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  1. 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

  2. Survival analysis

    International Nuclear Information System (INIS)

    Badwe, R.A.

    1999-01-01

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

  3. Modelling survival

    DEFF Research Database (Denmark)

    Ashauer, Roman; Albert, Carlo; Augustine, Starrlight

    2016-01-01

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

  4. 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

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

    DEFF Research Database (Denmark)

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

    2014-01-01

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

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

    DEFF Research Database (Denmark)

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

    2013-01-01

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

  7. Survival Analysis

    CERN Document Server

    Miller, Rupert G

    2011-01-01

    A concise summary of the statistical methods used in the analysis of survival data with censoring. Emphasizes recently developed nonparametric techniques. Outlines methods in detail and illustrates them with actual data. Discusses the theory behind each method. Includes numerous worked problems and numerical exercises.

  8. 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.

  9. 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.

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

    International Nuclear Information System (INIS)

    Lachet, Bernard; Dufour, Jacques

    1976-01-01

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

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

    Science.gov (United States)

    Linden, Ariel; Yarnold, Paul R

    2017-12-01

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

  12. Survival Analysis of a Nonautonomous Logistic Model with Stochastic Perturbation

    Directory of Open Access Journals (Sweden)

    Chun Lu

    2012-01-01

    Full Text Available Taking white noise into account, a stochastic nonautonomous logistic model is proposed and investigated. Sufficient conditions for extinction, nonpersistence in the mean, weak persistence, stochastic permanence, and global asymptotic stability are established. Moreover, the threshold between weak persistence and extinction is obtained. Finally, we introduce some numerical simulink graphics to illustrate our main results.

  13. 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...

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

    International Nuclear Information System (INIS)

    Unkel, Steffen; Belka, Claus; Lauber, Kirsten

    2016-01-01

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

  15. 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.

  16. Radiobilogical cell survival models

    International Nuclear Information System (INIS)

    Zackrisson, B.

    1992-01-01

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

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

    Science.gov (United States)

    Hasyim, M.; Prastyo, D. D.

    2018-03-01

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

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

    Science.gov (United States)

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

    2017-05-01

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

  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. Regression modeling strategies with applications to linear models, logistic and ordinal regression, and survival analysis

    CERN Document Server

    Harrell , Jr , Frank E

    2015-01-01

    This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap.  The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes.  This text realistically...

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

    Science.gov (United States)

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

    2016-01-01

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

  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. 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...

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

    Science.gov (United States)

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

    2017-10-12

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

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

    Science.gov (United States)

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

    2016-01-01

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

  6. 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.

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

    Directory of Open Access Journals (Sweden)

    Erhan Bilal

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

  8. Novel bifunctional anthracycline and nitrosourea chemotherapy for human bladder cancer: analysis in a preclinical survival model.

    Science.gov (United States)

    Glaves, D; Murray, M K; Raghavan, D

    1996-08-01

    A hybrid drug [N-2-chloroethylnitrosoureidodaunorubicin (AD312)] that combines structural and functional features of both anthracyclines and nitrosoureas was evaluated in a preclinical survival model of human bladder cancer. To measure the therapeutic activity of AD312, UCRU-BL13 transitional cell carcinoma cells were grown as xenografts in nude mice, and tumor growth rates were compared after i.v. administration of the drug at three dose levels. AD312 treatment at 45 and 60 mg/kg achieved 7-10-fold inhibition of tumor growth and increased host survival by 156 and 249%, respectively. Doses of 60 mg/kg showed optimal therapeutic efficacy, with sustained tumor growth inhibition, an over 2-fold increase in life span, and 40% of mice tumor free ("cured") at 120 days. Tumors were unresponsive to maximum tolerated doses of doxorubicin, a standard anthracycline used as a single agent and in combination therapies for bladder cancer. 1,3-Bis-[2-chloroethyl]-1-nitrosourea was used as a control for the apparently enhanced response of human tumors in murine hosts to nitrosoureas. 1, 3-Bis-[2-chloroethyl]-1-nitrosourea administered in three injections of 20 mg/kg did not cure mice but temporarily inhibited tumor growth by 70% and prolonged survival by 55%; its activity in this model suggests that it may be included in the repertoire of alkylating agents currently used for treatment of bladder cancers. AD312 showed increased antitumor activity with less toxicity than doxorubicin, and its bifunctional properties provide the opportunity for simultaneous treatment of individual cancer cells with two cytotoxic modalities as well as treatment of heterogeneous populations typical of bladder cancers. This novel cytotoxic drug cured doxorubicin-refractory disease and should be investigated for the clinical management of bladder cancer.

  9. 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

  10. Multivariate survival analysis and competing risks

    CERN Document Server

    Crowder, Martin J

    2012-01-01

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

  11. Probabilistic Survivability Versus Time Modeling

    Science.gov (United States)

    Joyner, James J., Sr.

    2016-01-01

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

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

    DEFF Research Database (Denmark)

    Scheike, Thomas H.; Zhang, Mei-Jie

    2002-01-01

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

  13. 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.

  14. Neyman, Markov processes and survival analysis.

    Science.gov (United States)

    Yang, Grace

    2013-07-01

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

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

    Science.gov (United States)

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

    2016-11-30

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

  16. A taylor series approach to survival analysis

    International Nuclear Information System (INIS)

    Brodsky, J.B.; Groer, P.G.

    1984-09-01

    A method of survival analysis using hazard functions is developed. The method uses the well known mathematical theory for Taylor Series. Hypothesis tests of the adequacy of many statistical models, including proportional hazards and linear and/or quadratic dose responses, are obtained. A partial analysis of leukemia mortality in the Life Span Study cohort is used as an example. Furthermore, a relatively robust estimation procedure for the proportional hazards model is proposed. (author)

  17. Biostatistics series module 9: Survival analysis

    Directory of Open Access Journals (Sweden)

    Avijit Hazra

    2017-01-01

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

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

    Science.gov (United States)

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

    2016-03-01

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

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

    International Nuclear Information System (INIS)

    Shen Xun; Hu Yiwei

    1992-01-01

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

  20. Additive interaction in survival analysis

    DEFF Research Database (Denmark)

    Rod, Naja Hulvej; Lange, Theis; Andersen, Ingelise

    2012-01-01

    It is a widely held belief in public health and clinical decision-making that interventions or preventive strategies should be aimed at patients or population subgroups where most cases could potentially be prevented. To identify such subgroups, deviation from additivity of absolute effects...... an empirical example of interaction between education and smoking on risk of lung cancer. We argue that deviations from additivity of effects are important for public health interventions and clinical decision-making, and such estimations should be encouraged in prospective studies on health. A detailed...... is the relevant measure of interest. Multiplicative survival models, such as the Cox proportional hazards model, are often used to estimate the association between exposure and risk of disease in prospective studies. In Cox models, deviations from additivity have usually been assessed by surrogate measures...

  1. Discrete survival model analysis of a couple’s smoking pattern and outcomes of assisted reproduction

    Directory of Open Access Journals (Sweden)

    Jose C. Vanegas

    2017-02-01

    Full Text Available Abstract Background Cigarette smoking has been associated with worse infertility treatment outcomes, yet some studies have found null or inconsistent results. Methods We followed 225 couples who underwent 354 fresh non-donor assisted reproductive technology (ART cycles between 2006 and 2014. Smoking history was self-reported at study entry. We evaluated the associations between smoking patterns and ART success using multivariable discrete time Cox proportional hazards models with six time periods: cycle initiation to egg retrieval, retrieval to fertilization, fertilization to embryo transfer (ET, ET to implantation, implantation to clinical pregnancy, and clinical pregnancy to live birth to estimate hazard ratios (HR and 95% CIs. Time-dependent interactions between smoking intensity and ART time period were used to identify vulnerable periods. Results Overall, 26% of women and 32% of men reported ever smoking. The HR of failing in the ART cycle without attaining live birth for male and female ever smokers was elevated, but non-significant, compared to never smokers regardless of intensity (HR = 1.02 and 1.30, respectively. Female ever smokers were more likely to fail prior to oocyte retrieval (HR: 3.37; 95% CI: 1.00, 12.73. Every one cigarette/day increase in smoking intensity for females was associated with a HR of 1.02 of failing ART (95% CI: 0.97, 1.08, regardless of duration or current smoking status. Women with higher smoking intensities were most likely to fail a cycle prior to oocyte retrieval (HR: 1.07; 95% CI: 1.00, 1.16. Among past smokers, every additional year since a man had quit smoking reduced the risk of failing ART by 4% (HR: 0.96; 95% CI: 0.91, 1.00 particularly between clinical pregnancy and live birth (HR: 0.86; 95% CI: 0.76, 0.96. Conclusions Female smoking intensity, regardless of current smoking status, is positively associated with the risk of failing ART cycles between initiation and oocyte retrieval. In men who ever

  2. Survival analysis of clinical mastitis data using a nested frailty Cox model fit as a mixed-effects Poisson model.

    Science.gov (United States)

    Elghafghuf, Adel; Dufour, Simon; Reyher, Kristen; Dohoo, Ian; Stryhn, Henrik

    2014-12-01

    Mastitis is a complex disease affecting dairy cows and is considered to be the most costly disease of dairy herds. The hazard of mastitis is a function of many factors, both managerial and environmental, making its control a difficult issue to milk producers. Observational studies of clinical mastitis (CM) often generate datasets with a number of characteristics which influence the analysis of those data: the outcome of interest may be the time to occurrence of a case of mastitis, predictors may change over time (time-dependent predictors), the effects of factors may change over time (time-dependent effects), there are usually multiple hierarchical levels, and datasets may be very large. Analysis of such data often requires expansion of the data into the counting-process format - leading to larger datasets - thus complicating the analysis and requiring excessive computing time. In this study, a nested frailty Cox model with time-dependent predictors and effects was applied to Canadian Bovine Mastitis Research Network data in which 10,831 lactations of 8035 cows from 69 herds were followed through lactation until the first occurrence of CM. The model was fit to the data as a Poisson model with nested normally distributed random effects at the cow and herd levels. Risk factors associated with the hazard of CM during the lactation were identified, such as parity, calving season, herd somatic cell score, pasture access, fore-stripping, and proportion of treated cases of CM in a herd. The analysis showed that most of the predictors had a strong effect early in lactation and also demonstrated substantial variation in the baseline hazard among cows and between herds. A small simulation study for a setting similar to the real data was conducted to evaluate the Poisson maximum likelihood estimation approach with both Gaussian quadrature method and Laplace approximation. Further, the performance of the two methods was compared with the performance of a widely used estimation

  3. Survival Function Analysis of Planet Size Distribution

    OpenAIRE

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

    2018-01-01

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

  4. Life-Cycle Models for Survivable Systems

    National Research Council Canada - National Science Library

    Linger, Richard

    2002-01-01

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

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

    NARCIS (Netherlands)

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

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

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

    NARCIS (Netherlands)

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

    2013-01-01

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

  7. Survival analysis II: Cox regression

    NARCIS (Netherlands)

    Stel, Vianda S.; Dekker, Friedo W.; Tripepi, Giovanni; Zoccali, Carmine; Jager, Kitty J.

    2011-01-01

    In contrast to the Kaplan-Meier method, Cox proportional hazards regression can provide an effect estimate by quantifying the difference in survival between patient groups and can adjust for confounding effects of other variables. The purpose of this article is to explain the basic concepts of the

  8. Survival analysis, the infinite Gaussian mixture model, FDG-PET and non-imaging data in the prediction of progression from mild cognitive impairment

    OpenAIRE

    Li, Rui; Perneczky, Robert; Drzezga, Alexander; Kramer, Stefan; Initiative, for the Alzheimer's Disease Neuroimaging

    2015-01-01

    We present a method to discover interesting brain regions in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, showing also the benefits when PET scans are in combined use with non-imaging variables. The discriminative brain regions facilitate a better understanding of Alzheimer's disease (AD) progression, and they can also be used for predicting conversion from mild cognitive impairment (MCI) to AD. A survival analysis(Cox regression) and infinite Gaussian mixture model (IGM...

