

    Hune, Tohmas Seidelin; Romijn, Judi; Stoelinga, Mariëlle

    2001-01-01

    We present an extension of the model checker Uppaal capable of synthesize linear parameter constraints for the correctness of parametric timed automata. The symbolic representation of the (parametric) state-space is shown to be correct. A second contribution of this paper is the identication...... of a subclass of parametric timed automata (L/U automata), for which the emptiness problem is decidable, contrary to the full class where it is know to be undecidable. Also we present a number of lemmas enabling the verication eort to be reduced for L/U automata in some cases. We illustrate our approach...

  5. CD47 blockade reduces ischemia/reperfusion injury and improves survival in a rat liver transplantation model.

    Science.gov (United States)

    Xiao, Zhen-Yu; Banan, Babak; Jia, Jianluo; Manning, Pamela T; Hiebsch, Ronald R; Gunasekaran, Muthukumar; Upadhya, Gundumi A; Frazier, William A; Mohanakumar, Thalachallour; Lin, Yiing; Chapman, William C

    2015-04-01

    Orthotopic liver transplantation (OLT) remains the standard treatment option for nonresponsive liver failure. Because ischemia/reperfusion injury (IRI) is an important impediment to the success of OLT, new therapeutic strategies are needed to reduce IRI. We investigated whether blocking the CD47/thrombospondin-1 inhibitory action on nitric oxide signaling with a monoclonal antibody specific to CD47 (CD47mAb400) would reduce IRI in liver grafts. Syngeneic OLT was performed with Lewis rats. Control immunoglobulin G or CD47mAb400 was administered to the donor organ at procurement or to both the organ and the recipient at the time of transplant. Serum transaminases, histological changes of the liver, and animal survival were assessed. Oxidative stress, inflammatory responses, and hepatocellular damage were also quantified. A significant survival benefit was not achieved when CD47mAb400 was administered to the donor alone. However, CD47mAb400 administration to both the donor and the recipient increased animal survival afterward. The CD47mAb400-treated group showed lower serum transaminases, bilirubin, oxidative stress, terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick-end labeling staining, caspase-3 activity, and proinflammatory cytokine expression of tumor necrosis factor α, interleukin-1β, and interleukin-6. Thus, CD47 blockade with CD47mAb400 administered both to the donor and the recipient reduced liver graft IRI in a rat liver transplantation model. This may translate to decreased liver dysfunction and increased survival of liver transplant recipients. © 2015 American Association for the Study of Liver Diseases.

  6. Comparison of Cox Regression and Parametric Models: Application for Assessment of Survival of Pediatric Cases of Acute Leukemia in Southern Iran

    Science.gov (United States)

    Hosseini Teshnizi, Saeed; Taghi Ayatollahi, Seyyed Mohammad

    2017-04-01

    Background: Finding the most appropriate regression model for survival data in cancer casesin order to determine prognosis is an important issue in medical research. Here we compare Cox and parametric regression models regarding survival of children with acute leukemia in southern Iran. Methods: In a retrospective cohort study, information for 197 children with acute leukemia over 6 years was collected through observation and interviews. In order to identify factors affecting their survival, the Cox and parametric (exponential, Weibull, log-logistic, log-normal, Gompertz and generalized gamma) models were fitted to the data. To find the best predictor model, the Akaike’s information criterion (AIC) and the Coxsnell residual were employed. Results: Out of 197 children, 164 (83.3%) had ALL and 33 (16.7%) AML; the mean (± standard deviation) survival time was 52.1±8.10 months. According to both the AIC and the Coxsnell residual, the Cox regression model was the weakest and the log-normal and Weibull models were the best for fitting to data. Based on the log-normal model, age (HR=1.01, p=0.004), residence area (HR=1.60, p=0.038) and WBC (White Blood Cell) (HR=1.57, p=0.014) had significant effects on patient survival. Conclusion: Parametric regression models demonstrate better performance as compared to the Cox model for identifying risk factors for prognosis with acute leukemia data. Just because the assumption of PH (Proportional Hazards) is held for the Cox regression model, we should not ignore parameter models. Creative Commons Attribution License

  7. Towards an Extended Evolutionary Game Theory with Survival Analysis and Agreement Algorithms for Modeling Uncertainty, Vulnerability, and Deception

    Science.gov (United States)

    Ma, Zhanshan (Sam)

    Competition, cooperation and communication are the three fundamental relationships upon which natural selection acts in the evolution of life. Evolutionary game theory (EGT) is a 'marriage' between game theory and Darwin's evolution theory; it gains additional modeling power and flexibility by adopting population dynamics theory. In EGT, natural selection acts as optimization agents and produces inherent strategies, which eliminates some essential assumptions in traditional game theory such as rationality and allows more realistic modeling of many problems. Prisoner's Dilemma (PD) and Sir Philip Sidney (SPS) games are two well-known examples of EGT, which are formulated to study cooperation and communication, respectively. Despite its huge success, EGT exposes a certain degree of weakness in dealing with time-, space- and covariate-dependent (i.e., dynamic) uncertainty, vulnerability and deception. In this paper, I propose to extend EGT in two ways to overcome the weakness. First, I introduce survival analysis modeling to describe the lifetime or fitness of game players. This extension allows more flexible and powerful modeling of the dynamic uncertainty and vulnerability (collectively equivalent to the dynamic frailty in survival analysis). Secondly, I introduce agreement algorithms, which can be the Agreement algorithms in distributed computing (e.g., Byzantine Generals Problem [6][8], Dynamic Hybrid Fault Models [12]) or any algorithms that set and enforce the rules for players to determine their consensus. The second extension is particularly useful for modeling dynamic deception (e.g., asymmetric faults in fault tolerance and deception in animal communication). From a computational perspective, the extended evolutionary game theory (EEGT) modeling, when implemented in simulation, is equivalent to an optimization methodology that is similar to evolutionary computing approaches such as Genetic algorithms with dynamic populations [15][17].

  8. Modelling of Patterns in Space and Time

    CERN Document Server

    Murray, James

    1984-01-01

    This volume contains a selection of papers presented at the work­ shop "Modelling of Patterns in Space and Time", organized by the 80nderforschungsbereich 123, "8tochastische Mathematische Modelle", in Heidelberg, July 4-8, 1983. The main aim of this workshop was to bring together physicists, chemists, biologists and mathematicians for an exchange of ideas and results in modelling patterns. Since the mathe­ matical problems arising depend only partially on the particular field of applications the interdisciplinary cooperation proved very useful. The workshop mainly treated phenomena showing spatial structures. The special areas covered were morphogenesis, growth in cell cultures, competition systems, structured populations, chemotaxis, chemical precipitation, space-time oscillations in chemical reactors, patterns in flames and fluids and mathematical methods. The discussions between experimentalists and theoreticians were especially interesting and effective. The editors hope that these proceedings reflect ...

  9. Intratumoral delivery of bortezomib: impact on survival in an intracranial glioma tumor model.

    Science.gov (United States)

    Wang, Weijun; Cho, Hee-Yeon; Rosenstein-Sisson, Rachel; Marín Ramos, Nagore I; Price, Ryan; Hurth, Kyle; Schönthal, Axel H; Hofman, Florence M; Chen, Thomas C

    2017-04-14

    OBJECTIVE Glioblastoma (GBM) is the most prevalent and the most aggressive of primary brain tumors. There is currently no effective treatment for this tumor. The proteasome inhibitor bortezomib is effective for a variety of tumors, but not for GBM. The authors' goal was to demonstrate that bortezomib can be effective in the orthotopic GBM murine model if the appropriate method of drug delivery is used. In this study the Alzet mini-osmotic pump was used to bring the drug directly to the tumor in the brain, circumventing the blood-brain barrier; thus making bortezomib an effective treatment for GBM. METHODS The 2 human glioma cell lines, U87 and U251, were labeled with luciferase and used in the subcutaneous and intracranial in vivo tumor models. Glioma cells were implanted subcutaneously into the right flank, or intracranially into the frontal cortex of athymic nude mice. Mice bearing intracranial glioma tumors were implanted with an Alzet mini-osmotic pump containing different doses of bortezomib. The Alzet pumps were introduced directly into the tumor bed in the brain. Survival was documented for mice with intracranial tumors. RESULTS Glioma cells were sensitive to bortezomib at nanomolar quantities in vitro. In the subcutaneous in vivo xenograft tumor model, bortezomib given intravenously was effective in reducing tumor progression. However, in the intracranial glioma model, bortezomib given systemically did not affect survival. By sharp contrast, animals treated with bortezomib intracranially at the tumor site exhibited significantly increased survival. CONCLUSIONS Bypassing the blood-brain barrier by using the osmotic pump resulted in an increase in the efficacy of bortezomib for the treatment of intracranial tumors. Thus, the intratumoral administration of bortezomib into the cranial cavity is an effective approach for glioma therapy.

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

    DEFF Research Database (Denmark)

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

    neutrons, stopped pions, and heavy ion beams. Nucl Instrum Meth. 1973;111:93-116. 2.Weyrather WK, Kraft G. RBE of carbon ions: experimental data and the strategy of RBE calculation for treatment planning. Radiother Oncol. 2004;73(Suppl 2):161-9. 3.Greilich S, Grzanka L, Bassler N, Andersen CE, Jäkel O......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...