  9. WE-H-BRA-08: A Monte Carlo Cell Nucleus Model for Assessing Cell Survival Probability Based On Particle Track Structure Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lee, B [Northwestern Memorial Hospital, Chicago, IL (United States); Georgia Institute of Technology, Atlanta, GA (Georgia); Wang, C [Georgia Institute of Technology, Atlanta, GA (Georgia)

    2016-06-15

    Purpose: To correlate the damage produced by particles of different types and qualities to cell survival on the basis of nanodosimetric analysis and advanced DNA structures in the cell nucleus. Methods: A Monte Carlo code was developed to simulate subnuclear DNA chromatin fibers (CFs) of 30nm utilizing a mean-free-path approach common to radiation transport. The cell nucleus was modeled as a spherical region containing 6000 chromatin-dense domains (CDs) of 400nm diameter, with additional CFs modeled in a sparser interchromatin region. The Geant4-DNA code was utilized to produce a particle track database representing various particles at different energies and dose quantities. These tracks were used to stochastically position the DNA structures based on their mean free path to interaction with CFs. Excitation and ionization events intersecting CFs were analyzed using the DBSCAN clustering algorithm for assessment of the likelihood of producing DSBs. Simulated DSBs were then assessed based on their proximity to one another for a probability of inducing cell death. Results: Variations in energy deposition to chromatin fibers match expectations based on differences in particle track structure. The quality of damage to CFs based on different particle types indicate more severe damage by high-LET radiation than low-LET radiation of identical particles. In addition, the model indicates more severe damage by protons than of alpha particles of same LET, which is consistent with differences in their track structure. Cell survival curves have been produced showing the L-Q behavior of sparsely ionizing radiation. Conclusion: Initial results indicate the feasibility of producing cell survival curves based on the Monte Carlo cell nucleus method. Accurate correlation between simulated DNA damage to cell survival on the basis of nanodosimetric analysis can provide insight into the biological responses to various radiation types. Current efforts are directed at producing cell

  10. Survivability Assessment: Modeling A Recovery Process

    OpenAIRE

    Paputungan, Irving Vitra; Abdullah, Azween

    2009-01-01

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

  11. Understanding survival analysis: Kaplan-Meier estimate.

    Science.gov (United States)

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

    2010-10-01

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

  12. Modeling the recurrent failure to thrive in less than two-year children: recurrent events survival analysis.

    Science.gov (United States)

    Saki Malehi, Amal; Hajizadeh, Ebrahim; Ahmadi, Kambiz; Kholdi, Nahid

    2014-01-01

    This study aimes to evaluate the failure to thrive (FTT) recurrent event over time. This longitudinal study was conducted during February 2007 to July 2009. The primary outcome was growth failure. The analysis was done using 1283 children who had experienced FTT several times, based on recurrent events analysis. Fifty-nine percent of the children had experienced the FTT at least one time and 5.3% of them had experienced it up to four times. The Prentice-Williams-Peterson (PWP) model revealed significant relationship between diarrhea (HR=1.26), respiratory infections (HR=1.25), urinary tract infections (HR=1.51), discontinuation of breast-feeding (HR=1.96), teething (HR=1.18), initiation age of complementary feeding (HR=1.11) and hazard rate of the first FTT event. Recurrence nature of the FTT is a main problem, which taking it into account increases the accuracy in analysis of FTT event process and can lead to identify different risk factors for each FTT recurrences.

  13. SURVIVAL ANALYSIS AND LENGTH-BIASED SAMPLING

    Directory of Open Access Journals (Sweden)

    Masoud Asgharian

    2010-12-01

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

  14. Prognostic and survival analysis of 837 Chinese colorectal cancer patients.

    Science.gov (United States)

    Yuan, Ying; Li, Mo-Dan; Hu, Han-Guang; Dong, Cai-Xia; Chen, Jia-Qi; Li, Xiao-Fen; Li, Jing-Jing; Shen, Hong

    2013-05-07

    To develop a prognostic model to predict survival of patients with colorectal cancer (CRC). Survival data of 837 CRC patients undergoing surgery between 1996 and 2006 were collected and analyzed by univariate analysis and Cox proportional hazard regression model to reveal the prognostic factors for CRC. All data were recorded using a standard data form and analyzed using SPSS version 18.0 (SPSS, Chicago, IL, United States). Survival curves were calculated by the Kaplan-Meier method. The log rank test was used to assess differences in survival. Univariate hazard ratios and significant and independent predictors of disease-specific survival and were identified by Cox proportional hazard analysis. The stepwise procedure was set to a threshold of 0.05. Statistical significance was defined as P analysis suggested age, preoperative obstruction, serum carcinoembryonic antigen level at diagnosis, status of resection, tumor size, histological grade, pathological type, lymphovascular invasion, invasion of adjacent organs, and tumor node metastasis (TNM) staging were positive prognostic factors (P analysis showed a significant statistical difference in 3-year survival among these groups: LNR1, 73%; LNR2, 55%; and LNR3, 42% (P analysis results showed that histological grade, depth of bowel wall invasion, and number of metastatic lymph nodes were the most important prognostic factors for CRC if we did not consider the interaction of the TNM staging system (P < 0.05). When the TNM staging was taken into account, histological grade lost its statistical significance, while the specific TNM staging system showed a statistically significant difference (P < 0.0001). The overall survival of CRC patients has improved between 1996 and 2006. LNR is a powerful factor for estimating the survival of stage III CRC patients.

  15. Breast cancer data analysis for survivability studies and prediction.

    Science.gov (United States)

    Shukla, Nagesh; Hagenbuchner, Markus; Win, Khin Than; Yang, Jack

    2018-03-01

    Breast cancer is the most common cancer affecting females worldwide. Breast cancer survivability prediction is challenging and a complex research task. Existing approaches engage statistical methods or supervised machine learning to assess/predict the survival prospects of patients. The main objectives of this paper is to develop a robust data analytical model which can assist in (i) a better understanding of breast cancer survivability in presence of missing data, (ii) providing better insights into factors associated with patient survivability, and (iii) establishing cohorts of patients that share similar properties. Unsupervised data mining methods viz. the self-organising map (SOM) and density-based spatial clustering of applications with noise (DBSCAN) is used to create patient cohort clusters. These clusters, with associated patterns, were used to train multilayer perceptron (MLP) model for improved patient survivability analysis. A large dataset available from SEER program is used in this study to identify patterns associated with the survivability of breast cancer patients. Information gain was computed for the purpose of variable selection. All of these methods are data-driven and require little (if any) input from users or experts. SOM consolidated patients into cohorts of patients with similar properties. From this, DBSCAN identified and extracted nine cohorts (clusters). It is found that patients in each of the nine clusters have different survivability time. The separation of patients into clusters improved the overall survival prediction accuracy based on MLP and revealed intricate conditions that affect the accuracy of a prediction. A new, entirely data driven approach based on unsupervised learning methods improves understanding and helps identify patterns associated with the survivability of patient. The results of the analysis can be used to segment the historical patient data into clusters or subsets, which share common variable values and

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

    Science.gov (United States)

    Llorca, Javier; Delgado-Rodríguez, Miguel

    2004-01-01

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

  17. Survival analysis for customer satisfaction: A case study

    Science.gov (United States)

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

    2017-11-01

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

  18. Direct Survival Analysis: a new stock assessment method

    Directory of Open Access Journals (Sweden)

    Eduardo Ferrandis

    2007-03-01

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

  19. A generalized additive regression model for survival times

    DEFF Research Database (Denmark)

    Scheike, Thomas H.

    2001-01-01

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

  20. Covariate analysis of bivariate survival data

    Energy Technology Data Exchange (ETDEWEB)

    Bennett, L.E.

    1992-01-01

    The methods developed are used to analyze the effects of covariates on bivariate survival data when censoring and ties are present. The proposed method provides models for bivariate survival data that include differential covariate effects and censored observations. The proposed models are based on an extension of the univariate Buckley-James estimators which replace censored data points by their expected values, conditional on the censoring time and the covariates. For the bivariate situation, it is necessary to determine the expectation of the failure times for one component conditional on the failure or censoring time of the other component. Two different methods have been developed to estimate these expectations. In the semiparametric approach these expectations are determined from a modification of Burke's estimate of the bivariate empirical survival function. In the parametric approach censored data points are also replaced by their conditional expected values where the expected values are determined from a specified parametric distribution. The model estimation will be based on the revised data set, comprised of uncensored components and expected values for the censored components. The variance-covariance matrix for the estimated covariate parameters has also been derived for both the semiparametric and parametric methods. Data from the Demographic and Health Survey was analyzed by these methods. The two outcome variables are post-partum amenorrhea and breastfeeding; education and parity were used as the covariates. Both the covariate parameter estimates and the variance-covariance estimates for the semiparametric and parametric models will be compared. In addition, a multivariate test statistic was used in the semiparametric model to examine contrasts. The significance of the statistic was determined from a bootstrap distribution of the test statistic.

  1. Prognostic and survival analysis of presbyopia: The healthy twin study

    Science.gov (United States)

    Lira, Adiyani; Sung, Joohon

    2015-12-01

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

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

    Czech Academy of Sciences Publication Activity Database

    Timková, Jana

    2010-01-01

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

  3. The Design and Analysis of Salmonid Tagging Studies in the Columbia Basin; Volume XII; A Multinomial Model for Estimating Ocean Survival from Salmonid Coded Wire-Tag Data.

    Energy Technology Data Exchange (ETDEWEB)

    Ryding, Kristen E.; Skalski, John R.

    1999-06-01

    The purpose of this report is to illustrate the development of a stochastic model using coded wire-tag (CWT) release and age-at-return data, in order to regress first year ocean survival probabilities against coastal ocean conditions and climate covariates.

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

    International Nuclear Information System (INIS)

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

    1989-01-01

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

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

    International Nuclear Information System (INIS)

    Lowther, A.B.; Skalski, J.

    1997-09-01

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

  6. Survival prediction model for postoperative hepatocellular carcinoma patients.

    Science.gov (United States)

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

    2017-09-01

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

  7. Standard model group: Survival of the fittest

    Science.gov (United States)

    Nielsen, H. B.; Brene, N.

    1983-09-01

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

  8. Standard model group: survival of the fittest

    Energy Technology Data Exchange (ETDEWEB)

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

    1983-09-19

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

  9. Standard model group: survival of the fittest

    International Nuclear Information System (INIS)

    Nielsen, H.B.; Brene, N.

    1983-01-01

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

  10. Standard model group survival of the fittest

    International Nuclear Information System (INIS)

    Nielsen, H.B.; Brene, N.

    1983-02-01

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

  11. Integrative Analysis of PRKAG2 Cardiomyopathy iPS and Microtissue Models Identifies AMPK as a Regulator of Metabolism, Survival, and Fibrosis

    Directory of Open Access Journals (Sweden)

    J. Travis Hinson

    2016-12-01

    Full Text Available AMP-activated protein kinase (AMPK is a metabolic enzyme that can be activated by nutrient stress or genetic mutations. Missense mutations in the regulatory subunit, PRKAG2, activate AMPK and cause left ventricular hypertrophy, glycogen accumulation, and ventricular pre-excitation. Using human iPS cell models combined with three-dimensional cardiac microtissues, we show that activating PRKAG2 mutations increase microtissue twitch force by enhancing myocyte survival. Integrating RNA sequencing with metabolomics, PRKAG2 mutations that activate AMPK remodeled global metabolism by regulating RNA transcripts to favor glycogen storage and oxidative metabolism instead of glycolysis. As in patients with PRKAG2 cardiomyopathy, iPS cell and mouse models are protected from cardiac fibrosis, and we define a crosstalk between AMPK and post-transcriptional regulation of TGFβ isoform signaling that has implications in fibrotic forms of cardiomyopathy. Our results establish critical connections among metabolic sensing, myocyte survival, and TGFβ signaling.

  12. Analysis of telomerase target gene expression effects from murine models in patient cohorts by homology translation and random survival forest modeling

    Directory of Open Access Journals (Sweden)

    Frederik Otzen Bagger

    2016-03-01

    Full Text Available Acute myeloid leukemia (AML is an aggressive and rapidly fatal blood cancer that affects patients of any age group. Despite an initial response to standard chemotherapy, most patients relapse and this relapse is mediated by leukemia stem cell (LSC populations. We identified a functional requirement for telomerase in sustaining LSC populations in murine models of AML and validated this requirement using an inhibitor of telomerase in human AML. Here, we describe in detail the contents, quality control and methods of the gene expression analysis used in the published study (Gene Expression Omnibus GSE63242. Additionally, we provide annotated gene lists of telomerase regulated genes in AML and R code snippets to access and analyze the data used in the original manuscript. Keywords: AML, Leukemia, Stem cells, Telomere, Telomerase

  13. Evaluating disease management program effectiveness: an introduction to survival analysis.

    Science.gov (United States)

    Linden, Ariel; Adams, John L; Roberts, Nancy

    2004-01-01

    Currently, the most widely used method in the disease management industry for evaluating program effectiveness is the "total population approach." This model is a pretest-posttest design, with the most basic limitation being that without a control group, there may be sources of bias and/or competing extraneous confounding factors that offer plausible rationale explaining the change from baseline. Survival analysis allows for the inclusion of data from censored cases, those subjects who either "survived" the program without experiencing the event (e.g., achievement of target clinical levels, hospitalization) or left the program prematurely, due to disenrollement from the health plan or program, or were lost to follow-up. Additionally, independent variables may be included in the model to help explain the variability in the outcome measure. In order to maximize the potential of this statistical method, validity of the model and research design must be assured. This paper reviews survival analysis as an alternative, and more appropriate, approach to evaluating DM program effectiveness than the current total population approach.