  11. Space-time modeling of soil moisture

    Science.gov (United States)

    Chen, Zijuan; Mohanty, Binayak P.; Rodriguez-Iturbe, Ignacio

    2017-11-01

    A physically derived space-time mathematical representation of the soil moisture field is carried out via the soil moisture balance equation driven by stochastic rainfall forcing. The model incorporates spatial diffusion and in its original version, it is shown to be unable to reproduce the relative fast decay in the spatial correlation functions observed in empirical data. This decay resulting from variations in local topography as well as in local soil and vegetation conditions is well reproduced via a jitter process acting multiplicatively over the space-time soil moisture field. The jitter is a multiplicative noise acting on the soil moisture dynamics with the objective to deflate its correlation structure at small spatial scales which are not embedded in the probabilistic structure of the rainfall process that drives the dynamics. These scales of order of several meters to several hundred meters are of great importance in ecohydrologic dynamics. Properties of space-time correlation functions and spectral densities of the model with jitter are explored analytically, and the influence of the jitter parameters, reflecting variabilities of soil moisture at different spatial and temporal scales, is investigated. A case study fitting the derived model to a soil moisture dataset is presented in detail.

  12. Lipid emulsion improves survival in animal models of local anesthetic toxicity: a meta-analysis.

    Science.gov (United States)

    Fettiplace, Michael R; McCabe, Daniel J

    2017-08-01

    The Lipid Emulsion Therapy workgroup, organized by the American Academy of Clinical Toxicology, recently conducted a systematic review, which subjectively evaluated lipid emulsion as a treatment for local anesthetic toxicity. We re-extracted data and conducted a meta-analysis of survival in animal models. We extracted survival data from 26 publications and conducted a random-effect meta-analysis based on odds ratio weighted by inverse variance. We assessed the benefit of lipid emulsion as an independent variable in resuscitative models (16 studies). We measured Cochran's Q for heterogeneity and I2 to determine variance contributed by heterogeneity. Finally, we conducted a funnel plot analysis and Egger's test to assess for publication bias in studies. Lipid emulsion reduced the odds of death in resuscitative models (OR =0.24; 95%CI: 0.1-0.56, p = .0012). Heterogeneity analysis indicated a homogenous distribution. Funnel plot analysis did not indicate publication bias in experimental models. Meta-analysis of animal data supports the use of lipid emulsion (in combination with other resuscitative measures) for the treatment of local anesthetic toxicity, specifically from bupivacaine. Our conclusion differed from the original review. Analysis of outliers reinforced the need for good life support measures (securement of airway and chest compressions) along with prompt treatment with lipid.

  13. Integrative genomic testing of cancer survival using semiparametric linear transformation models.

    Science.gov (United States)

    Huang, Yen-Tsung; Cai, Tianxi; Kim, Eunhee

    2016-07-20

    The wide availability of multi-dimensional genomic data has spurred increasing interests in integrating multi-platform genomic data. Integrative analysis of cancer genome landscape can potentially lead to deeper understanding of the biological process of cancer. We integrate epigenetics (DNA methylation and microRNA expression) and gene expression data in tumor genome to delineate the association between different aspects of the biological processes and brain tumor survival. To model the association, we employ a flexible semiparametric linear transformation model that incorporates both the main effects of these genomic measures as well as the possible interactions among them. We develop variance component tests to examine different coordinated effects by testing various subsets of model coefficients for the genomic markers. A Monte Carlo perturbation procedure is constructed to approximate the null distribution of the proposed test statistics. We further propose omnibus testing procedures to synthesize information from fitting various parsimonious sub-models to improve power. Simulation results suggest that our proposed testing procedures maintain proper size under the null and outperform standard score tests. We further illustrate the utility of our procedure in two genomic analyses for survival of glioblastoma multiforme patients. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  14. Influence analysis for skew-normal semiparametric joint models of multivariate longitudinal and multivariate survival data.

    Science.gov (United States)

    Tang, An-Min; Tang, Nian-Sheng; Zhu, Hongtu

    2017-04-30

    The normality assumption of measurement error is a widely used distribution in joint models of longitudinal and survival data, but it may lead to unreasonable or even misleading results when longitudinal data reveal skewness feature. This paper proposes a new joint model for multivariate longitudinal and multivariate survival data by incorporating a nonparametric function into the trajectory function and hazard function and assuming that measurement errors in longitudinal measurement models follow a skew-normal distribution. A Monte Carlo Expectation-Maximization (EM) algorithm together with the penalized-splines technique and the Metropolis-Hastings algorithm within the Gibbs sampler is developed to estimate parameters and nonparametric functions in the considered joint models. Case deletion diagnostic measures are proposed to identify the potential influential observations, and an extended local influence method is presented to assess local influence of minor perturbations. Simulation studies and a real example from a clinical trial are presented to illustrate the proposed methodologies. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  15. Time series modeling for syndromic surveillance

    Directory of Open Access Journals (Sweden)

    Mandl Kenneth D

    2003-01-01

    Full Text Available Abstract Background Emergency department (ED based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates. Methods Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks. Results Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity. Conclusions Time series methods applied to historical ED utilization data are an important tool

  16. Assessment of chronic effects of tebuconazole on survival, reproduction and growth of Daphnia magna after different exposure times.

    Science.gov (United States)

    Sancho, E; Villarroel, M J; Ferrando, M D

    2016-02-01

    The effect of the fungicide tebuconazole (0.41, 0.52, 0.71 and 1.14mg/L) on survival, reproduction and growth of Daphnia magna organisms was monitored using 14 and 21 days exposure tests. A third experiment was performed by exposing D. magna to the fungicide for 14 days followed by 7 days of recovery (14+7). In order to test fungicide effects on D. magna, parameters as survival, mean whole body length, mean total number of neonates per female, mean number of broods per female, mean brood size per female, time to first brood/reproduction and intrinsic rate of natural increase (r) were used. Reproduction was seriously affected by tebuconazole. All tebuconazole concentrations tested affected the number of broods per female and day to first brood. At 14-days test, number of neonates per female and body size decreased by concentrations of tebuconazole higher than 0.52mg/L, whereas at 21-days test both parameters were affected at all the concentrations tested. Survival of the daphnids after 14 days fungicide exposure did not exhibited differences among experimental and control groups. In this experiment r value was reduced (in a 22%) when animals were exposed to concentrations of 0.71mg/L and 1.14mg/L. Survival of daphnids exposed during 21 days to 1.14mg/L declined, and the intrinsic rate of natural increase (r) decreased in a 30 % for tebuconazole concentrations higher than 0.41mg/L. Longevity of daphnids pre-exposed to tebuconazole for 14 days and 7 days in clean water did not show differences from control values and all of them survived the 21 days of the test. However, after 7 days in fungicide free medium animals were unable to restore control values for reproductive parameters and length. The maximum acceptable toxicant concentration (MATC) was calculated using the r values as parameter of evaluation. MATC estimations were 0.61mg/L and 0.46mg/L for 14 and 21 days, respectively. Results showed that the number of neonates per female was the highest sensitive

  17. Effect of increased ovulation rate on embryo and foetal survival as a model for selection by ovulation rate in rabbits

    Directory of Open Access Journals (Sweden)

    A.Y. Badawy

    2016-06-01

    Full Text Available Selection for ovulation rate in prolific species has not improved litter size, due to an increase in prenatal mortality, with most mortality observed in the foetal period. The aim of this study was to investigate the magnitude and timing of embryo and early foetal survival in females with high ovulation rate using hormonal treatment as a model for selection by ovulation rate. Two groups of females (treated and untreated were used. Treated females were injected with 50 IU equine chorionic gonadotropin 48 h before mating. Females were slaughtered at 18 d of gestation. Ovulation rate (OR, number of implanted embryos (IE, number of live foetuses at 12 and 18 d (LF12 and LF18, respectively were recorded. In addition, embryo survival (ES=IE/OR, foetal survival at 18 d of gestation (FSLF18=LF18/IE, foetal survival between 12 and 18 d of gestation (FSLF18/LF12=LF18/LF12 and prenatal survival (PSLF18=LF18/OR were estimated. For each female, the mean and variability of the weight for live foetuses (LFWm and LFWv, respectively and their placentas (LFPWm and LFPWv, respectively were calculated. Treated females had a higher ovulation rate (+3.02 ova than untreated females, with a probability of 0.99. An increase in the differences (D between treated and untreated females was observed from implantation to 18 d of gestation (D=–0.33, –0.70 and –1.28 for IE, LF12 and LF18, respectively. These differences had a low accuracy and the probability that treated females would have a lower number of foetuses also increased throughout gestation (0.60, 0.70 and 0.86 for IE, LF12 and LF18, respectively. According to the previous results for OR and LF18, treated females showed a lower survival rate from ovulation to 18 d of gestation (D=–0.12, P=0.98 for PSLF18. Treated females also had lower embryo and foetal survival (D=–0.10 and P=0.94 for ES and D=–0.08 and P=0.93 for FSLF18. Main differences in foetal survival appeared from 12 to 18 d of gestation (D=–0

  18. Resveratrol improves survival, hemodynamics and energetics in a rat model of hypertension leading to heart failure.

    Science.gov (United States)