  14. Survival analysis in hematologic malignancies: recommendations for clinicians

    Science.gov (United States)

    Delgado, Julio; Pereira, Arturo; Villamor, Neus; López-Guillermo, Armando; Rozman, Ciril

    2014-01-01

    The widespread availability of statistical packages has undoubtedly helped hematologists worldwide in the analysis of their data, but has also led to the inappropriate use of statistical methods. In this article, we review some basic concepts of survival analysis and also make recommendations about how and when to perform each particular test using SPSS, Stata and R. In particular, we describe a simple way of defining cut-off points for continuous variables and the appropriate and inappropriate uses of the Kaplan-Meier method and Cox proportional hazard regression models. We also provide practical advice on how to check the proportional hazards assumption and briefly review the role of relative survival and multiple imputation. PMID:25176982

  15. Multilevel survival analysis of health inequalities in life expectancy

    Directory of Open Access Journals (Sweden)

    Merlo Juan

    2009-08-01

    Full Text Available Abstract Background The health status of individuals is determined by multiple factors operating at both micro and macro levels and the interactive effects of them. Measures of health inequalities should reflect such determinants explicitly through sources of levels and combining mean differences at group levels and the variation of individuals, for the benefits of decision making and intervention planning. Measures derived recently from marginal models such as beta-binomial and frailty survival, address this issue to some extent, but are limited in handling data with complex structures. Beta-binomial models were also limited in relation to measuring inequalities of life expectancy (LE directly. Methods We propose a multilevel survival model analysis that estimates life expectancy based on survival time with censored data. The model explicitly disentangles total health inequalities in terms of variance components of life expectancy compared to the source of variation at the level of individuals in households and parishes and so on, and estimates group differences of inequalities at the same time. Adjusted distributions of life expectancy by gender and by household socioeconomic level are calculated. Relative and absolute health inequality indices are derived based on model estimates. The model based analysis is illustrated on a large Swedish cohort of 22,680 men and 26,474 women aged 65–69 in 1970 and followed up for 30 years. Model based inequality measures are compared to the conventional calculations. Results Much variation of life expectancy is observed at individual and household levels. Contextual effects at Parish and Municipality level are negligible. Women have longer life expectancy than men and lower inequality. There is marked inequality by the level of household socioeconomic status measured by the median life expectancy in each socio-economic group and the variation in life expectancy within each group. Conclusion Multilevel

  16. Analyzing sickness absence with statistical models for survival data

    DEFF Research Database (Denmark)

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

    2007-01-01

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

  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....

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

    DEFF Research Database (Denmark)

    Scheike, Thomas H.; Zhang, Mei-Jie

    2003-01-01

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

  19. Mathematical Methods in Survival Analysis, Reliability and Quality of Life

    CERN Document Server

    Huber, Catherine; Mesbah, Mounir

    2008-01-01

    Reliability and survival analysis are important applications of stochastic mathematics (probability, statistics and stochastic processes) that are usually covered separately in spite of the similarity of the involved mathematical theory. This title aims to redress this situation: it includes 21 chapters divided into four parts: Survival analysis, Reliability, Quality of life, and Related topics. Many of these chapters were presented at the European Seminar on Mathematical Methods for Survival Analysis, Reliability and Quality of Life in 2006.

  20. Survival analysis of heart failure patients: A case study.

    Directory of Open Access Journals (Sweden)

    Tanvir Ahmad

    Full Text Available This study was focused on survival analysis of heart failure patients who were admitted to Institute of Cardiology and Allied hospital Faisalabad-Pakistan during April-December (2015. All the patients were aged 40 years or above, having left ventricular systolic dysfunction, belonging to NYHA class III and IV. Cox regression was used to model mortality considering age, ejection fraction, serum creatinine, serum sodium, anemia, platelets, creatinine phosphokinase, blood pressure, gender, diabetes and smoking status as potentially contributing for mortality. Kaplan Meier plot was used to study the general pattern of survival which showed high intensity of mortality in the initial days and then a gradual increase up to the end of study. Martingale residuals were used to assess functional form of variables. Results were validated computing calibration slope and discrimination ability of model via bootstrapping. For graphical prediction of survival probability, a nomogram was constructed. Age, renal dysfunction, blood pressure, ejection fraction and anemia were found as significant risk factors for mortality among heart failure patients.

  1. Survival analysis of heart failure patients: A case study.

    Science.gov (United States)

    Ahmad, Tanvir; Munir, Assia; Bhatti, Sajjad Haider; Aftab, Muhammad; Raza, Muhammad Ali

    2017-01-01

    This study was focused on survival analysis of heart failure patients who were admitted to Institute of Cardiology and Allied hospital Faisalabad-Pakistan during April-December (2015). All the patients were aged 40 years or above, having left ventricular systolic dysfunction, belonging to NYHA class III and IV. Cox regression was used to model mortality considering age, ejection fraction, serum creatinine, serum sodium, anemia, platelets, creatinine phosphokinase, blood pressure, gender, diabetes and smoking status as potentially contributing for mortality. Kaplan Meier plot was used to study the general pattern of survival which showed high intensity of mortality in the initial days and then a gradual increase up to the end of study. Martingale residuals were used to assess functional form of variables. Results were validated computing calibration slope and discrimination ability of model via bootstrapping. For graphical prediction of survival probability, a nomogram was constructed. Age, renal dysfunction, blood pressure, ejection fraction and anemia were found as significant risk factors for mortality among heart failure patients.

  2. Survival models for harvest management of mourning dove populations

    Science.gov (United States)

    Otis, D.L.

    2002-01-01

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

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

    Science.gov (United States)

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

    2018-01-01

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

  4. CASAS: Cancer Survival Analysis Suite, a web based application.

    Science.gov (United States)

    Rupji, Manali; Zhang, Xinyan; Kowalski, Jeanne

    2017-01-01

    We present CASAS, a shiny R based tool for interactive survival analysis and visualization of results. The tool provides a web-based one stop shop to perform the following types of survival analysis:  quantile, landmark and competing risks, in addition to standard survival analysis.  The interface makes it easy to perform such survival analyses and obtain results using the interactive Kaplan-Meier and cumulative incidence plots.  Univariate analysis can be performed on one or several user specified variable(s) simultaneously, the results of which are displayed in a single table that includes log rank p-values and hazard ratios along with their significance. For several quantile survival analyses from multiple cancer types, a single summary grid is constructed. The CASAS package has been implemented in R and is available via http://shinygispa.winship.emory.edu/CASAS/. The developmental repository is available at https://github.com/manalirupji/CASAS/.

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

    Science.gov (United States)

    Kate, Rohit J; Nadig, Ramya

    2017-01-01

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

  6. Bayesian linear regression with skew-symmetric error distributions with applications to survival analysis

    KAUST Repository

    Rubio, Francisco J.; Genton, Marc G.

    2016-01-01

    are censored. The latter scenario is of interest in the context of accelerated failure time models, which are relevant in survival analysis. We present a simulation study that demonstrates good frequentist properties of the posterior credible intervals

  7. Developing a scalable modeling architecture for studying survivability technologies

    Science.gov (United States)

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

    2006-05-01

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

  8. 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.

  9. 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.

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

    Science.gov (United States)

    Huang, Yen-Tsung; Yang, Hwai-I

    2017-05-01

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

  11. Pseudo-observations in survival analysis

    DEFF Research Database (Denmark)

    Andersen, Per Kragh; Perme, Maja Pohar

    2010-01-01

    -state models, e.g. the competing risks cumulative incidence function. Graphical and numerical methods for assessing goodness-of-fit for hazard regression models and for the Fine-Gray model in competing risks studies based on pseudo-observations are also reviewed. Sensitivity to covariate-dependent censoring...

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

    CERN Document Server

    Emura, Takeshi

    2018-01-01

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

  13. A Framework for RFID Survivability Requirement Analysis and Specification

    Science.gov (United States)

    Zuo, Yanjun; Pimple, Malvika; Lande, Suhas

    Many industries are becoming dependent on Radio Frequency Identification (RFID) technology for inventory management and asset tracking. The data collected about tagged objects though RFID is used in various high level business operations. The RFID system should hence be highly available, reliable, and dependable and secure. In addition, this system should be able to resist attacks and perform recovery in case of security incidents. Together these requirements give rise to the notion of a survivable RFID system. The main goal of this paper is to analyze and specify the requirements for an RFID system to become survivable. These requirements, if utilized, can assist the system in resisting against devastating attacks and recovering quickly from damages. This paper proposes the techniques and approaches for RFID survivability requirements analysis and specification. From the perspective of system acquisition and engineering, survivability requirement is the important first step in survivability specification, compliance formulation, and proof verification.

  14. An Approach to Addressing Selection Bias in Survival Analysis

    Science.gov (United States)

    Carlin, Caroline S.; Solid, Craig A.

    2014-01-01

    This work proposes a frailty model that accounts for non-random treatment assignment in survival analysis. Using Monte Carlo simulation, we found that estimated treatment parameters from our proposed endogenous selection survival model (esSurv) closely parallel the consistent two-stage residual inclusion (2SRI) results, while offering computational and interpretive advantages. The esSurv method greatly enhances computational speed relative to 2SRI by eliminating the need for bootstrapped standard errors, and generally results in smaller standard errors than those estimated by 2SRI. In addition, esSurv explicitly estimates the correlation of unobservable factors contributing to both treatment assignment and the outcome of interest, providing an interpretive advantage over the residual parameter estimate in the 2SRI method. Comparisons with commonly used propensity score methods and with a model that does not account for non-random treatment assignment show clear bias in these methods that is not mitigated by increased sample size. We illustrate using actual dialysis patient data comparing mortality of patients with mature arteriovenous grafts for venous access to mortality of patients with grafts placed but not yet ready for use at the initiation of dialysis. We find strong evidence of endogeneity (with estimate of correlation in unobserved factors ρ̂ = 0.55), and estimate a mature-graft hazard ratio of 0.197 in our proposed method, with a similar 0.173 hazard ratio using 2SRI. The 0.630 hazard ratio from a frailty model without a correction for the non-random nature of treatment assignment illustrates the importance of accounting for endogeneity. PMID:24845211

  15. Survival analysis and classification methods for forest fire size.

    Science.gov (United States)

    Tremblay, Pier-Olivier; Duchesne, Thierry; Cumming, Steven G

    2018-01-01

    Factors affecting wildland-fire size distribution include weather, fuels, and fire suppression activities. We present a novel application of survival analysis to quantify the effects of these factors on a sample of sizes of lightning-caused fires from Alberta, Canada. Two events were observed for each fire: the size at initial assessment (by the first fire fighters to arrive at the scene) and the size at "being held" (a state when no further increase in size is expected). We developed a statistical classifier to try to predict cases where there will be a growth in fire size (i.e., the size at "being held" exceeds the size at initial assessment). Logistic regression was preferred over two alternative classifiers, with covariates consistent with similar past analyses. We conducted survival analysis on the group of fires exhibiting a size increase. A screening process selected three covariates: an index of fire weather at the day the fire started, the fuel type burning at initial assessment, and a factor for the type and capabilities of the method of initial attack. The Cox proportional hazards model performed better than three accelerated failure time alternatives. Both fire weather and fuel type were highly significant, with effects consistent with known fire behaviour. The effects of initial attack method were not statistically significant, but did suggest a reverse causality that could arise if fire management agencies were to dispatch resources based on a-priori assessment of fire growth potentials. We discuss how a more sophisticated analysis of larger data sets could produce unbiased estimates of fire suppression effect under such circumstances.

  16. Survival analysis and classification methods for forest fire size

    Science.gov (United States)

    2018-01-01

    Factors affecting wildland-fire size distribution include weather, fuels, and fire suppression activities. We present a novel application of survival analysis to quantify the effects of these factors on a sample of sizes of lightning-caused fires from Alberta, Canada. Two events were observed for each fire: the size at initial assessment (by the first fire fighters to arrive at the scene) and the size at “being held” (a state when no further increase in size is expected). We developed a statistical classifier to try to predict cases where there will be a growth in fire size (i.e., the size at “being held” exceeds the size at initial assessment). Logistic regression was preferred over two alternative classifiers, with covariates consistent with similar past analyses. We conducted survival analysis on the group of fires exhibiting a size increase. A screening process selected three covariates: an index of fire weather at the day the fire started, the fuel type burning at initial assessment, and a factor for the type and capabilities of the method of initial attack. The Cox proportional hazards model performed better than three accelerated failure time alternatives. Both fire weather and fuel type were highly significant, with effects consistent with known fire behaviour. The effects of initial attack method were not statistically significant, but did suggest a reverse causality that could arise if fire management agencies were to dispatch resources based on a-priori assessment of fire growth potentials. We discuss how a more sophisticated analysis of larger data sets could produce unbiased estimates of fire suppression effect under such circumstances. PMID:29320497

  17. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves

    Directory of Open Access Journals (Sweden)

    Guyot Patricia

    2012-02-01

    Full Text Available Abstract Background The results of Randomized Controlled Trials (RCTs on time-to-event outcomes that are usually reported are median time to events and Cox Hazard Ratio. These do not constitute the sufficient statistics required for meta-analysis or cost-effectiveness analysis, and their use in secondary analyses requires strong assumptions that may not have been adequately tested. In order to enhance the quality of secondary data analyses, we propose a method which derives from the published Kaplan Meier survival curves a close approximation to the original individual patient time-to-event data from which they were generated. Methods We develop an algorithm that maps from digitised curves back to KM data by finding numerical solutions to the inverted KM equations, using where available information on number of events and numbers at risk. The reproducibility and accuracy of survival probabilities, median survival times and hazard ratios based on reconstructed KM data was assessed by comparing published statistics (survival probabilities, medians and hazard ratios with statistics based on repeated reconstructions by multiple observers. Results The validation exercise established there was no material systematic error and that there was a high degree of reproducibility for all statistics. Accuracy was excellent for survival probabilities and medians, for hazard ratios reasonable accuracy can only be obtained if at least numbers at risk or total number of events are reported. Conclusion The algorithm is a reliable tool for meta-analysis and cost-effectiveness analyses of RCTs reporting time-to-event data. It is recommended that all RCTs should report information on numbers at risk and total number of events alongside KM curves.