    Rimbaud, Stéphanie; Ruiz, Matthieu; Piquereau, Jérôme; Mateo, Philippe; Fortin, Dominique; Veksler, Vladimir; Garnier, Anne; Ventura-Clapier, Renée

    2011-01-01

    Heart failure (HF) is characterized by contractile dysfunction associated with altered energy metabolism. This study was aimed at determining whether resveratrol, a polyphenol known to activate energy metabolism, could be beneficial as a metabolic therapy of HF. Survival, ventricular and vascular function as well as cardiac and skeletal muscle energy metabolism were assessed in a hypertensive model of HF, the Dahl salt-sensitive rat fed with a high-salt diet (HS-NT). Resveratrol (18 mg/kg/day; HS-RSV) was given for 8 weeks after hypertension and cardiac hypertrophy were established (which occurred 3 weeks after salt addition). Resveratrol treatment improved survival (64% in HS-RSV versus 15% in HS-NT, phypertension or hypertrophy. Moreover, aortic endothelial dysfunction present in HS-NT was prevented in resveratrol-treated rats. Resveratrol treatment tended to preserve mitochondrial mass and biogenesis and completely protected mitochondrial fatty acid oxidation and PPARα (peroxisome proliferator-activated receptor α) expression. We conclude that resveratrol treatment exerts beneficial protective effects on survival, endothelium-dependent smooth muscle relaxation and cardiac contractile and mitochondrial function, suggesting that resveratrol or metabolic activators could be a relevant therapy in hypertension-induced HF.

  19. Modeling of thermal stresses and probability of survival of tubular SOFC

    Energy Technology Data Exchange (ETDEWEB)

    Nakajo, Arata [Laboratory for Industrial Energy Systems (LENI), Faculty of Engineering, Swiss Federal Institute of Technology, 1015 Lausanne (Switzerland); Stiller, Christoph; Bolland, Olav [Department of Energy and Process Engineering, Norwegian University of Science and Technology, Trondheim N-7491 (Norway); Haerkegaard, Gunnar [Department of Engineering Design and Materials, Norwegian University of Science and Technology, Trondheim N-7491 (Norway)

    2006-07-14

    The temperature profile generated by a thermo-electro-chemical model was used to calculate the thermal stress distribution in a tubular solid oxide fuel cell (SOFC). The solid heat balances were calculated separately for each layer of the MEA (membrane electrode assembly) in order to detect the radial thermal gradients more precisely. It appeared that the electrolyte undergoes high tensile stresses at the ends of the cell in limited areas and that the anode is submitted to moderate tensile stresses. A simplified version of the widely used Weibull analysis was used to calculate the global probability of survival for the assessment of the risks related to both operating points and load changes. The cell at room temperature was considered and revealed as critical. As a general trend, the computed probabilities of survival were too low for the typical requirements for a commercial product. A sensitivity analysis showed a strong influence of the thermal expansion mismatch between the layers of the MEA on the probability of survival. The lack of knowledge on mechanical material properties as well as uncertainties about the phenomena occurring in the cell revealed itself as a limiting parameter for the simulation of thermal stresses. (author)

  20. Resveratrol improves survival, hemodynamics and energetics in a rat model of hypertension leading to heart failure.

    Directory of Open Access Journals (Sweden)

    Stéphanie Rimbaud

    Full Text Available Heart failure (HF is characterized by contractile dysfunction associated with altered energy metabolism. This study was aimed at determining whether resveratrol, a polyphenol known to activate energy metabolism, could be beneficial as a metabolic therapy of HF. Survival, ventricular and vascular function as well as cardiac and skeletal muscle energy metabolism were assessed in a hypertensive model of HF, the Dahl salt-sensitive rat fed with a high-salt diet (HS-NT. Resveratrol (18 mg/kg/day; HS-RSV was given for 8 weeks after hypertension and cardiac hypertrophy were established (which occurred 3 weeks after salt addition. Resveratrol treatment improved survival (64% in HS-RSV versus 15% in HS-NT, p<0.001, and prevented the 25% reduction in body weight in HS-NT (P<0.001. Moreover, RSV counteracted the development of cardiac dysfunction (fractional shortening -34% in HS-NT as evaluated by echocardiography, which occurred without regression of hypertension or hypertrophy. Moreover, aortic endothelial dysfunction present in HS-NT was prevented in resveratrol-treated rats. Resveratrol treatment tended to preserve mitochondrial mass and biogenesis and completely protected mitochondrial fatty acid oxidation and PPARα (peroxisome proliferator-activated receptor α expression. We conclude that resveratrol treatment exerts beneficial protective effects on survival, endothelium-dependent smooth muscle relaxation and cardiac contractile and mitochondrial function, suggesting that resveratrol or metabolic activators could be a relevant therapy in hypertension-induced HF.

  1. Resveratrol Improves Survival, Hemodynamics and Energetics in a Rat Model of Hypertension Leading to Heart Failure

    Science.gov (United States)

    Rimbaud, Stéphanie; Ruiz, Matthieu; Piquereau, Jérôme; Mateo, Philippe; Fortin, Dominique; Veksler, Vladimir; Garnier, Anne; Ventura-Clapier, Renée

    2011-01-01

    Heart failure (HF) is characterized by contractile dysfunction associated with altered energy metabolism. This study was aimed at determining whether resveratrol, a polyphenol known to activate energy metabolism, could be beneficial as a metabolic therapy of HF. Survival, ventricular and vascular function as well as cardiac and skeletal muscle energy metabolism were assessed in a hypertensive model of HF, the Dahl salt-sensitive rat fed with a high-salt diet (HS-NT). Resveratrol (18 mg/kg/day; HS-RSV) was given for 8 weeks after hypertension and cardiac hypertrophy were established (which occurred 3 weeks after salt addition). Resveratrol treatment improved survival (64% in HS-RSV versus 15% in HS-NT, p<0.001), and prevented the 25% reduction in body weight in HS-NT (P<0.001). Moreover, RSV counteracted the development of cardiac dysfunction (fractional shortening −34% in HS-NT) as evaluated by echocardiography, which occurred without regression of hypertension or hypertrophy. Moreover, aortic endothelial dysfunction present in HS-NT was prevented in resveratrol-treated rats. Resveratrol treatment tended to preserve mitochondrial mass and biogenesis and completely protected mitochondrial fatty acid oxidation and PPARα (peroxisome proliferator-activated receptor α) expression. We conclude that resveratrol treatment exerts beneficial protective effects on survival, endothelium–dependent smooth muscle relaxation and cardiac contractile and mitochondrial function, suggesting that resveratrol or metabolic activators could be a relevant therapy in hypertension-induced HF. PMID:22028869

  2. 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.

  3. Predictive model for survival and growth of Salmonella typhimurium DT104 on chicken skin during temperature abuse.

    Science.gov (United States)

    Oscar, T P

    2009-02-01

    To better predict risk of Salmonella infection from chicken subjected to temperature abuse, a study was undertaken to develop a predictive model for survival and growth of Salmonella Typhimurium DT104 on chicken skin with native flora. For model development, chicken skin portions (2.14 cm2) were inoculated with 0.85 log of Salmonella Typhimurium DT104 (ATCC 700408) and then stored at 5 to 50 degrees C for 8 h. Kinetic data from the storage trials were fit to a primary model to determine lag time (lamda), specific growth rate (micrro), and the 95% prediction interval (PI). Secondary models for lamda, mu, and PI as a function of storage temperature were developed and then combined with the primary model to create a tertiary model. Performance of the tertiary model was evaluated against dependent data, independent data for interpolation, and independent data for extrapolation to kosher chicken skin by using an acceptable prediction zone from -1 (fail-safe) to 0.5 (fail-dangerous) log per skin portion. Survival of Salmonella Typhimurium DT104 on chicken skin was observed during 8 h of storage at 5 to 20 degrees C and at 50 degrees C, whereas growth was observed from 25 to 45 degrees C and was optimal at 40 degrees C with a lamda of 2.5 h and a mu of 1.1 log/h. Variation of pathogen growth, as assessed by PI, increased in a nonlinear manner as a function of temperature and was greater for growth conditions than no-growth conditions. The percentage of acceptable prediction errors was 82.6% for dependent data, 83.7% for independent data for interpolation, and 81.6% for independent data for extrapolation to kosher skin, which all exceeded the performance criterion of 70% acceptable predictions. Thus, it was concluded that the tertiary model provided valid predictions for survival and growth of Salmonella Typhimurium DT104 from a low initial dose on both nonkosher and kosher chicken skin with native flora.

  4. Vitamin D Depletion in Pregnancy Decreases Survival Time, Oxygen Saturation, Lung Weight and Body Weight in Preterm Rat Offspring.