  18. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves.

    Science.gov (United States)

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

    2012-02-01

    The results of Randomized Controlled Trials (RCTs) on time-to-event outcomes that are usually reported are median time to events and Cox Hazard Ratio. These do not constitute the sufficient statistics required for meta-analysis or cost-effectiveness analysis, and their use in secondary analyses requires strong assumptions that may not have been adequately tested. In order to enhance the quality of secondary data analyses, we propose a method which derives from the published Kaplan Meier survival curves a close approximation to the original individual patient time-to-event data from which they were generated. We develop an algorithm that maps from digitised curves back to KM data by finding numerical solutions to the inverted KM equations, using where available information on number of events and numbers at risk. The reproducibility and accuracy of survival probabilities, median survival times and hazard ratios based on reconstructed KM data was assessed by comparing published statistics (survival probabilities, medians and hazard ratios) with statistics based on repeated reconstructions by multiple observers. The validation exercise established there was no material systematic error and that there was a high degree of reproducibility for all statistics. Accuracy was excellent for survival probabilities and medians, for hazard ratios reasonable accuracy can only be obtained if at least numbers at risk or total number of events are reported. The algorithm is a reliable tool for meta-analysis and cost-effectiveness analyses of RCTs reporting time-to-event data. It is recommended that all RCTs should report information on numbers at risk and total number of events alongside KM curves.

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

    DEFF Research Database (Denmark)

    Carstensen, Bendix

    1996-01-01

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

  20. Reporting and methodological quality of survival analysis in articles published in Chinese oncology journals.

    Science.gov (United States)

    Zhu, Xiaoyan; Zhou, Xiaobin; Zhang, Yuan; Sun, Xiao; Liu, Haihua; Zhang, Yingying

    2017-12-01

    Survival analysis methods have gained widespread use in the filed of oncology. For achievement of reliable results, the methodological process and report quality is crucial. This review provides the first examination of methodological characteristics and reporting quality of survival analysis in articles published in leading Chinese oncology journals.To examine methodological and reporting quality of survival analysis, to identify some common deficiencies, to desirable precautions in the analysis, and relate advice for authors, readers, and editors.A total of 242 survival analysis articles were included to be evaluated from 1492 articles published in 4 leading Chinese oncology journals in 2013. Articles were evaluated according to 16 established items for proper use and reporting of survival analysis.The application rates of Kaplan-Meier, life table, log-rank test, Breslow test, and Cox proportional hazards model (Cox model) were 91.74%, 3.72%, 78.51%, 0.41%, and 46.28%, respectively, no article used the parametric method for survival analysis. Multivariate Cox model was conducted in 112 articles (46.28%). Follow-up rates were mentioned in 155 articles (64.05%), of which 4 articles were under 80% and the lowest was 75.25%, 55 articles were100%. The report rates of all types of survival endpoint were lower than 10%. Eleven of 100 articles which reported a loss to follow-up had stated how to treat it in the analysis. One hundred thirty articles (53.72%) did not perform multivariate analysis. One hundred thirty-nine articles (57.44%) did not define the survival time. Violations and omissions of methodological guidelines included no mention of pertinent checks for proportional hazard assumption; no report of testing for interactions and collinearity between independent variables; no report of calculation method of sample size. Thirty-six articles (32.74%) reported the methods of independent variable selection. The above defects could make potentially inaccurate

  1. Repair-misrepair model of cell survival

    International Nuclear Information System (INIS)

    Tobias, C.A.; Blakely, E.A.; Ngo, F.Q.H.

    1980-01-01

    During the last three years a new model, the repair-misrepair model (RMR) has been proposed, to interpret radiobiological experiments with heavy ions. In using the RMR model it became apparent that some of its features are suitable for handling the effects produced by a variety of environmental agents in addition to ionizing radiation. Two separate sequences of events are assumed to take place in an irradiated cell. The first sequence begins with an initial energy transfer consisting of ionizations and excitations, culminating via fast secondary physical and chemical processes in established macromolecular lesions in essential cell structures. The second sequence contains the responses of the cell to the lesions and consists of the processes of recognition and molecular repair. In normal cells there exists one repair process or at most a few enzymatic repair processes for each essential macromolecular lesion. The enzymatic repair processes may last for hours and minutes, and can be separated in time from the initial physicochemical and later genetic phases

  2. In-season retail sales forecasting using survival models

    African Journals Online (AJOL)

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

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

    Science.gov (United States)

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

    2017-12-01

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

  4. A survival analysis on critical components of nuclear power plants

    International Nuclear Information System (INIS)

    Durbec, V.; Pitner, P.; Riffard, T.

    1995-06-01

    Some tubes of heat exchangers of nuclear power plants may be affected by Primary Water Stress Corrosion Cracking (PWSCC) in highly stressed areas. These defects can shorten the lifetime of the component and lead to its replacement. In order to reduce the risk of cracking, a preventive remedial operation called shot peening was applied on the French reactors between 1985 and 1988. To assess and investigate the effects of shot peening, a statistical analysis was carried on the tube degradation results obtained from in service inspection that are regularly conducted using non destructive tests. The statistical method used is based on the Cox proportional hazards model, a powerful tool in the analysis of survival data, implemented in PROC PHRED recently available in SAS/STAT. This technique has a number of major advantages including the ability to deal with censored failure times data and with the complication of time-dependant co-variables. The paper focus on the modelling and a presentation of the results given by SAS. They provide estimate of how the relative risk of degradation changes after peening and indicate for which values of the prognostic factors analyzed the treatment is likely to be most beneficial. (authors). 2 refs., 3 figs., 6 tabs

  5. Survival Analysis of Patients with End Stage Renal Disease

    Science.gov (United States)

    Urrutia, J. D.; Gayo, W. S.; Bautista, L. A.; Baccay, E. B.

    2015-06-01

    This paper provides a survival analysis of End Stage Renal Disease (ESRD) under Kaplan-Meier Estimates and Weibull Distribution. The data were obtained from the records of V. L. MakabaliMemorial Hospital with respect to time t (patient's age), covariates such as developed secondary disease (Pulmonary Congestion and Cardiovascular Disease), gender, and the event of interest: the death of ESRD patients. Survival and hazard rates were estimated using NCSS for Weibull Distribution and SPSS for Kaplan-Meier Estimates. These lead to the same conclusion that hazard rate increases and survival rate decreases of ESRD patient diagnosed with Pulmonary Congestion, Cardiovascular Disease and both diseases with respect to time. It also shows that female patients have a greater risk of death compared to males. The probability risk was given the equation R = 1 — e-H(t) where e-H(t) is the survival function, H(t) the cumulative hazard function which was created using Cox-Regression.

  6. 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.

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

    Science.gov (United States)

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

    2016-07-06

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

  8. 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 ...

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

    Science.gov (United States)

    2010-01-01

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

  10. 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

  11. Causal inference in survival analysis using pseudo-observations

    DEFF Research Database (Denmark)

    Andersen, Per K; Syriopoulou, Elisavet; Parner, Erik T

    2017-01-01

    Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs ...

  12. Using Survival Analysis to Evaluate Medical Equipment Battery Life.

    Science.gov (United States)

    Kuhajda, David

    2016-01-01

    As hospital medical device managers obtain more data, opportunities exist for using the data to improve medical device management, enhance patient safety, and evaluate costs of decisions. As a demonstration of the ability to use data analytics, this article applies survival analysis statistical techniques to assist in making decisions on medical equipment maintenance. The analysis was performed on a large amount of data related to failures of an infusion pump manufacturer's lithium battery and two aftermarket replacement lithium batteries from one hospital facility. The survival analysis resulted in statistical evidence showing that one of the third-party batteries had a lower survival curve than the infusion pump manufacturer's battery. This lower survival curve translates to a shorter expected life before replacement is needed. The data suggested that to limit unexpected failures, replacing batteries at a two-year interval, rather than the current industry recommendation of three years, may be warranted. For less than $5,400 in additional annual cost, the risk of unexpected battery failures can be reduced from an estimated 28% to an estimated 7%.

  13. Discrete dynamic modeling of T cell survival signaling networks

    Science.gov (United States)

    Zhang, Ranran

    2009-03-01

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

  14. Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data.

    Science.gov (United States)

    Tekwe, Carmen D; Carroll, Raymond J; Dabney, Alan R

    2012-08-01

    Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets. Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions. The testing procedures discussed in this article can all be performed using readily available software such as R. The R codes are provided as supplemental materials. ctekwe@stat.tamu.edu.

  15. Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data

    KAUST Repository

    Tekwe, C. D.

    2012-05-24

    MOTIVATION: Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets. RESULTS: Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions. AVAILABILITY: The testing procedures discussed in this article can all be performed using readily available software such as R. The R codes are provided as supplemental materials. CONTACT: ctekwe@stat.tamu.edu.

  16. Causal inference in survival analysis using pseudo-observations.

    Science.gov (United States)

    Andersen, Per K; Syriopoulou, Elisavet; Parner, Erik T

    2017-07-30

    Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs to address right-censoring, and often, special techniques are required for that purpose. We will show how censoring can be dealt with 'once and for all' by means of so-called pseudo-observations when doing causal inference in survival analysis. The pseudo-observations can be used as a replacement of the outcomes without censoring when applying 'standard' causal inference methods, such as (1) or (2) earlier. We study this idea for estimating the average causal effect of a binary treatment on the survival probability, the restricted mean lifetime, and the cumulative incidence in a competing risks situation. The methods will be illustrated in a small simulation study and via a study of patients with acute myeloid leukemia who received either myeloablative or non-myeloablative conditioning before allogeneic hematopoetic cell transplantation. We will estimate the average causal effect of the conditioning regime on outcomes such as the 3-year overall survival probability and the 3-year risk of chronic graft-versus-host disease. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  17. 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

  18. Nonparametric Bayesian inference for mean residual life functions in survival analysis.

    Science.gov (United States)

    Poynor, Valerie; Kottas, Athanasios

    2018-01-19

    Modeling and inference for survival analysis problems typically revolves around different functions related to the survival distribution. Here, we focus on the mean residual life (MRL) function, which provides the expected remaining lifetime given that a subject has survived (i.e. is event-free) up to a particular time. This function is of direct interest in reliability, medical, and actuarial fields. In addition to its practical interpretation, the MRL function characterizes the survival distribution. We develop general Bayesian nonparametric inference for MRL functions built from a Dirichlet process mixture model for the associated survival distribution. The resulting model for the MRL function admits a representation as a mixture of the kernel MRL functions with time-dependent mixture weights. This model structure allows for a wide range of shapes for the MRL function. Particular emphasis is placed on the selection of the mixture kernel, taken to be a gamma distribution, to obtain desirable properties for the MRL function arising from the mixture model. The inference method is illustrated with a data set of two experimental groups and a data set involving right censoring. The supplementary material available at Biostatistics online provides further results on empirical performance of the model, using simulated data examples. © The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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

    Science.gov (United States)

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

    2014-01-01

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

  20. Use of a Survival Analysis Technique in Understanding Game Performance in Instructional Games. CRESST Report 812

    Science.gov (United States)

    Kim, Jinok; Chung, Gregory K. W. K.

    2012-01-01

    In this study we compared the effects of two math game designs on math and game performance, using discrete-time survival analysis (DTSA) to model players' risk of not advancing to the next level in the game. 137 students were randomly assigned to two game conditions. The game covered the concept of a unit and the addition of like-sized fractional…

  1. 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.

  2. Survival analysis of a treatment data for cancer of the larynx

    International Nuclear Information System (INIS)

    Khan, K.

    2002-01-01

    In this paper a survival analysis of the survival time is done. The Cox regression model is fitted to the survival time with the assumption of proportional hazard. A model is selected after inclusion and exclusion of factors and variables as explanatory variables. The assumption of proportional hazards is tested in the manner suggested by Harrell (1986). The assumption of proportional hazards is supported by these tests. However the plot of Schoenfeld residuals against dose gave a little evidence of non validity of the proportional hazard assumption. The assumption seems to be satisfied for variable time. The martingale residuals suggest no pattern for variable age. The functional form of dose is not linear. Hence the quadratic dose is used as an explanatory variable. A comparison of logistic regression analysis and survival analysis is also made in this paper. It can be concluded that Cox proportional hazards model is a better model than the logistic model as it is more parsimonious and utilizes more information. (author)

  3. 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

  4. 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.