    Science.gov (United States)

    Lykkedegn, Sine; Sorensen, Grith Lykke; Beck-Nielsen, Signe Sparre; Pilecki, Bartosz; Duelund, Lars; Marcussen, Niels; Christesen, Henrik Thybo

    2016-01-01

    Animal studies suggest a role of vitamin D in fetal lung development although not studied in preterm animals. We tested the hypothesis that vitamin D depletion aggravates respiratory insufficiency in preterm rat offspring. Furthermore, the effects of vitamin D depletion on growth and lung surfactant were investigated. Female Sprague-Dawley rats were randomly assigned low vitamin D (VDL) or control diet before mating and followed with serum 25-hydroxyvitamin D (s-25(OH)D) determinations. After cesarean section at gestational day 19 (E19) or day 22 (E22), placental weight, birth weight, crown-rump-length (CRL), oxygenation (SaO2) at 30 min and survival time were recorded. The pup lungs were analyzed for phospholipid levels, surfactant protein A-D mRNA and the expression of the vitamin D receptor (VDR). S-25(OH)D was significantly lower in the VDL group at cesarean section (12 vs. 30nmol/L, plung weight (0.09 vs. 0.10g, p = 0.002), SaO2 (54% vs. 69%, p = 0.002) as well as reduced survival time (0.50 vs. 1.25h, plung weight and birth weight. Further studies of vitamin D depletion in respiratory insufficiency in preterm neonates are warranted.

  5. Survival benefits of antiretroviral therapy in Brazil: a model-based analysis

    Science.gov (United States)

    Luz, Paula M; Girouard, Michael P; Grinsztejn, Beatriz; Freedberg, Kenneth A; Veloso, Valdilea G; Losina, Elena; Struchiner, Claudio J; MacLean, Rachel L; Parker, Robert A; Paltiel, A David; Walensky, Rochelle P

    2016-01-01

    Objective In Brazil, universal provision of antiretroviral therapy (ART) has been guaranteed free of charge to eligible HIV-positive patients since December 1996. We sought to quantify the survival benefits of ART attributable to this programme. Methods We used a previously published microsimulation model of HIV disease and treatment (CEPAC-International) and data from Brazil to estimate life expectancy increase for HIV-positive patients initiating ART in Brazil. We divided the period of 1997 to 2014 into six eras reflecting increased drug regimen efficacy, regimen availability and era-specific mean CD4 count at ART initiation. Patients were simulated first without ART and then with ART. The 2014-censored and lifetime survival benefits attributable to ART in each era were calculated as the product of the number of patients initiating ART in a given era and the increase in life expectancy attributable to ART in that era. Results In total, we estimated that 598,741 individuals initiated ART. Projected life expectancy increased from 2.7, 3.3, 4.1, 4.9, 5.5 and 7.1 years without ART to 11.0, 17.5, 20.7, 23.0, 25.3, and 27.0 years with ART in Eras 1 through 6, respectively. Of the total projected lifetime survival benefit of 9.3 million life-years, 16% (or 1.5 million life-years) has been realized as of December 2014. Conclusions Provision of ART through a national programme has led to dramatic survival benefits in Brazil, the majority of which are still to be realized. Improvements in initial and subsequent ART regimens and higher CD4 counts at ART initiation have contributed to these increasing benefits. PMID:27029828

  6. Should we use standard survival models or the illness-death model for interval-censored data to investigate risk factors of chronic kidney disease progression?

    Science.gov (United States)

    Boucquemont, Julie; Metzger, Marie; Combe, Christian; Stengel, Bénédicte; Leffondre, Karen

    2014-01-01

    In studies investigating risk factors of chronic kidney disease (CKD) progression, one may be interested in estimating factors effects on both a fall of glomerular filtration rate (GFR) below a specific level (i.e., a CKD stage) and death. Such studies have to account for the fact that GFR is measured at intermittent visit only, which implies that progression to the stage of interest is unknown for patients who die before being observed at that stage. Our objective was to compare the results of an illness-death model that handles this uncertainty, with frequently used survival models. This study included 1,519 patients from the NephroTest cohort with CKD stages 1-4 at baseline (69% males, 59±15 years, median protein/creatinine ratio [PCR] 27.4 mg/mmol) and subsequent annual measures of GFR (follow-up time 4.3±2.7 years). Each model was used to estimate the effects of sex, age, PCR, and GFR at baseline on the hazards of progression to CKD stage 5 (GFRmodels. The differences between results were higher for the hazard of death before or after progression. Our results also suggest that previous findings on the effect of age on end-stage renal disease are more likely due to a strong impact of age on death than to an effect on progression. The probabilities of progression were systematically under-estimated with the survival model as compared with the illness-death model. This study illustrates the advantages of the illness-death model for accurately estimating the effects of risk factors on the hazard of progression and death, and probabilities of progression. It avoids the need to choose arbitrary time-to-event and time-to-censoring, while accounting for both interval censoring and competition by death, using a single analytical model.

  7. Discrete mixture modeling to address genetic heterogeneity in time-to-event regression.

    Science.gov (United States)

    Eng, Kevin H; Hanlon, Bret M

    2014-06-15

    Time-to-event regression models are a critical tool for associating survival time outcomes with molecular data. Despite mounting evidence that genetic subgroups of the same clinical disease exist, little attention has been given to exploring how this heterogeneity affects time-to-event model building and how to accommodate it. Methods able to diagnose and model heterogeneity should be valuable additions to the biomarker discovery toolset. We propose a mixture of survival functions that classifies subjects with similar relationships to a time-to-event response. This model incorporates multivariate regression and model selection and can be fit with an expectation maximization algorithm, we call Cox-assisted clustering. We illustrate a likely manifestation of genetic heterogeneity and demonstrate how it may affect survival models with little warning. An application to gene expression in ovarian cancer DNA repair pathways illustrates how the model may be used to learn new genetic subsets for risk stratification. We explore the implications of this model for censored observations and the effect on genomic predictors and diagnostic analysis. R implementation of CAC using standard packages is available at https://gist.github.com/programeng/8620b85146b14b6edf8f Data used in the analysis are publicly available. © The Author 2014. Published by Oxford University Press.

  8. Time Dynamic Modeling and Inference Approaches for Outcomes in Patients on Dialysis

    OpenAIRE

    Estes, Jason

    2015-01-01

    In the first chapter of this work, we characterize the dynamics of cardiovascular event risk trajectories for patients on dialysis while conditioning on survival status via multiple time indices: (1) time since the start of dialysis, (2) time since the pivotal initial infection-related hospitalization and (3) the patient's age at the start of dialysis. This is achieved by using a new class of generalized multiple-index varying coefficient (GM-IVC) models utilizing a multiplicative structure a...

  9. Human Engineered Heart Muscles Engraft and Survive Long-Term in a Rodent Myocardial Infarction Model

    Science.gov (United States)

    Riegler, Johannes; Tiburcy, Malte; Ebert, Antje; Tzatzalos, Evangeline; Raaz, Uwe; Abilez, Oscar J.; Shen, Qi; Kooreman, Nigel G.; Neofytou, Evgenios; Chen, Vincent C.; Wang, Mouer; Meyer, Tim; Tsao, Philip S.; Connolly, Andrew J.; Couture, Larry A.; Gold, Joseph D.; Zimmermann, Wolfram H.; Wu, Joseph C.

    2015-01-01

    Rational Tissue engineering approaches may improve survival and functional benefits from human embryonic stem cell-derived cardiomyocte (ESC-CM) transplantation, thereby potentially preventing dilative remodelling and progression to heart failure. Objective Assessment of transport stability, long term survival, structural organisation, functional benefits, and teratoma risk of engineered heart muscle (EHM) in a chronic myocardial infarction (MI) model. Methods and Results We constructed EHMs from ESC-CMs and released them for transatlantic shipping following predefined quality control criteria. Two days of shipment did not lead to adverse effects on cell viability or contractile performance of EHMs (n=3, P=0.83, P=0.87). After ischemia/reperfusion (I/R) injury, EHMs were implanted onto immunocompromised rat hearts at 1 month to simulate chronic ischemia. Bioluminescence imaging (BLI) showed stable engraftment with no significant cell loss between week 2 and 12 (n=6, P=0.67), preserving up to 25% of the transplanted cells. Despite high engraftment rates and attenuated disease progression (change in ejection fraction for EHMs −6.7±1.4% vs control −10.9±1.5%, n>12, P=0.05), we observed no difference between EHMs containing viable or non-viable human cardiomyocytes in this chronic xenotransplantation model (n>12, P=0.41). Grafted cardiomyocytes showed enhanced sarcomere alignment and increased connexin 43 expression at 220 days after transplantation. No teratomas or tumors were found in any of the animals (n=14) used for long-term monitoring. Conclusions EHM transplantation led to high engraftment rates, long term survival, and progressive maturation of human cardiomyocytes. However, cell engraftment was not correlated with functional improvements in this chronic MI model. Most importantly, the safety of this approach was demonstrated by the lack of tumor or teratoma formation. PMID:26291556

  10. Modeling of wireless networks using multivariate time models

    Science.gov (United States)

    Rozal, Edilberto; Pelaes, Evaldo; Queiroz, Joaquim; Salame, Camil

    2012-12-01

    The literature analysis of propagation models has investigated different statistical prediction methods to identify appropriate techniques for this purpose. This article presents the results of propagation channel modeling, based on multivariate time series models using data collected in measurement campaigns and the main characteristics of urbanization in the city of Belém-PA. Transfer function models were used to evaluate the relationship between received power signal and other variables, such as the height of buildings, the distance between buildings, and the distance to the radio base station. A multivariate model was designed in which the contributions due to the height of the buildings and the distance between buildings had a significant effect on the received power signal. The utilized sample failed to identify the contribution of distance to the source for the received power signal. The result obtained with the proposed model appeared to be accurate for the samples used in the study.

  11. A comparative study of two food model systems to test the survival of Campylobacter jejuni at -18 degrees C

    DEFF Research Database (Denmark)

    Birk, Tina; Rosenquist, Hanne; Brondsted, L.

    2006-01-01

    The survival of Campylobacter jejuni NCTC 11168 was tested at freezing conditions (-18 degrees C) over a period of 32 days in two food models that simulated either (i) the chicken skin surface (skin model) or (ii) the chicken juice in and around a broiler carcass (liquid model). In the skin model...

  12. Cell-delivered magnetic nanoparticles caused hyperthermia-mediated increased survival in a murine pancreatic cancer model.

    Science.gov (United States)

    Basel, Matthew T; Balivada, Sivasai; Wang, Hongwang; Shrestha, Tej B; Seo, Gwi Moon; Pyle, Marla; Abayaweera, Gayani; Dani, Raj; Koper, Olga B; Tamura, Masaaki; Chikan, Viktor; Bossmann, Stefan H; Troyer, Deryl L

    2012-01-01

    Using magnetic nanoparticles to absorb alternating magnetic field energy as a method of generating localized hyperthermia has been shown to be a potential cancer treatment. This report demonstrates a system that uses tumor homing cells to actively carry iron/iron oxide nanoparticles into tumor tissue for alternating magnetic field treatment. Paramagnetic iron/ iron oxide nanoparticles were synthesized and loaded into RAW264.7 cells (mouse monocyte/ macrophage-like cells), which have been shown to be tumor homing cells. A murine model of disseminated peritoneal pancreatic cancer was then generated by intraperitoneal injection of Pan02 cells. After tumor development, monocyte/macrophage-like cells loaded with iron/ iron oxide nanoparticles were injected intraperitoneally and allowed to migrate into the tumor. Three days after injection, mice were exposed to an alternating magnetic field for 20 minutes to cause the cell-delivered nanoparticles to generate heat. This treatment regimen was repeated three times. A survival study demonstrated that this system can significantly increase survival in a murine pancreatic cancer model, with an average post-tumor insertion life expectancy increase of 31%. This system has the potential to become a useful method for specifically and actively delivering nanoparticles for local hyperthermia treatment of cancer.

  13. RTMOD: Real-Time MODel evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Graziani, G; Galmarini, S. [Joint Research centre, Ispra (Italy); Mikkelsen, T. [Risoe National Lab., Wind Energy and Atmospheric Physics Dept. (Denmark)

    2000-01-01

    The 1998 - 1999 RTMOD project is a system based on an automated statistical evaluation for the inter-comparison of real-time forecasts produced by long-range atmospheric dispersion models for national nuclear emergency predictions of cross-boundary consequences. The background of RTMOD was the 1994 ETEX project that involved about 50 models run in several Institutes around the world to simulate two real tracer releases involving a large part of the European territory. In the preliminary phase of ETEX, three dry runs (i.e. simulations in real-time of fictitious releases) were carried out. At that time, the World Wide Web was not available to all the exercise participants, and plume predictions were therefore submitted to JRC-Ispra by fax and regular mail for subsequent processing. The rapid development of the World Wide Web in the second half of the nineties, together with the experience gained during the ETEX exercises suggested the development of this project. RTMOD featured a web-based user-friendly interface for data submission and an interactive program module for displaying, intercomparison and analysis of the forecasts. RTMOD has focussed on model intercomparison of concentration predictions at the nodes of a regular grid with 0.5 degrees of resolution both in latitude and in longitude, the domain grid extending from 5W to 40E and 40N to 65N. Hypothetical releases were notified around the world to the 28 model forecasters via the web on a one-day warning in advance. They then accessed the RTMOD web page for detailed information on the actual release, and as soon as possible they then uploaded their predictions to the RTMOD server and could soon after start their inter-comparison analysis with other modelers. When additional forecast data arrived, already existing statistical results would be recalculated to include the influence by all available predictions. The new web-based RTMOD concept has proven useful as a practical decision-making tool for realtime

  14. In times of uncertainty: predicting the survival of long-distance relationships.

    Science.gov (United States)

    Cameron, Jessica J; Ross, Michael

    2007-12-01

    The authors examined the degree to which ratings of negative affectivity (NA) and relational security predicted the breakup of long-distance and same-city dating relationships. Couples completed initial surveys and were contacted 1 year later about the status of their relationship. In the initial surveys, both partners completed NA and relational security assessments. Overall, both the NA and relational security of men and women predicted stability. However, as predicted, structural equation modeling revealed a gender difference in the interaction between NA and long-distance status. The presence of high NA in men was associated with breakup for long-distance but not same-city couples. High NA in women was not differentially associated with relational stability on the basis of long-distance status. The authors discuss the psychological basis of this gender difference.

  15. Outlier Detection in Structural Time Series Models

    DEFF Research Database (Denmark)

    Marczak, Martyna; Proietti, Tommaso

    investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality......Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has attracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general...... and a stationary component. Further, we apply both kinds of indicator saturation to detect additive outliers and level shifts in the industrial production series in five European countries....

  16. Measuring and modeling attentional dwell time.

    Science.gov (United States)

    Petersen, Anders; Kyllingsbæk, Søren; Bundesen, Claus

    2012-12-01

    Attentional dwell time (AD) defines our inability to perceive spatially separate events when they occur in rapid succession. In the standard AD paradigm, subjects should identify two target stimuli presented briefly at different peripheral locations with a varied stimulus onset asynchrony (SOA). The AD effect is seen as a long-lasting impediment in reporting the second target, culminating at SOAs of 200-500 ms. Here, we present the first quantitative computational model of the effect--a theory of temporal visual attention. The model is based on the neural theory of visual attention (Bundesen, Habekost, & Kyllingsbæk, Psychological Review, 112, 291-328 2005) and introduces the novel assumption that a stimulus retained in visual short-term memory takes up visual processing-resources used to encode stimuli into memory. Resources are thus locked and cannot process subsequent stimuli until the stimulus in memory has been recoded, which explains the long-lasting AD effect. The model is used to explain results from two experiments providing detailed individual data from both a standard AD paradigm and an extension with varied exposure duration of the target stimuli. Finally, we discuss new predictions by the model.

  17. Time Modeling: Salvatore Sciarrino, Windows and Beclouding

    Directory of Open Access Journals (Sweden)

    Acácio Tadeu de Camargo Piedade

    2017-08-01

    Full Text Available In this article I intend to discuss one of the figures created by the Italian composer Salvatore Sciarrino: the windowed form. After the composer's explanation of this figure, I argue that windows in composition can open inwards and outwards the musical discourse. On one side, they point to the composition's inner ambiences and constitute an internal remission. On the other, they instigate the audience to comprehend the external reference, thereby constructing intertextuality. After the outward window form, I will consider some techniques of distortion, particularly one that I call beclouding. To conclude, I will comment the question of memory and of compostition as time modeling.

  18. Modelling of nonlinear filtering Poisson time series

    Science.gov (United States)

    Bochkarev, Vladimir V.; Belashova, Inna A.

    2016-08-01

    In this article, algorithms of non-linear filtering of Poisson time series are tested using statistical modelling. The objective is to find a representation of a time series as a wavelet series with a small number of non-linear coefficients, which allows distinguishing statistically significant details. There are well-known efficient algorithms of non-linear wavelet filtering for the case when the values of a time series have a normal distribution. However, if the distribution is not normal, good results can be expected using the maximum likelihood estimations. The filtration is studied according to the criterion of maximum likelihood by the example of Poisson time series. For direct optimisation of the likelihood function, different stochastic (genetic algorithms, annealing method) and deterministic optimization algorithms are used. Testing of the algorithm using both simulated series and empirical data (series of rare words frequencies according to the Google Books Ngram data were used) showed that filtering based on the criterion of maximum likelihood has a great advantage over well-known algorithms for the case of Poisson series. Also, the most perspective methods of optimisation were selected for this problem.