  5. Survival analysis of cancer risk reduction strategies for BRCA1/2 mutation carriers.

    Science.gov (United States)

    Kurian, Allison W; Sigal, Bronislava M; Plevritis, Sylvia K

    2010-01-10

    Women with BRCA1/2 mutations inherit high risks of breast and ovarian cancer; options to reduce cancer mortality include prophylactic surgery or breast screening, but their efficacy has never been empirically compared. We used decision analysis to simulate risk-reducing strategies in BRCA1/2 mutation carriers and to compare resulting survival probability and causes of death. We developed a Monte Carlo model of breast screening with annual mammography plus magnetic resonance imaging (MRI) from ages 25 to 69 years, prophylactic mastectomy (PM) at various ages, and/or prophylactic oophorectomy (PO) at ages 40 or 50 years in 25-year-old BRCA1/2 mutation carriers. With no intervention, survival probability by age 70 is 53% for BRCA1 and 71% for BRCA2 mutation carriers. The most effective single intervention for BRCA1 mutation carriers is PO at age 40, yielding a 15% absolute survival gain; for BRCA2 mutation carriers, the most effective single intervention is PM, yielding a 7% survival gain if performed at age 40 years. The combination of PM and PO at age 40 improves survival more than any single intervention, yielding 24% survival gain for BRCA1 and 11% for BRCA2 mutation carriers. PM at age 25 instead of age 40 offers minimal incremental benefit (1% to 2%); substituting screening for PM yields a similarly minimal decrement in survival (2% to 3%). Although PM at age 25 plus PO at age 40 years maximizes survival probability, substituting mammography plus MRI screening for PM seems to offer comparable survival. These results may guide women with BRCA1/2 mutations in their choices between prophylactic surgery and breast screening.

  6. Pregnancy associated nasopharyngeal carcinoma: A retrospective case-control analysis of maternal survival outcomes

    International Nuclear Information System (INIS)

    Cheng, Yi-Kan; Zhang, Fan; Tang, Ling-Long; Chen, Lei; Zhou, Guan-Qun; Zeng, Mu-Sheng; Kang, Tie-Bang; Jia, Wei-Hua; Shao, Jian-Yong; Mai, Hai-Qiang; Guo, Ying; Ma, Jun

    2015-01-01

    Background: Pregnancy-associated nasopharyngeal carcinoma (PANPC) has been associated with poor survival. Recent advances in radiation technology and imaging techniques, and the introduction of chemotherapy have improved survival in nasopharyngeal carcinoma (NPC); however, it is not clear whether these changes have improved survival in PANPC. Therefore, the purpose of this study was to compare five-year maternal survival in patients with PANPC and non-pregnant patients with NPC. Methods: After adjusting for age, stage and chemotherapy mode, we conducted a retrospective case-control study among 36 non-metastatic PANPC patients and 36 non-pregnant NPC patients (control group) who were treated at our institution between 2000 and 2010. Results: The median age of both groups was 30 years (range, 23–35 years); median follow-up for all patients was 70 months. Locoregionally-advanced disease accounted for 83.3% of all patients with PANPC and 92.9% of patients who developed NPC during pregnancy. In both the PANPC and control groups, 31 patients (86.1%) received chemotherapy and all patients received definitive radiotherapy. The five-year rates for overall survival (70% vs. 78%, p = 0.72), distant metastasis-free survival (79% vs. 76%, p = 0.77), loco-regional relapse-free survival (97% vs. 91%, p = 0.69) and disease-free survival (69% vs. 74%, p = 0.98) were not significantly different between the PANPC and control groups. Multivariate analysis using a Cox proportional hazards model revealed that only N-classification was significantly associated with five-year OS. Conclusion: This study demonstrates that, in the modern treatment era, pregnancy itself may not negatively influence survival outcomes in patients with NPC; however, pregnancy may delay the diagnosis of NPC

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

    Science.gov (United States)

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

    2018-04-01

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

  8. A retrospective analysis of survival and prognostic factors after stereotactic radiosurgery for aggressive meningiomas

    International Nuclear Information System (INIS)

    Ferraro, Daniel J; Zoberi, Imran; Simpson, Joseph R; Jaboin, Jerry J; Funk, Ryan K; Blackett, John William; Ju, Michelle R; DeWees, Todd A; Chicoine, Michael R; Dowling, Joshua L; Rich, Keith M; Drzymala, Robert E

    2014-01-01

    While most meningiomas are benign, aggressive meningiomas are associated with high levels of recurrence and mortality. A single institution’s Gamma Knife radiosurgical experience with atypical and malignant meningiomas is presented, stratified by the most recent WHO classification. Thirty-one patients with atypical and 4 patients with malignant meningiomas treated with Gamma Knife radiosurgery between July 2000 and July 2011 were retrospectively reviewed. All patients underwent prior surgical resection. Overall survival was the primary endpoint and rate of disease recurrence in the brain was a secondary endpoint. Patients who had previous radiotherapy or prior surgical resection were included. Kaplan-Meier and Cox proportional hazards models were used to estimate survival and identify factors predictive of recurrence and survival. Post-Gamma Knife recurrence was identified in 11 patients (31.4%) with a median overall survival of 36 months and progression-free survival of 25.8 months. Nine patients (25.7%) had died. Three-year overall survival (OS) and progression-free survival (PFS) rates were 78.0% and 65.0%, respectively. WHO grade II 3-year OS and PFS were 83.4% and 70.1%, while WHO grade III 3-year OS and PFS were 33.3% and 0%. Recurrence rate was significantly higher in patients with a prior history of benign meningioma, nuclear atypia, high mitotic rate, spontaneous necrosis, and WHO grade III diagnosis on univariate analysis; only WHO grade III diagnosis was significant on multivariate analysis. Overall survival was adversely affected in patients with WHO grade III diagnosis, prior history of benign meningioma, prior fractionated radiotherapy, larger tumor volume, and higher isocenter number on univariate analysis; WHO grade III diagnosis and larger treated tumor volume were significant on multivariate analysis. Atypical and anaplastic meningiomas remain difficult tumors to treat. WHO grade III diagnosis and treated tumor volume were significantly

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

    LENUS (Irish Health Repository)

    Haase, Trutz

    2016-02-29

    A wide-ranging debate has taken place in recent years on mediation analysis and causal modelling, raising profound theoretical, philosophical and methodological questions. The authors build on the results of these discussions to work towards an integrated approach to the analysis of research questions that situate survival outcomes in relation to complex causal pathways with multiple mediators. The background to this contribution is the increasingly urgent need for policy-relevant research on the nature of inequalities in health and healthcare.

  10. Connecting single-stock assessment models through correlated survival

    DEFF Research Database (Denmark)

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

    2017-01-01

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

  11. Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data

    KAUST Repository

    Tekwe, C. D.; Carroll, R. J.; Dabney, A. R.

    2012-01-01

    positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon

  12. Support vector methods for survival analysis: a comparison between ranking and regression approaches.

    Science.gov (United States)

    Van Belle, Vanya; Pelckmans, Kristiaan; Van Huffel, Sabine; Suykens, Johan A K

    2011-10-01

    To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods

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

    Science.gov (United States)

    Gong, Qi; Schaubel, Douglas E

    2017-03-01

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

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

    Science.gov (United States)

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

    2017-11-17

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

  15. Volumetric and MGMT parameters in glioblastoma patients: Survival analysis

    International Nuclear Information System (INIS)

    Iliadis, Georgios; Kotoula, Vassiliki; Chatzisotiriou, Athanasios; Televantou, Despina; Eleftheraki, Anastasia G; Lambaki, Sofia; Misailidou, Despina; Selviaridis, Panagiotis; Fountzilas, George

    2012-01-01

    In this study several tumor-related volumes were assessed by means of a computer-based application and a survival analysis was conducted to evaluate the prognostic significance of pre- and postoperative volumetric data in patients harboring glioblastomas. In addition, MGMT (O 6 -methylguanine methyltransferase) related parameters were compared with those of volumetry in order to observe possible relevance of this molecule in tumor development. We prospectively analyzed 65 patients suffering from glioblastoma (GBM) who underwent radiotherapy with concomitant adjuvant temozolomide. For the purpose of volumetry T1 and T2-weighted magnetic resonance (MR) sequences were used, acquired both pre- and postoperatively (pre-radiochemotherapy). The volumes measured on preoperative MR images were necrosis, enhancing tumor and edema (including the tumor) and on postoperative ones, net-enhancing tumor. Age, sex, performance status (PS) and type of operation were also included in the multivariate analysis. MGMT was assessed for promoter methylation with Multiplex Ligation-dependent Probe Amplification (MLPA), for RNA expression with real time PCR, and for protein expression with immunohistochemistry in a total of 44 cases with available histologic material. In the multivariate analysis a negative impact was shown for pre-radiochemotherapy net-enhancing tumor on the overall survival (OS) (p = 0.023) and for preoperative necrosis on progression-free survival (PFS) (p = 0.030). Furthermore, the multivariate analysis confirmed the importance of PS in PFS and OS of patients. MGMT promoter methylation was observed in 13/23 (43.5%) evaluable tumors; complete methylation was observed in 3/13 methylated tumors only. High rate of MGMT protein positivity (> 20% positive neoplastic nuclei) was inversely associated with pre-operative tumor necrosis (p = 0.021). Our findings implicate that volumetric parameters may have a significant role in the prognosis of GBM patients. Furthermore

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

    Science.gov (United States)

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

    2016-02-29

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

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

    Directory of Open Access Journals (Sweden)

    Jonathan Pratschke

    2016-02-01

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

  18. 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...

  19. Survival Rate and Associated Factors of Childhood Leukemia in Iran: A Systematic Review and Meta Analysis

    Directory of Open Access Journals (Sweden)

    Yousef Veisani

    2017-02-01

    Full Text Available Context Resent reviews have shown that about 18% of all child cancers are leukemia. Track of the survival rate can help researchers improve quality of life of patients through improving screening or discovery of better treatments. Objectives This review aimed at estimating the 5-year survival rates and associated factors of childhood leukemia in Iran. Data Sources We carried out a systematic review through search of relevant studies published in English (PubMed, Scopus, Google scholar, and ISI and Persian databases (Magiran, Medlib, SID, and Iran Medex. Study Selection The study included all epidemiologic studies that estimated survival rate in children with leukemia in Iran during years 2002 to 2015, and a standardized manner was used for extraction of information. Data Extraction The entire text or summary of all searched articles was extracted and then, related articles were selected, and irrelevant ones were excluded. Fixed and random effects models were calculated by the STATA using standard meta-analysis methods. Heterogeneity was assessed by I² statistics. Results The overall 5-year survival rate in patients with childhood leukemia in Iran was 0.65 (95% CI, 0.62 to 0.67, 10 studies, in the acute lymphoblastic leukemia (ALL subtype was 71.0% (95% CI: 68.0 to 74.0, and in the acute myeloid leukemia (AML subtype was 46.0%. Results of the meta analysis showed significant poor survival with relapse (heart rate (HR 1.59, 95% confidence interval (CI 1.27 to 1.98 and white blood count (WBC counts ≥ 50,000 (HR 2.92, 95% CI 1.23 to 4.60. Conclusions The results showed that 5-year survival rates in patients with AML were lower than patients with ALL. The results of this meta analysis strongly support the need for future research, action, and guidance for clinicians to improve health-related quality of life and outcomes for children with leukemia.

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2016-12-01

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

  2. Models for cell survival with low LET radiation

    International Nuclear Information System (INIS)

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

    1975-01-01

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

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

    KAUST Repository

    Wong, Yee-Chin

    2018-05-29

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

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

    KAUST Repository

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

    2018-01-01

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

  5. Marital status independently predicts testis cancer survival--an analysis of the SEER database.

    Science.gov (United States)

    Abern, Michael R; Dude, Annie M; Coogan, Christopher L

    2012-01-01

    Previous reports have shown that married men with malignancies have improved 10-year survival over unmarried men. We sought to investigate the effect of marital status on 10-year survival in a U.S. population-based cohort of men with testis cancer. We examined 30,789 cases of testis cancer reported to the Surveillance, Epidemiology, and End Results (SEER 17) database between 1973 and 2005. All staging were converted to the 1997 AJCC TNM system. Patients less than 18 years of age at time of diagnosis were excluded. A subgroup analysis of patients with stages I or II non-seminomatous germ cell tumors (NSGCT) was performed. Univariate analysis using t-tests and χ(2) tests compared characteristics of patients separated by marital status. Multivariate analysis was performed using a Cox proportional hazard model to generate Kaplan-Meier survival curves, with all-cause and cancer-specific mortality as the primary endpoints. 20,245 cases met the inclusion criteria. Married men were more likely to be older (38.9 vs. 31.4 years), Caucasian (94.4% vs. 92.1%), stage I (73.1% vs. 61.4%), and have seminoma as the tumor histology (57.3% vs. 43.4%). On multivariate analysis, married status (HR 0.58, P married status (HR 0.60, P married and unmarried men (44.8% vs. 43.4%, P = 0.33). Marital status is an independent predictor of improved overall and cancer-specific survival in men with testis cancer. In men with stages I or II NSGCT, RPLND is an additional predictor of improved overall survival. Marital status does not appear to influence whether men undergo RPLND. Copyright © 2012 Elsevier Inc. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Eloranta Sandra

    2012-06-01

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

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

    Science.gov (United States)

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

    2009-11-01

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

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

    Science.gov (United States)

    Karimi, Asrin; Delpisheh, Ali; Sayehmiri, Kourosh

    2016-01-01

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

  9. 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

  10. Estimating Probability of Default on Peer to Peer Market – Survival Analysis Approach

    Directory of Open Access Journals (Sweden)

    Đurović Andrija

    2017-05-01

    Full Text Available Arguably a cornerstone of credit risk modelling is the probability of default. This article aims is to search for the evidence of relationship between loan characteristics and probability of default on peer-to-peer (P2P market. In line with that, two loan characteristics are analysed: 1 loan term length and 2 loan purpose. The analysis is conducted using survival analysis approach within the vintage framework. Firstly, 12 months probability of default through the cycle is used to compare riskiness of analysed loan characteristics. Secondly, log-rank test is employed in order to compare complete survival period of cohorts. Findings of the paper suggest that there is clear evidence of relationship between analysed loan characteristics and probability of default. Longer term loans are more risky than the shorter term ones and the least risky loans are those used for credit card payoff.

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

    OpenAIRE

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

    2005-01-01

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

  12. Survival after radiotherapy in gastric cancer: Systematic review and meta-analysis

    International Nuclear Information System (INIS)

    Valentini, Vincenzo; Cellini, Francesco; Minsky, Bruce D.; Mattiucci, Gian Carlo; Balducci, Mario; D'Agostino, Giuseppe; D'Angelo, Elisa; Dinapoli, Nicola; Nicolotti, Nicola; Valentini, Chiara; La Torre, Giuseppe

    2009-01-01

    Background and purpose: A systematic review and meta-analysis was performed to assess the impact of radiotherapy on both 3- and 5-year survival in patients with resectable gastric cancer. Methods: Randomized Clinical Trials (RCTs) in which radiotherapy, (preoperative, postoperative and/or intraoperative), was compared with surgery alone or surgery plus chemotherapy in resectable gastric cancer were identified by searching web-based databases and supplemented by manual examination of reference lists. Meta-analysis was performed using Risk Ratios (RRs). Random or fixed effects models were used to combine data. The methodological quality was evaluated by Chalmers' score. Results: Radiotherapy had a significant impact on 5-year survival. Using an intent to treat (ITT) and a Per Protocol (PP) analysis, the overall 5-year RR was 1.26 (95% CI: 1.08-1.48; NNT = 17) and 1.31 (95% CI: 1.04-1.66; NNT = 13), respectively. Although the quality of the studies was variable, the data were consistent and no clear publication bias was found. Conclusion: This meta-analysis showed a statistically significant 5-year survival benefit with the addition of radiotherapy in patients with resectable gastric cancer. Radiotherapy remains a standard component in the treatment of resectable gastric cancer and new RCTs need to address the impact of new conformal radiotherapy technologies.

  13. Association between obesity with disease-free survival and overall survival in triple-negative breast cancer: A meta-analysis.

    Science.gov (United States)

    Mei, Lin; He, Lin; Song, Yuhua; Lv, Yang; Zhang, Lijiu; Hao, Fengxi; Xu, Mengmeng

    2018-05-01

    To investigate the relationship between obesity and disease-free survival (DFS) and overall survival (OS) of triple-negative breast cancer. Citations were searched in PubMed, Cochrane Library, and Web of Science. Random effect model meta-analysis was conducted by using Revman software version 5.0, and publication bias was evaluated by creating Egger regression with STATA software version 12. Nine studies (4412 patients) were included for DFS meta-analysis, 8 studies (4392 patients) include for OS meta-analysis. There were no statistical significances between obesity with DFS (P = .60) and OS (P = .71) in triple-negative breast cancer (TNBC) patients. Obesity has no impact on DFS and OS in patients with TNBC.

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

    Science.gov (United States)

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

    2015-06-01

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

  15. Survival Analysis of US Air Force Officer Retention Rate

    Science.gov (United States)

    2017-03-23

    an independent global business research organization] has studied the timing of unemployment… the timing of this variable is designated as...retrieval, and management; report writing and graphics design; statistical and mathematical analysis; business forecasting and decision support; operations...less flexible to experimentation with the system’s variables and assumptions. Today , many researchers utilize simulation to model real world

  16. Relationships between mastitis and functional longevity in Danish Black and White dairy cattle estimated using survival analysis

    NARCIS (Netherlands)

    Neerhof, H.J.; Madsen, P.; Ducrucq, V.; Vollema, A.R.; Jensen, I.; Korsgaard, I.R.

    2000-01-01

    The relationship between mastitis and functional longevity was assessed with survival analysis on data of Danish Black and White dairy cows. Different methods of including the effect of mastitis treatment on the culling decision by a farmer in the model were compared. The model in which mastitis

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

    International Nuclear Information System (INIS)

    Volkov, M V; Ostrovsky, V N

    2004-01-01

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

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

    Science.gov (United States)

    Wang, Ming; Long, Qi

    2016-09-01

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

  19. Determinants of malignant pleural mesothelioma survival and burden of disease in France: a national cohort analysis.

    Science.gov (United States)

    Chouaid, Christos; Assié, Jean Baptiste; Andujar, Pascal; Blein, Cecile; Tournier, Charlène; Vainchtock, Alexandre; Scherpereel, Arnaud; Monnet, Isabelle; Pairon, Jean Claude

    2018-04-01

    This study was undertaken to determine the healthcare burden of malignant pleural mesothelioma (MPM) in France and to analyze its associations with socioeconomic deprivation, population density, and management outcomes. A national hospital database was used to extract incident MPM patients in years 2011 and 2012. Cox models were used to analyze 1- and 2-year survival according to sex, age, co-morbidities, management, population-density index, and social deprivation index. The analysis included 1,890 patients (76% men; age: 73.6 ± 10.0 years; 84% with significant co-morbidities; 57% living in urban zones; 53% in highly underprivileged areas). Only 1% underwent curative surgical procedure; 65% received at least one chemotherapy cycle, 72% of them with at least one pemetrexed and/or bevacizumab administration. One- and 2-year survival rates were 64% and 48%, respectively. Median survival was 14.9 (95% CI: 13.7-15.7) months. The mean cost per patient was 27,624 ± 17,263 euros (31% representing pemetrexed and bevacizumab costs). Multivariate analyses retained men, age >70 years, chronic renal failure, chronic respiratory failure, and never receiving pemetrexed as factors of poor prognosis. After adjusting the analysis to age, sex, and co-morbidities, living in rural/semi-rural area was associated with better 2-year survival (HR: 0.83 [95% CI: 0.73-0.94]; P < 0.01); social deprivation index was not significantly associated with survival. With approximately 1,000 new cases per year in France, MPMs represents a significant national health care burden. Co-morbidities, sex, age, and living place appear to be significant factors of prognosis. © 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

  20. Study of Hip Fracture Risk using Tree Structured Survival Analysis

    Directory of Open Access Journals (Sweden)

    Lu Y

    2003-01-01

    Full Text Available In dieser Studie wird das Hüftfraktur-Risiko bei postmenopausalen Frauen untersucht, indem die Frauen in verschiedene Subgruppen hinsichtlich dieses Risikos klassifiziert werden. Frauen in einer gemeinsamen Subgruppe haben ein ähnliches Risiko, hingegen in verschiedenen Subgruppen ein unterschiedliches Hüftfraktur-Risiko. Die Subgruppen wurden mittels der Tree Structured Survival Analysis (TSSA aus den Daten von 7.665 Frauen der SOF (Study of Osteoporosis Fracture ermittelt. Bei allen Studienteilnehmerinnen wurde die Knochenmineraldichte (BMD von Unterarm, Oberschenkelhals, Hüfte und Wirbelsäule gemessen. Die Zeit von der BMD-Messung bis zur Hüftfraktur wurde als Endpunkt notiert. Eine Stichprobe von 75% der Teilnehmerinnen wurde verwendet, um die prognostischen Subgruppen zu bilden (Trainings-Datensatz, während die anderen 25% als Bestätigung der Ergebnisse diente (Validierungs-Datensatz. Aufgrund des Trainings-Datensatzes konnten mittels TSSA 4 Subgruppen identifiziert werden, deren Hüftfraktur-Risiko bei einem Follow-up von im Mittel 6,5 Jahren bei 19%, 9%, 4% und 1% lag. Die Einteilung in die Subgruppen erfolgte aufgrund der Bewertung der BMD des Ward'schen Dreiecks sowie des Oberschenkelhalses und nach dem Alter. Diese Ergebnisse konnten mittels des Validierungs-Datensatzes reproduziert werden, was die Sinnhaftigkeit der Klassifizierungregeln in einem klinischen Setting bestätigte. Mittels TSSA war eine sinnvolle, aussagekräftige und reproduzierbare Identifikation von prognostischen Subgruppen, die auf dem Alter und den BMD-Werten beruhen, möglich. In this paper we studied the risk of hip fracture for post-menopausal women by classifying women into different subgroups based on their risk of hip fracture. The subgroups were generated such that all the women in a particular subgroup had relatively similar risk while women belonging to two different subgroups had rather different risks of hip fracture. We used the Tree Structured

  1. Exposure, hazard, and survival analysis of diffusion on social networks.

    Science.gov (United States)

    Wu, Jiacheng; Crawford, Forrest W; Kim, David A; Stafford, Derek; Christakis, Nicholas A

    2018-04-29

    Sociologists, economists, epidemiologists, and others recognize the importance of social networks in the diffusion of ideas and behaviors through human societies. To measure the flow of information on real-world networks, researchers often conduct comprehensive sociometric mapping of social links between individuals and then follow the spread of an "innovation" from reports of adoption or change in behavior over time. The innovation is introduced to a small number of individuals who may also be encouraged to spread it to their network contacts. In conjunction with the known social network, the pattern of adoptions gives researchers insight into the spread of the innovation in the population and factors associated with successful diffusion. Researchers have used widely varying statistical tools to estimate these quantities, and there is disagreement about how to analyze diffusion on fully observed networks. Here, we describe a framework for measuring features of diffusion processes on social networks using the epidemiological concepts of exposure and competing risks. Given a realization of a diffusion process on a fully observed network, we show that classical survival regression models can be adapted to estimate the rate of diffusion, and actor/edge attributes associated with successful transmission or adoption, while accounting for the topology of the social network. We illustrate these tools by applying them to a randomized network intervention trial conducted in Honduras to estimate the rate of adoption of 2 health-related interventions-multivitamins and chlorine bleach for water purification-and determine factors associated with successful social transmission. Copyright © 2018 John Wiley & Sons, Ltd.

  2. Young patients with colorectal cancer have poor survival in the first twenty months after operation and predictable survival in the medium and long-term: Analysis of survival and prognostic markers

    Directory of Open Access Journals (Sweden)

    Wickramarachchi RE

    2010-09-01

    Full Text Available Abstract Objectives This study compares clinico-pathological features in young (50 years with colorectal cancer, survival in the young and the influence of pre-operative clinical and histological factors on survival. Materials and methods A twelve year prospective database of colorectal cancer was analysed. Fifty-three young patients were compared with forty seven consecutive older patients over fifty years old. An analysis of survival was undertaken in young patients using Kaplan Meier graphs, non parametric methods, Cox's Proportional Hazard Ratios and Weibull Hazard models. Results Young patients comprised 13.4 percent of 397 with colorectal cancer. Duration of symptoms and presentation in the young was similar to older patients (median, range; young patients; 6 months, 2 weeks to 2 years, older patients; 4 months, 4 weeks to 3 years, p > 0.05. In both groups, the majority presented without bowel obstruction (young - 81%, older - 94%. Cancer proximal to the splenic flexure was present more in young than in older patients. Synchronous cancers were found exclusively in the young. Mucinous tumours were seen in 16% of young and 4% of older patients (p Conclusion If patients, who are less than 40 years old with colorectal cancer, survive twenty months after operation, the prognosis improves and their survival becomes predictable.

  3. Models of Economic Analysis

    OpenAIRE

    Adrian Ioana; Tiberiu Socaciu

    2013-01-01

    The article presents specific aspects of management and models for economic analysis. Thus, we present the main types of economic analysis: statistical analysis, dynamic analysis, static analysis, mathematical analysis, psychological analysis. Also we present the main object of the analysis: the technological activity analysis of a company, the analysis of the production costs, the economic activity analysis of a company, the analysis of equipment, the analysis of labor productivity, the anal...