  19. Users' guide to system dynamics model describing Coho salmon survival in Olema Creek, Point Reyes National Seashore, Marin County, California

    Science.gov (United States)

    Woodward, Andrea; Torregrosa, Alicia; Madej, Mary Ann; Reichmuth, Michael; Fong, Darren

    2014-01-01

    The system dynamics model described in this report is the result of a collaboration between U.S. Geological Survey (USGS) scientists and National Park Service (NPS) San Francisco Bay Area Network (SFAN) staff, whose goal was to develop a methodology to integrate inventory and monitoring data to better understand ecosystem dynamics and trends using salmon in Olema Creek, Marin County, California, as an example case. The SFAN began monitoring multiple life stages of coho salmon (Oncorhynchus kisutch) in Olema Creek during 2003 (Carlisle and others, 2013), building on previous monitoring of spawning fish and redds. They initiated water-quality and habitat monitoring, and had access to flow and weather data from other sources. This system dynamics model of the freshwater portion of the coho salmon life cycle in Olema Creek integrated 8 years of existing monitoring data, literature values, and expert opinion to investigate potential factors limiting survival and production, identify data gaps, and improve monitoring and restoration prescriptions. A system dynamics model is particularly effective when (1) data are insufficient in time series length and/or measured parameters for a statistical or mechanistic model, and (2) the model must be easily accessible by users who are not modelers. These characteristics helped us meet the following overarching goals for this model: Summarize and synthesize NPS monitoring data with data and information from other sources to describe factors and processes affecting freshwater survival of coho salmon in Olema Creek. Provide a model that can be easily manipulated to experiment with alternative values of model parameters and novel scenarios of environmental drivers. Although the model describes the ecological dynamics of Olema Creek, these dynamics are structurally similar to numerous other coastal streams along the California coast that also contain anadromous fish populations. The model developed for Olema can be used, at least as a

  20. Plasma Resuscitation Improved Survival in a Cecal Ligation and Puncture Rat Model of Sepsis.

    Science.gov (United States)

    Chang, Ronald; Holcomb, John B; Johansson, Par I; Pati, Shibani; Schreiber, Martin A; Wade, Charles E

    2017-06-06

    The paradigm shift from crystalloid to plasma resuscitation of traumatic hemorrhagic shock has improved patient outcomes due in part to plasma-mediated reversal of catecholamine and inflammation-induced endothelial injury, decreasing vascular permeability and attenuating organ injury. Since sepsis induces a similar endothelial injury as seen in hemorrhage, we hypothesized that plasma resuscitation would increase 48-hour survival in a rat sepsis model. Adult male Sprague-Dawley rats (375-425 g) were subjected to 35% cecal ligation and puncture (CLP) (t = 0 h). Twenty-two hours post-CLP and prior to resuscitation (t = 22 h), animals were randomized to resuscitation with normal saline (NS, 10 cc/kg/hr) or pooled rat fresh frozen plasma (FFP, 3.33 cc/kg/hr). Resuscitation under general anesthesia proceeded for the next six hours (t = 22 h to t = 28 h); lactate was checked every 2 hours, and fluid volumes were titrated based on lactate clearance. Blood samples were obtained before (t = 22 h) and after resuscitation (t = 28 h), and at death or study conclusion. Lung specimens were obtained for calculation of wet-to-dry weight ratio. Fisher's exact test was used to analyze the primary outcome of 48-hour survival. ANOVA with repeated measures was used to analyze the effect of FFP versus NS resuscitation on blood gas, electrolytes, blood urea nitrogen (BUN), creatinine, interleukin (IL)-6, IL-10, catecholamines, and syndecan-1 (marker for endothelial injury). A two-tailed alpha level of dry weight ratio (5.28 vs 5.94) (all p < 0.05). Compared to crystalloid, plasma resuscitation increased 48-hour survival in a rat sepsis model, improved pulmonary function and decreased pulmonary edema, and attenuated markers for inflammation, endothelial injury, and catecholamines.

  1. Extension of cox proportional hazard model for estimation of interrelated age-period-cohort effects on cancer survival.

    Science.gov (United States)

    Mdzinarishvili, Tengiz; Gleason, Michael X; Kinarsky, Leo; Sherman, Simon

    2011-02-23

    In the frame of the Cox proportional hazard (PH) model, a novel two-step procedure for estimating age-period-cohort (APC) effects on the hazard function of death from cancer was developed. In the first step, the procedure estimates the influence of joint APC effects on the hazard function, using Cox PH regression procedures from a standard software package. In the second step, the coefficients for age at diagnosis, time period and birth cohort effects are estimated. To solve the identifiability problem that arises in estimating these coefficients, an assumption that neighboring birth cohorts almost equally affect the hazard function was utilized. Using an anchoring technique, simple procedures for obtaining estimates of interrelated age at diagnosis, time period and birth cohort effect coefficients were developed.As a proof-of-concept these procedures were used to analyze survival data, collected in the SEER database, on white men and women diagnosed with LC in 1975-1999 and the age at diagnosis, time period and birth cohort effect coefficients were estimated. The PH assumption was evaluated by a graphical approach using log-log plots. Analysis of trends of these coefficients suggests that the hazard of death from LC for a given time from cancer diagnosis: (i) decreases between 1975 and 1999; (ii) increases with increasing the age at diagnosis; and (iii) depends upon birth cohort effects.The proposed computing procedure can be used for estimating joint APC effects, as well as interrelated age at diagnosis, time period and birth cohort effects in survival analysis of different types of cancer.

  2. 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.

  3. Survival analysis of timing of first marriage among women of reproductive age in Nigeria: regional differences.

    Science.gov (United States)

    Adebowale, Stephen A; Fagbamigbe, Francis A; Okareh, Titus O; Lawal, Ganiyu O

    2012-12-01

    Early marriage is common among women in developing countries. Age at first marriage (AFM) has health implication on women and their under-five children. In Nigeria, few studies have explored AFM; the current study was designed to fill the gap. Nigeria Demographic and Health Survey, 2008 dataset on married women aged 15-49 (N = 24,986) was used. Chi-square, OLS regression and Cox proportional hazard models were used in the analysis. The mean AFM was 17.8 +/- 4.8 years and significant difference existed between the mean AFM of women in the North (16.0 +/- 3.6) and South (20.4 +/- 5.0) (p marriage (p marriage was more common in all the regions in the North than the South and the hazard was highest in the North West and North East. Women who reside in rural area (H.R = 1.15; C.I = 1.11-1.18) married early than their counterparts in the urban area. Age at first marriage was directly related to levels of education (p marriage more common in the North than the South. Education has influence on AFM; therefore, women should have at least secondary education before marriage in Nigeria.

  4. ENTREPRENEURSHIP IN TIMES OF ECONOMIC AND FINANCIAL CRISES, METHODS OF ADAPTATION AND SURVIVAL.

    Directory of Open Access Journals (Sweden)

    Radu Oprea

    2011-12-01

    Full Text Available This paper shows the main results of a research activity conducted in the South – East region of Romania during November 2009, over 117 small and medium-sized enterprises. The results help to better explain some of the social problems that have been generated in the region as a snow ball effect, due to the economic and financial crisis. The research has revealed the following:The majority of the employees of the companies that were interviewed do not participate in training courses related to project management and personal development. However, company administrators consider that such courses are useful to increase competitiveness in times of economic and financial crises. Personnel under 40 years of age suffer from a lack of professional training and dedication to job requirements. Companies have expressed an interest in accessing European Union funding. However, they do not have complete information that would allow them to start a project and finalize it. This lack of information is mainly due to lack of access to specialists who would write the projects and submit them. The majority of entrepreneurs consider they cannot write the projects themselves but on the other hand they do not have access to consulting companies or other organizations that could help them. Their main information source is the mass-media.As a result, companies have adjusted to the effects of the economic crisis, diminishing their activity and laying off people, decreasing the running expenses and stopping investing in their development.

  5. Resin Versus Glass Microspheres for90Y Transarterial Radioembolization: Comparing Survival in Unresectable Hepatocellular Carcinoma Using Pretreatment Partition Model Dosimetry.

    Science.gov (United States)

    Van Der Gucht, Axel; Jreige, Mario; Denys, Alban; Blanc-Durand, Paul; Boubaker, Ariane; Pomoni, Anastasia; Mitsakis, Periklis; Silva-Monteiro, Marina; Gnesin, Silvano; Lalonde, Marie Nicod; Duran, Rafael; Prior, John O; Schaefer, Niklaus

    2017-08-01

    The aim of this study was to compare survival of patients treated for unresectable hepatocellular carcinoma (uHCC) with 90 Y transarterial radioembolization (TARE) using pretreatment partition model dosimetry (PMD). Methods: We performed a retrospective analysis of prospectively collected data on 77 patients consecutively treated (mean age ± SD, 66.4 ± 12.2 y) for uHCC (36 uninodular, 5 multinodular, 36 diffuse) with 90 Y TARE (41 resin, 36 glass) using pretreatment PMD. Study endpoints were progression-free survival (PFS) and overall survival (OS) assessed by Kaplan-Meier estimates. Several variables including Barcelona Clinic Liver Cancer (BCLC) staging system, tumor size, and serum α-fetoprotein (AFP) level were investigated using Cox proportional hazards regression. Results: The characteristics of 2 groups were comparable with regard to demographic data, comorbidities, Child-Pugh score, BCLC, serum AFP level, and 90 Y global administered activity. The median follow-up time was 7.7 mo (range, 0.4-50.1 mo). Relapse occurred in 44 patients (57%) at a median of 6 mo (range, 0.4-27.9 mo) after 90 Y TARE, and 41 patients (53%) died from tumor progression. Comparison between resin and glass microspheres revealed higher but not statistically significantly PFS and OS rates in the 90 Y resin group than the 90 Y glass group (resin PFS 6.1 mo [95% confidence interval CI, 4.7-7.4] and glass PFS 5 mo [95% CI, 0.9-9.2], P = 0.53; resin OS 7.7 mo [95% CI, 7.2-8.2] and glass OS 7 mo [95% CI 1.6-12.4], P = 0.77). No significant survival difference between both types of 90 Y microspheres was observed in any subgroups of patients with early/intermediate or advanced BCLC stages. Among the variables investigated, Cox analyses showed that only in the glass group, the BCLC staging system and the serum AFP level were associated with PFS ( P = 0.04) and OS ( P = 0.04). Tumor size was a prognostic factor without significant influence on PFS and OS after 90 Y TARE. Conclusion

  6. Effect of Migration Pathway on Travel Time and Survival of Acoustic-Tagged Juvenile Salmonids in the Columbia River Estuary

    Energy Technology Data Exchange (ETDEWEB)

    Harnish, Ryan A.; Johnson, Gary E.; McMichael, Geoffrey A.; Hughes, Michael S.; Ebberts, Blaine D.

    2012-02-01

    Off-channel areas (side channels, tidal flats, sand bars, and shallow-water bays) may serve as important migration corridors through estuarine environments for salmon and steelhead smolts. Relatively large percentages (21-33%) of acoustic-tagged yearling and subyearling Chinook salmon and steelhead smolts were detected migrating through off-channel areas of the Columbia River estuary in 2008. The probability of survival for off-channel migrants (0.78-0.94) was similar to or greater than the survival probability of main channel migrants (0.67-0.93). Median travel times were similar for all species or run types and migration pathways we examined, ranging from 1-2 d. The route used by smolts to migrate through the estuary may affect their vulnerability to predation. Acoustic-tagged steelhead that migrated nearest to avian predator nesting colonies experienced higher predation rates (24%) than those that migrated farthest from the colonies (10%). The use of multiple migration pathways may be advantageous to out-migrating smolts because it helps to buffer against high rates of mortality, which may occur in localized areas, and helps to minimize inter- and intraspecific competition.

  7. NanOx, a new model to predict cell survival in the context of particle therapy

    Science.gov (United States)

    Cunha, M.; Monini, C.; Testa, E.; Beuve, M.

    2017-02-01

    Particle therapy is increasingly attractive for the treatment of tumors and the number of facilities offering it is rising worldwide. Due to the well-known enhanced effectiveness of ions, it is of utmost importance to plan treatments with great care to ensure tumor killing and healthy tissues sparing. Hence, the accurate quantification of the relative biological effectiveness (RBE) of ions, used in the calculation of the biological dose, is critical. Nevertheless, the RBE is a complex function of many parameters and its determination requires modeling. The approaches currently used have allowed particle therapy to thrive, but still show some shortcomings. We present herein a short description of a new theoretical framework, NanOx, to calculate cell survival in the context of particle therapy. It gathers principles from existing approaches, while addressing some of their weaknesses. NanOx is a multiscale model that takes the stochastic nature of radiation at nanometric and micrometric scales fully into account, integrating also the chemical aspects of radiation-matter interaction. The latter are included in the model by means of a chemical specific energy, determined from the production of reactive chemical species induced by irradiation. Such a production represents the accumulation of oxidative stress and sublethal damage in the cell, potentially generating non-local lethal events in NanOx. The complementary local lethal events occur in a very localized region and can, alone, lead to cell death. Both these classes of events contribute to cell death. The comparison between experimental data and model predictions for the V79 cell line show a good agreement. In particular, the dependence of the typical shoulders of cell survival curves on linear energy transfer are well described, but also the effectiveness of different ions, including the overkill effect. These results required the adjustment of a number of parameters compatible with the application of the model in

  8. Design and Analysis of Salmonid Tagging Studies in the Columbia Basin : Evaluating Wetland Restoration Projects in the Columbia River Estuary using Hydroacoustic Telemetry Arrays to Estimate Movement, Survival, and Residence Times of Juvenile Salmonids, Volume XXII (22).

    Energy Technology Data Exchange (ETDEWEB)

    Perry, Russell W.; Skalski, John R.

    2008-08-01

    Wetlands in the Columbia River estuary are actively being restored by reconnecting these habitats to the estuary, making more wetland habitats available to rearing and migrating juvenile salmon. Concurrently, thousands of acoustically tagged juvenile salmonids are released into the Columbia River to estimate their survival as they migrate through the estuary. Here, we develop a release-recapture model that makes use of these tagged fish to measure the success of wetland restoration projects in terms of their contribution to populations of juvenile salmon. Specifically, our model estimates the fraction of the population that enter the wetland, survival within the wetland, and the mean residence time of fish within the wetland. Furthermore, survival in mainstem Columbia River downstream of the wetland can be compared between fish that remained the mainstem and entered the wetland. These conditional survival estimates provide a means of testing whether the wetland improves the subsequent survival of juvenile salmon by fostering growth or improving their condition. Implementing such a study requires little additional cost because it takes advantage of fish already released to estimate survival through the estuary. Thus, such a study extracts the maximum information at minimum cost from research projects that typically cost millions of dollars annually.

  9. Survival prediction based on compound covariate under Cox proportional hazard models.

    Directory of Open Access Journals (Sweden)

    Takeshi Emura

    Full Text Available Survival prediction from a large number of covariates is a current focus of statistical and medical research. In this paper, we study a methodology known as the compound covariate prediction performed under univariate Cox proportional hazard models. We demonstrate via simulations and real data analysis that the compound covariate method generally competes well with ridge regression and Lasso methods, both already well-studied methods for predicting survival outcomes with a large number of covariates. Furthermore, we develop a refinement of the compound covariate method by incorporating likelihood information from multivariate Cox models. The new proposal is an adaptive method that borrows information contained in both the univariate and multivariate Cox regression estimators. We show that the new proposal has a theoretical justification from a statistical large sample theory and is naturally interpreted as a shrinkage-type estimator, a popular class of estimators in statistical literature. Two datasets, the primary biliary cirrhosis of the liver data and the non-small-cell lung cancer data, are used for illustration. The proposed method is implemented in R package "compound.Cox" available in CRAN at http://cran.r-project.org/.

  10. The Antarctic nematode Plectus murrayi: an emerging model to study multiple stress survival.

    Science.gov (United States)

    Adhikari, Bishwo N; Tomasel, Cecilia M; Li, Grace; Wall, Diana H; Adams, Byron J

    2010-11-01

    The genus Plectus is one of the most widely distributed and common nematode taxa of freshwater and terrestrial habitats in the world, and is of particular interest because of its phylogenetic position relative to the origin of the Secernentean radiation. Plectus murrayi, a bacteria-feeding nematode, inhabits both semi-aquatic and terrestrial biotopes in the Antarctic McMurdo Dry Valleys (MCM), where its distribution is limited by organic carbon and soil moisture. Plectus nematodes from the MCM can survive extreme desiccation, freezing conditions, and other types of stress. Ongoing investigations of the physiological and molecular aspects of the stress biology of P. murrayi, along with the availability of genomic resources, will likely establish this nematode as an excellent invertebrate model system for studies of extreme environmental survival, and may provide a valuable source of genomic resources for comparative studies in other organisms. Moreover, because P. murrayi and Caenorhabditis elegans share a most recent common ancestor with the rest of the Secernentea, and given the ability of P. murrayi to be cultured at lower temperatures compared to C. elegans, P. murrayi could also be an emerging model system for the study of the evolution of environment-sensitive (stress response) alleles in nematodes.

  11. Mathematical modelling of survival of glioblastoma patients suggests a role for radiotherapy dose escalation and predicts poorer outcome after delay to start treatment.

    Science.gov (United States)

    Burnet, N G; Jena, R; Jefferies, S J; Stenning, S P; Kirkby, N F

    2006-03-01

    The outcome of patients with glioblastoma (GBM) remains extremely poor. We have developed a mathematical model, using pathological and radiation biology concepts, to assess the detrimental effect of delay to start radiotherapy, the possible benefit from dose escalation, and to extract biological data from clinical data. Survival data were available for 154 adult patients with GBM treated in our centre with curative intent to a dose of 60 Gy in 30 fractions between 1996 and 2002. Survival data for 129 patients from the 60 Gy arm of the MRC BR02 randomised trial of radiotherapy dose were obtained for comparison. The model generates the equivalent of individual patients with a brain tumour, and produces an explicit outcome, either death or survival. The tumour, assumed to be growing exponentially, causes normal cell damage in the brain, and death occurs when the number of normal brain cells falls below a critical level. The outcome for an individual patient is determined by values of the variables assigned by the model. Parameters for the single patient include tumour doubling time, surviving fraction of tumour cells after each fraction of radiotherapy, and a waiting time from presentation to the start of radiotherapy. A surrogate for performance status is implemented, using a rule that rejects patients whose tumours are too advanced at presentation to be suitable for radical radiotherapy. Values for the parameters that determine individual patient outcome are randomly assigned from a set of probability distributions, using Monte Carlo simulation. The simulation constructs survival results for a population, typically 2000 individuals. The descriptors of the probability distributions that are used to determine the parameters that define the patient characteristics are adjusted to optimise the fit of the modelled population to real clinical data, using a combination of folding polygon and simulated annealing techniques. The model fits the clinical data well. The results

  12. Fasudil improves survival and promotes skeletal muscle development in a mouse model of spinal muscular atrophy

    Directory of Open Access Journals (Sweden)