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

    Science.gov (United States)

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

    2007-11-01

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

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

    Science.gov (United States)

    Shek, L L; Godolphin, W

    1988-10-01

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

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

    Science.gov (United States)

    Faruk, Alfensi

    2018-03-01

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

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

    Science.gov (United States)

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

    2012-12-01

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

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

    International Nuclear Information System (INIS)

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

    1989-01-01

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

  9. Bayesian Analysis for EMP Survival Probability of Solid State Relay

    International Nuclear Information System (INIS)

    Sun Beiyun; Zhou Hui; Cheng Xiangyue; Mao Congguang

    2009-01-01

    The principle to estimate the parameter p of binomial distribution by Bayesian method and the several non-informative prior are introduced. The survival probability of DC solid state relay under current injection at certain amplitude is obtained by this method. (authors)

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

    Science.gov (United States)

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

    2010-07-01

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

  11. Survival analysis approach to account for non-exponential decay rate effects in lifetime experiments

    Energy Technology Data Exchange (ETDEWEB)

    Coakley, K.J., E-mail: kevincoakley@nist.gov [National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305 (United States); Dewey, M.S.; Huber, M.G. [National Institute of Standards and Technology, 100 Bureau Drive, Stop 8461, Gaithersburg, MD 20899 (United States); Huffer, C.R.; Huffman, P.R. [North Carolina State University, 2401 Stinson Drive, Box 8202, Raleigh, NC 27695 (United States); Triangle Universities Nuclear Laboratory, 116 Science Drive, Box 90308, Durham, NC 27708 (United States); Marley, D.E. [National Institute of Standards and Technology, 100 Bureau Drive, Stop 8461, Gaithersburg, MD 20899 (United States); North Carolina State University, 2401 Stinson Drive, Box 8202, Raleigh, NC 27695 (United States); Mumm, H.P. [National Institute of Standards and Technology, 100 Bureau Drive, Stop 8461, Gaithersburg, MD 20899 (United States); O' Shaughnessy, C.M. [University of North Carolina at Chapel Hill, 120 E. Cameron Ave., CB #3255, Chapel Hill, NC 27599 (United States); Triangle Universities Nuclear Laboratory, 116 Science Drive, Box 90308, Durham, NC 27708 (United States); Schelhammer, K.W. [North Carolina State University, 2401 Stinson Drive, Box 8202, Raleigh, NC 27695 (United States); Triangle Universities Nuclear Laboratory, 116 Science Drive, Box 90308, Durham, NC 27708 (United States); Thompson, A.K.; Yue, A.T. [National Institute of Standards and Technology, 100 Bureau Drive, Stop 8461, Gaithersburg, MD 20899 (United States)

    2016-03-21

    In experiments that measure the lifetime of trapped particles, in addition to loss mechanisms with exponential survival probability functions, particles can be lost by mechanisms with non-exponential survival probability functions. Failure to account for such loss mechanisms produces systematic measurement error and associated systematic uncertainties in these measurements. In this work, we develop a general competing risks survival analysis method to account for the joint effect of loss mechanisms with either exponential or non-exponential survival probability functions, and a method to quantify the size of systematic effects and associated uncertainties for lifetime estimates. As a case study, we apply our survival analysis formalism and method to the Ultra Cold Neutron lifetime experiment at NIST. In this experiment, neutrons can escape a magnetic trap before they decay due to a wall loss mechanism with an associated non-exponential survival probability function.

  12. Survival analysis approach to account for non-exponential decay rate effects in lifetime experiments

    International Nuclear Information System (INIS)

    Coakley, K.J.; Dewey, M.S.; Huber, M.G.; Huffer, C.R.; Huffman, P.R.; Marley, D.E.; Mumm, H.P.; O'Shaughnessy, C.M.; Schelhammer, K.W.; Thompson, A.K.; Yue, A.T.

    2016-01-01

    In experiments that measure the lifetime of trapped particles, in addition to loss mechanisms with exponential survival probability functions, particles can be lost by mechanisms with non-exponential survival probability functions. Failure to account for such loss mechanisms produces systematic measurement error and associated systematic uncertainties in these measurements. In this work, we develop a general competing risks survival analysis method to account for the joint effect of loss mechanisms with either exponential or non-exponential survival probability functions, and a method to quantify the size of systematic effects and associated uncertainties for lifetime estimates. As a case study, we apply our survival analysis formalism and method to the Ultra Cold Neutron lifetime experiment at NIST. In this experiment, neutrons can escape a magnetic trap before they decay due to a wall loss mechanism with an associated non-exponential survival probability function.

  13. Meta-regression analysis of commensal and pathogenic Escherichia coli survival in soil and water.

    Science.gov (United States)

    Franz, Eelco; Schijven, Jack; de Roda Husman, Ana Maria; Blaak, Hetty

    2014-06-17

    The extent to which pathogenic and commensal E. coli (respectively PEC and CEC) can survive, and which factors predominantly determine the rate of decline, are crucial issues from a public health point of view. The goal of this study was to provide a quantitative summary of the variability in E. coli survival in soil and water over a broad range of individual studies and to identify the most important sources of variability. To that end, a meta-regression analysis on available literature data was conducted. The considerable variation in reported decline rates indicated that the persistence of E. coli is not easily predictable. The meta-analysis demonstrated that for soil and water, the type of experiment (laboratory or field), the matrix subtype (type of water and soil), and temperature were the main factors included in the regression analysis. A higher average decline rate in soil of PEC compared with CEC was observed. The regression models explained at best 57% of the variation in decline rate in soil and 41% of the variation in decline rate in water. This indicates that additional factors, not included in the current meta-regression analysis, are of importance but rarely reported. More complete reporting of experimental conditions may allow future inference on the global effects of these variables on the decline rate of E. coli.

  14. Surviving the present: Modeling tools for organizational change

    International Nuclear Information System (INIS)

    Pangaro, P.

    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

  15. Talent in Female Gymnastics: a Survival Analysis Based upon Performance Characteristics.

    Science.gov (United States)

    Pion, J; Lenoir, M; Vandorpe, B; Segers, V

    2015-11-01

    This study investigated the link between the anthropometric, physical and motor characteristics assessed during talent identification and dropout in young female gymnasts. 3 cohorts of female gymnasts (n=243; 6-9 years) completed a test battery for talent identification. Performance-levels were monitored over 5 years of competition. Kaplan-Meier and Cox Proportional Hazards analyses were conducted to determine the survival rate and the characteristics that influence dropout respectively. Kaplan-Meier analysis indicated that only 18% of the female gymnasts that passed the baseline talent identification test survived at the highest competition level 5 years later. The Cox Proportional Hazards Model indicated that gymnasts with a score in the best quartile for a specific characteristic significantly increased chances of survival by 45-129%. These characteristics being: basic motor skills (129%), shoulder strength (96%), leg strength (53%) and 3 gross motor coordination items (45-73%). These results suggest that tests batteries commonly used for talent identification in young female gymnasts may also provide valuable insights into future dropout. Therefore, multidimensional test batteries deserve a prominent place in the selection process. The individual test results should encourage trainers to invest in an early development of basic physical and motor characteristics to prevent attrition. © Georg Thieme Verlag KG Stuttgart · New York.

  16. 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.

  17. Survival Analysis of Factors Influencing Cyclic Fatigue of Nickel-Titanium Endodontic Instruments

    Directory of Open Access Journals (Sweden)

    Eva Fišerová

    2015-01-01

    Full Text Available Objective. The aim of this study was to validate a survival analysis assessing the effect of type of rotary system, canal curvature, and instrument size on cyclic resistance. Materials and Methods. Cyclic fatigue testing was carried out in stainless steel artificial canals with radii of curvature of 3 or 5 mm and the angle of curvature of 60 degrees. All the instruments were new and 25 mm in working length, and ISO colour coding indicated the instrument size (yellow for size 20; red for size 25. Wizard Navigator instruments, Mtwo instruments, ProTaper instruments, and Revo-S instruments were passively rotated at 250 rotations per minute, and the time fracture was being recorded. Subsequently, fractographic analysis of broken tips was performed by scanning electron microscope. The data were then analysed by the Kaplan-Meier estimator of the survival function, the Cox proportional hazards model, the Wald test for regression covariates, and the Wald test for significance of regression model. Conclusion. The lifespan registered for the tested instruments was Mtwo > Wizard Navigator > Revo-S > ProTaper; 5 mm radius > 3 mm radius; and yellow > red in ISO colour coding system.

  18. Estimation of failure criteria in multivariate sensory shelf life testing using survival analysis.

    Science.gov (United States)

    Giménez, Ana; Gagliardi, Andrés; Ares, Gastón

    2017-09-01

    For most food products, shelf life is determined by changes in their sensory characteristics. A predetermined increase or decrease in the intensity of a sensory characteristic has frequently been used to signal that a product has reached the end of its shelf life. Considering all attributes change simultaneously, the concept of multivariate shelf life allows a single measurement of deterioration that takes into account all these sensory changes at a certain storage time. The aim of the present work was to apply survival analysis to estimate failure criteria in multivariate sensory shelf life testing using two case studies, hamburger buns and orange juice, by modelling the relationship between consumers' rejection of the product and the deterioration index estimated using PCA. In both studies, a panel of 13 trained assessors evaluated the samples using descriptive analysis whereas a panel of 100 consumers answered a "yes" or "no" question regarding intention to buy or consume the product. PC1 explained the great majority of the variance, indicating all sensory characteristics evolved similarly with storage time. Thus, PC1 could be regarded as index of sensory deterioration and a single failure criterion could be estimated through survival analysis for 25 and 50% consumers' rejection. The proposed approach based on multivariate shelf life testing may increase the accuracy of shelf life estimations. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2018-05-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-07-01

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

  1. Kaplan-Meier Survival Analysis Overestimates the Risk of Revision Arthroplasty: A Meta-analysis.

    Science.gov (United States)

    Lacny, Sarah; Wilson, Todd; Clement, Fiona; Roberts, Derek J; Faris, Peter D; Ghali, William A; Marshall, Deborah A

    2015-11-01

    Although Kaplan-Meier survival analysis is commonly used to estimate the cumulative incidence of revision after joint arthroplasty, it theoretically overestimates the risk of revision in the presence of competing risks (such as death). Because the magnitude of overestimation is not well documented, the potential associated impact on clinical and policy decision-making remains unknown. We performed a meta-analysis to answer the following questions: (1) To what extent does the Kaplan-Meier method overestimate the cumulative incidence of revision after joint replacement compared with alternative competing-risks methods? (2) Is the extent of overestimation influenced by followup time or rate of competing risks? We searched Ovid MEDLINE, EMBASE, BIOSIS Previews, and Web of Science (1946, 1980, 1980, and 1899, respectively, to October 26, 2013) and included article bibliographies for studies comparing estimated cumulative incidence of revision after hip or knee arthroplasty obtained using both Kaplan-Meier and competing-risks methods. We excluded conference abstracts, unpublished studies, or studies using simulated data sets. Two reviewers independently extracted data and evaluated the quality of reporting of the included studies. Among 1160 abstracts identified, six studies were included in our meta-analysis. The principal reason for the steep attrition (1160 to six) was that the initial search was for studies in any clinical area that compared the cumulative incidence estimated using the Kaplan-Meier versus competing-risks methods for any event (not just the cumulative incidence of hip or knee revision); we did this to minimize the likelihood of missing any relevant studies. We calculated risk ratios (RRs) comparing the cumulative incidence estimated using the Kaplan-Meier method with the competing-risks method for each study and used DerSimonian and Laird random effects models to pool these RRs. Heterogeneity was explored using stratified meta-analyses and

  2. The ATLAS Analysis Model

    CERN Multimedia

    Amir Farbin

    The ATLAS Analysis Model is a continually developing vision of how to reconcile physics analysis requirements with the ATLAS offline software and computing model constraints. In the past year this vision has influenced the evolution of the ATLAS Event Data Model, the Athena software framework, and physics analysis tools. These developments, along with the October Analysis Model Workshop and the planning for CSC analyses have led to a rapid refinement of the ATLAS Analysis Model in the past few months. This article introduces some of the relevant issues and presents the current vision of the future ATLAS Analysis Model. Event Data Model The ATLAS Event Data Model (EDM) consists of several levels of details, each targeted for a specific set of tasks. For example the Event Summary Data (ESD) stores calorimeter cells and tracking system hits thereby permitting many calibration and alignment tasks, but will be only accessible at particular computing sites with potentially large latency. In contrast, the Analysis...

  3. Lamb survival analysis from birth to weaning in Iranian Kermani sheep.

    Science.gov (United States)

    Barazandeh, Arsalan; Moghbeli, Sadrollah Molaei; Vatankhah, Mahmood; Hossein-Zadeh, Navid Ghavi

    2012-04-01

    Survival records from 1,763 Kermani lambs born between 1996 and 2004 from 294 ewes and 81 rams were used to determine genetic and non-genetic factors affecting lamb survival. Traits included were lamb survival across five periods from birth to 7, 14, 56, 70, and 90 days of age. Traits were analyzed under Weibull proportional hazard sire models. Several binary analyses were also conducted using animal models. Statistical models included the fixed class effects of sex of lamb, month and year of birth, a covariate effect of birth weight, and random genetic effects of both sire (in survival analyses) and animal (in binary analyses). The average survival to 90 days of age was 94.8%. Hazard rates ranged from 1.00 (birth to 90 days of age) to 1.73 (birth to 7 days of age) between the two sexes indicating that male lambs were at higher risk of mortality than females (P lamb survival and lamb birth weight, suggesting that viability and birth weight could be considered simultaneously in the selection programs to obtain optimal birth weight in Kermani lambs. Estimates of heritabilities from survival analyses were medium and ranged from 0.23 to 0.29. In addition, heritability estimates obtained from binary analyses were low and varied from 0.04 to 0.09. The results of this study suggest that progress in survival traits could be possible through managerial strategies and genetic selection.