    Bowerman Melissa

    2012-03-01

    Full Text Available Abstract Background Spinal muscular atrophy (SMA is the leading genetic cause of infant death. It is caused by mutations/deletions of the survival motor neuron 1 (SMN1 gene and is typified by the loss of spinal cord motor neurons, muscular atrophy, and in severe cases, death. The SMN protein is ubiquitously expressed and various cellular- and tissue-specific functions have been investigated to explain the specific motor neuron loss in SMA. We have previously shown that the RhoA/Rho kinase (ROCK pathway is misregulated in cellular and animal SMA models, and that inhibition of ROCK with the chemical Y-27632 significantly increased the lifespan of a mouse model of SMA. In the present study, we evaluated the therapeutic potential of the clinically approved ROCK inhibitor fasudil. Methods Fasudil was administered by oral gavage from post-natal day 3 to 21 at a concentration of 30 mg/kg twice daily. The effects of fasudil on lifespan and SMA pathological hallmarks of the SMA mice were assessed and compared to vehicle-treated mice. For the Kaplan-Meier survival analysis, the log-rank test was used and survival curves were considered significantly different at P t test for paired variables and one-way analysis of variance (ANOVA were used to test for differences between samples and data were considered significantly different at P Results Fasudil significantly improves survival of SMA mice. This dramatic phenotypic improvement is not mediated by an up-regulation of Smn protein or via preservation of motor neurons. However, fasudil administration results in a significant increase in muscle fiber and postsynaptic endplate size, and restores normal expression of markers of skeletal muscle development, suggesting that the beneficial effects of fasudil could be muscle-specific. Conclusions Our work underscores the importance of muscle as a therapeutic target in SMA and highlights the beneficial potential of ROCK inhibitors as a therapeutic strategy for SMA

  13. A prognostic model of therapy-related myelodysplastic syndrome for predicting survival and transformation to acute myeloid leukemia.

    Science.gov (United States)

    Quintás-Cardama, Alfonso; Daver, Naval; Kim, Hawk; Dinardo, Courtney; Jabbour, Elias; Kadia, Tapan; Borthakur, Gautam; Pierce, Sherry; Shan, Jianqin; Cardenas-Turanzas, Marylou; Cortes, Jorge; Ravandi, Farhad; Wierda, William; Estrov, Zeev; Faderl, Stefan; Wei, Yue; Kantarjian, Hagop; Garcia-Manero, Guillermo

    2014-10-01

    We evaluated the characteristics of a cohort of patients with myelodysplastic syndrome (MDS) related to therapy (t-MDS) to create a prognostic model. We identified 281 patients with MDS who had received previous chemotherapy and/or radiotherapy for previous malignancy. Potential prognostic factors were determined using univariate and multivariate analyses. Multivariate Cox regression analysis identified 7 factors that independently predicted short survival in t-MDS: age ≥ 65 years (hazard ratio [HR], 1.63), Eastern Cooperative Oncology Group performance status 2-4 (HR, 1.86), poor cytogenetics (-7 and/or complex; HR, 2.47), World Health Organization MDS subtype (RARs or RAEB-1/2; HR, 1.92), hemoglobin (HR, 2.24), platelets (HR, 2.01), and transfusion dependency (HR, 1.59). These risk factors were used to create a prognostic model that segregated patients into 3 groups with distinct median overall survival: good (0-2 risk factors; 34 months), intermediate (3-4 risk factors; 12 months), and poor (5-7 risk factors; 5 months) (P < .001) and 1-year leukemia-free survival (96%, 84%, and 72%, respectively, P = .003). This model also identified distinct survival groups according to t-MDS therapy. In summary, we devised a prognostic model specifically for patients with t-MDS that predicted overall survival and leukemia-free survival. This model might facilitate the development of risk-adapted therapeutic strategies. Copyright © 2014 Elsevier Inc. All rights reserved.

  14. The Design and Analysis of Salmonid Tagging Studies in the Columbia Basin : Volume XVII : Effects of Ocean Covariates and Release Timing on First Ocean-Year Survival of Fall Chinook Salmon from Oregon and Washington Coastal Hatcheries.

    Energy Technology Data Exchange (ETDEWEB)

    Burgess, Caitlin; Skalski, John R.

    2001-05-01

    Effects of oceanographic conditions, as well as effects of release-timing and release-size, on first ocean-year survival of subyearling fall chinook salmon were investigated by analyzing CWT release and recovery data from Oregon and Washington coastal hatcheries. Age-class strength was estimated using a multinomial probability likelihood which estimated first-year survival as a proportional hazards regression against ocean and release covariates. Weight-at-release and release-month were found to significantly effect first year survival (p < 0.05) and ocean effects were therefore estimated after adjusting for weight-at-release. Negative survival trend was modeled for sea surface temperature (SST) during 11 months of the year over the study period (1970-1992). Statistically significant negative survival trends (p < 0.05) were found for SST during April, June, November and December. Strong pairwise correlations (r > 0.6) between SST in April/June, April/November and April/December suggest the significant relationships were due to one underlying process. At higher latitudes (45{sup o} and 48{sup o}N), summer upwelling (June-August) showed positive survival trend with survival and fall (September-November) downwelling showed positive trend with survival, indicating early fall transition improved survival. At 45{sup o} and 48{sup o}, during spring, alternating survival trends with upwelling were observed between March and May, with negative trend occurring in March and May, and positive trend with survival occurring in April. In January, two distinct scenarios of improved survival were linked to upwelling conditions, indicated by (1) a significant linear model effect (p < 0.05) showing improved survival with increasing upwelling, and (2) significant bowl-shaped curvature (p < 0.05) of survival with upwelling. The interpretation of the effects is that there was (1) significantly improved survival when downwelling conditions shifted to upwelling conditions in January (i

  15. Review and evaluation of performance measures for survival prediction models in external validation settings.

    Science.gov (United States)

    Rahman, M Shafiqur; Ambler, Gareth; Choodari-Oskooei, Babak; Omar, Rumana Z

    2017-04-18

    When developing a prediction model for survival data it is essential to validate its performance in external validation settings using appropriate performance measures. Although a number of such measures have been proposed, there is only limited guidance regarding their use in the context of model validation. This paper reviewed and evaluated a wide range of performance measures to provide some guidelines for their use in practice. An extensive simulation study based on two clinical datasets was conducted to investigate the performance of the measures in external validation settings. Measures were selected from categories that assess the overall performance, discrimination and calibration of a survival prediction model. Some of these have been modified to allow their use with validation data, and a case study is provided to describe how these measures can be estimated in practice. The measures were evaluated with respect to their robustness to censoring and ease of interpretation. All measures are implemented, or are straightforward to implement, in statistical software. Most of the performance measures were reasonably robust to moderate levels of censoring. One exception was Harrell's concordance measure which tended to increase as censoring increased. We recommend that Uno's concordance measure is used to quantify concordance when there are moderate levels of censoring. Alternatively, Gönen and Heller's measure could be considered, especially if censoring is very high, but we suggest that the prediction model is re-calibrated first. We also recommend that Royston's D is routinely reported to assess discrimination since it has an appealing interpretation. The calibration slope is useful for both internal and external validation settings and recommended to report routinely. Our recommendation would be to use any of the predictive accuracy measures and provide the corresponding predictive accuracy curves. In addition, we recommend to investigate the characteristics

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

    Directory of Open Access Journals (Sweden)

    Kabluchko TV

    2017-03-01

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

  17. 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

    In this paper, we consider selection based on the best predictor of animal additive genetic values in Gaussian linear mixed models, threshold models, Poisson mixed models, and log normal frailty models for survival data (including models with time-dependent covariates with associated fixed or ran...... of the model (heritability on the normally distributed level of the model) or a generalised version of heritability plays a central role in these formulas...

  18. A dynamic prognostic model to predict survival in primary myelofibrosis: a study by the IWG-MRT (International Working Group for Myeloproliferative Neoplasms Research and Treatment).

    Science.gov (United States)

    Passamonti, Francesco; Cervantes, Francisco; Vannucchi, Alessandro Maria; Morra, Enrica; Rumi, Elisa; Pereira, Arturo; Guglielmelli, Paola; Pungolino, Ester; Caramella, Marianna; Maffioli, Margherita; Pascutto, Cristiana; Lazzarino, Mario; Cazzola, Mario; Tefferi, Ayalew

    2010-03-04

    Age older than 65 years, hemoglobin level lower than 100 g/L (10 g/dL), white blood cell count greater than 25 x 10(9)/L, peripheral blood blasts 1% or higher, and constitutional symptoms have been shown to predict poor survival in primary myelofibrosis (PMF) at diagnosis. To investigate whether the acquisition of these factors during follow-up predicts survival, we studied 525 PMF patients regularly followed. All 5 variables had a significant impact on survival when analyzed as time-dependent covariates in a multivariate Cox proportional hazard model and were included in 2 separate models, 1 for all patients (Dynamic International Prognostic Scoring System [DIPSS]) and 1 for patients younger than 65 years (age-adjusted DIPSS). Risk factors were assigned score values based on hazard ratios (HRs). Risk categories were low, intermediate-1, intermediate-2, and high in both models. Survival was estimated by the HR. When shifting to the next risk category, the HR was 4.13 for low risk, 4.61 for intermediate-1, and 2.54 for intermediate-2 according to DIPSS; 3.97 for low risk, 2.84 for intermediate-1, and 1.81 for intermediate-2 according to the age-adjusted DIPSS. The novelty of these models is the prognostic assessment of patients with PMF anytime during their clinical course, which may be useful for treatment decision-making.

  19. 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