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

    Science.gov (United States)

    Astuti Thamrin, Sri; Taufik, Irfan

    2018-03-01

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

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

    Directory of Open Access Journals (Sweden)

    Payman Ahi

    2012-01-01

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

  6. Acute Myeloid Leukemia: analysis of epidemiological profile and survival rate.

    Science.gov (United States)

    de Lima, Mariana Cardoso; da Silva, Denise Bousfield; Freund, Ana Paula Ferreira; Dacoregio, Juliana Shmitz; Costa, Tatiana El Jaick Bonifácio; Costa, Imaruí; Faraco, Daniel; Silva, Maurício Laerte

    2016-01-01

    To describe the epidemiological profile and the survival rate of patients with acute myeloid leukemia (AML) in a state reference pediatric hospital. Clinical-epidemiological, observational, retrospective, descriptive study. The study included new cases of patients with AML, diagnosed between 2004 and 2012, younger than 15 years. Of the 51 patients studied, 84% were white; 45% were females and 55%, males. Regarding age, 8% were younger than 1 year, 47% were aged between 1 and 10 years, and 45% were older than 10 years. The main signs/symptoms were fever (41.1%), asthenia/lack of appetite (35.2%), and hemorrhagic manifestations (27.4%). The most affected extra-medullary site was the central nervous system (14%). In 47% of patients, the white blood cell (WBC) count was below 10,000/mm(3) at diagnosis. The minimal residual disease (MRD) was less than 0.1%, on the 15th day of treatment in 16% of the sample. Medullary relapse occurred in 14% of cases. When comparing the bone marrow MRD with the vital status, it was observed that 71.42% of the patients with type M3 AML were alive, as were 54.05% of those with non-M3 AML. The death rate was 43% and the main proximate cause was septic shock (63.6%). In this study, the majority of patients were male, white, and older than 1 year. Most patients with WBC count <10,000/mm(3) at diagnosis lived. Overall survival was higher in patients with MRD <0.1%. The prognosis was better in patients with AML-M3. Copyright © 2016 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

  7. Bayesian linear regression with skew-symmetric error distributions with applications to survival analysis

    KAUST Repository

    Rubio, Francisco J.

    2016-02-09

    We study Bayesian linear regression models with skew-symmetric scale mixtures of normal error distributions. These kinds of models can be used to capture departures from the usual assumption of normality of the errors in terms of heavy tails and asymmetry. We propose a general noninformative prior structure for these regression models and show that the corresponding posterior distribution is proper under mild conditions. We extend these propriety results to cases where the response variables are censored. The latter scenario is of interest in the context of accelerated failure time models, which are relevant in survival analysis. We present a simulation study that demonstrates good frequentist properties of the posterior credible intervals associated with the proposed priors. This study also sheds some light on the trade-off between increased model flexibility and the risk of over-fitting. We illustrate the performance of the proposed models with real data. Although we focus on models with univariate response variables, we also present some extensions to the multivariate case in the Supporting Information.

  8. Conditionally replicating adenovirus expressing TIMP2 increases survival in a mouse model of disseminated ovarian cancer.

    Directory of Open Access Journals (Sweden)

    Sherry W Yang

    Full Text Available Ovarian cancer remains difficult to treat mainly due to presentation of the disease at an advanced stage. Conditionally-replicating adenoviruses (CRAds are promising anti-cancer agents that selectively kill the tumor cells. The present study evaluated the efficacy of a novel CRAd (Ad5/3-CXCR4-TIMP2 containing the CXCR4 promoter for selective viral replication in cancer cells together with TIMP2 as a therapeutic transgene, targeting the matrix metalloproteases (MMPs in a murine orthotopic model of disseminated ovarian cancer. An orthotopic model of ovarian cancer was established in athymic nude mice by intraperitonal injection of the human ovarian cancer cell line, SKOV3-Luc, expressing luciferase. Upon confirmation of peritoneal dissemination of the cells by non-invasive imaging, mice were randomly divided into four treatment groups: PBS, Ad-ΔE1-TIMP2, Ad5/3-CXCR4, and Ad5/3-CXCR4-TIMP2. All mice were imaged weekly to monitor tumor growth and were sacrificed upon reaching any of the predefined endpoints, including high tumor burden and significant weight loss along with clinical evidence of pain and distress. Survival analysis was performed using the Log-rank test. The median survival for the PBS cohort was 33 days; for Ad-ΔE1-TIMP2, 39 days; for Ad5/3-CXCR4, 52.5 days; and for Ad5/3-CXCR4-TIMP2, 63 days. The TIMP2-armed CRAd delayed tumor growth and significantly increased survival when compared to the unarmed CRAd. This therapeutic effect was confirmed to be mediated through inhibition of MMP9. Results of the in vivo study support the translational potential of Ad5/3-CXCR4-TIMP2 for treatment of human patients with advanced ovarian cancer.

  9. Prognostic classification index in Iranian colorectal cancer patients: Survival tree analysis

    Directory of Open Access Journals (Sweden)

    Amal Saki Malehi

    2016-01-01

    Full Text Available Aims: The aim of this study was to determine the prognostic index for separating homogenous subgroups in colorectal cancer (CRC patients based on clinicopathological characteristics using survival tree analysis. Methods: The current study was conducted at the Research Center of Gastroenterology and Liver Disease, Shahid Beheshti Medical University in Tehran, between January 2004 and January 2009. A total of 739 patients who already have been diagnosed with CRC based on pathologic report were enrolled. The data included demographic and clinical-pathological characteristic of patients. Tree-structured survival analysis based on a recursive partitioning algorithm was implemented to evaluate prognostic factors. The probability curves were calculated according to the Kaplan-Meier method, and the hazard ratio was estimated as an interest effect size. Result: There were 526 males (71.2% of these patients. The mean survival time (from diagnosis time was 42.46± (3.4. Survival tree identified three variables as main prognostic factors and based on their four prognostic subgroups was constructed. The log-rank test showed good separation of survival curves. Patients with Stage I-IIIA and treated with surgery as the first treatment showed low risk (median = 34 months whereas patients with stage IIIB, IV, and more than 68 years have the worse survival outcome (median = 9.5 months. Conclusion: Constructing the prognostic classification index via survival tree can aid the researchers to assess interaction between clinical variables and determining the cumulative effect of these variables on survival outcome.

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

    Science.gov (United States)

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

    1993-04-01

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

  11. Fluid Survival Tool: A Model Checker for Hybrid Petri Nets

    NARCIS (Netherlands)

    Postema, Björn Frits; Remke, Anne Katharina Ingrid; Haverkort, Boudewijn R.H.M.; Ghasemieh, Hamed

    2014-01-01

    Recently, algorithms for model checking Stochastic Time Logic (STL) on Hybrid Petri nets with a single general one-shot transition (HPNG) have been introduced. This paper presents a tool for model checking HPNG models against STL formulas. A graphical user interface (GUI) not only helps to

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

    International Nuclear Information System (INIS)

    Lachet, Bernard.

    1975-01-01

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

  13. Comparing survival outcomes of gross total resection and subtotal resection with radiotherapy for craniopharyngioma: a meta-analysis.

    Science.gov (United States)

    Wang, Guoqing; Zhang, Xiaoyang; Feng, Mengzhao; Guo, Fuyou

    2018-06-01

    Recent studies suggest that subtotal resection (STR) followed by radiation therapy (RT) is an appealing alternative to gross total resection (GTR) for craniopharyngioma, but it remains controversial. We conducted a meta-analysis to determine whether GTR is superior to STR with RT for craniopharyngioma. A systematic search was performed for articles published until October 2017 in the PubMed, Embase, and Cochrane Central databases. The endpoints of interest are overall survival and progression-free survival. Pooled hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were calculated using a fixed or random-effects model. The data were analyzed using Review Manager 5.3 software. A total of 744 patients (seven cohort studies) were enrolled for analyses. There were no significant differences between the GTR and STR with RT groups when the authors compared the pooled HRs at the end of the follow-up period. Overall survival (pooled HR = 0.76, 95% CI: 0.46-1.25, P = 0.28) and progression-free survival (pooled HR = 1.52, 95% CI: 0.42-5.44, P = 0.52) were similar between the two groups. The current meta-analysis suggests that GTR and STR with RT have the similar survival outcomes for craniopharyngioma. Copyright © 2018 Elsevier Inc. All rights reserved.

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

    International Nuclear Information System (INIS)

    Kabir, Golam; Tesfamariam, Solomon; Sadiq, Rehan

    2015-01-01

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

  15. Multivariate Analysis of the Predictors of Survival for Patients with Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization: Focusing on Superselective Chemoembolization

    International Nuclear Information System (INIS)

    Ji, Suk Kyeong; Cho, Yun Ku; Ahn, Yong Sik; Kim, Mi Young; Park, Yoon Ok; Kim, Jae Kyun; Kim, Wan Tae

    2008-01-01

    While the prognostic factors of survival for patients with hepatocellular carcinoma (HCC) who underwent transarterial chemoembolization (TACE) are well known, the clinical significance of performing selective TACE for HCC patients has not been clearly documented. We tried to analyze the potential factors of disease-free survival for these patients, including the performance of selective TACE. A total of 151 patients with HCC who underwent TACE were retrospectively analyzed for their disease-free survival (a median follow- up of 23 months, range: 1-88 months). Univariate and multivariate analyses were performed for 20 potential factors by using the Cox proportional hazard model, including 19 baseline factors and one procedure-related factor (conventional versus selective TACE). The parameters that proved to be significant on the univariate analysis were subsequently tested with the multivariate model. Conventional or selective TACE was performed for 40 and 111 patients, respectively. Univariate and multivariate analyses revealed that tumor multiplicity, venous tumor thrombosis and selective TACE were the only three independent significant prognostic factors of disease-free survival (p = 0.002, 0.015 and 0.019, respectively). In our study, selective TACE was a favorable prognostic factor for the disease-free survival of patients with HCC who underwent TACE

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

    Science.gov (United States)

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

    2015-02-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

  18. Modeling the effects of binary mixtures on survival in time.

    NARCIS (Netherlands)

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

    2007-01-01

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

  19. Acute lymphoblastic leukemia in children and adolescents: prognostic factors and analysis of survival

    Science.gov (United States)

    Lustosa de Sousa, Daniel Willian; de Almeida Ferreira, Francisco Valdeci; Cavalcante Félix, Francisco Helder; de Oliveira Lopes, Marcos Vinicios

    2015-01-01

    Objective To describe the clinical and laboratory features of children and adolescents with acute lymphoblastic leukemia treated at three referral centers in Ceará and evaluate prognostic factors for survival, including age, gender, presenting white blood cell count, immunophenotype, DNA index and early response to treatment. Methods Seventy-six under 19-year-old patients with newly diagnosed acute lymphoblastic leukemia treated with the Grupo Brasileiro de Tratamento de Leucemia da Infância – acute lymphoblastic leukemia-93 and -99 protocols between September 2007 and December 2009 were analyzed. The diagnosis was based on cytological, immunophenotypic and cytogenetic criteria. Associations between variables, prognostic factors and response to treatment were analyzed using the chi-square test and Fisher's exact test. Overall and event-free survival were estimated by Kaplan–Meier analysis and compared using the log-rank test. A Cox proportional hazards model was used to identify independent prognostic factors. Results The average age at diagnosis was 6.3 ± 0.5 years and males were predominant (65%). The most frequently observed clinical features were hepatomegaly, splenomegaly and lymphadenopathy. Central nervous system involvement and mediastinal enlargement occurred in 6.6% and 11.8%, respectively. B-acute lymphoblastic leukemia was more common (89.5%) than T-acute lymphoblastic leukemia. A DNA index >1.16 was found in 19% of patients and was associated with favorable prognosis. On Day 8 of induction therapy, 95% of the patients had lymphoblast counts <1000/μL and white blood cell counts <5.0 × 109/L. The remission induction rate was 95%, the induction mortality rate was 2.6% and overall survival was 72%. Conclusion The prognostic factors identified are compatible with the literature. The 5-year overall and event-free survival rates were lower than those reported for developed countries. As shown by the multivariate analysis, age and baseline white

  20. Acute lymphoblastic leukemia in children and adolescents: prognostic factors and analysis of survival

    Directory of Open Access Journals (Sweden)

    Daniel Willian Lustosa de Sousa

    2015-08-01

    Full Text Available OBJECTIVE: To describe the clinical and laboratory features of children and adolescents with acute lymphoblastic leukemia treated at three referral centers in Ceará and evaluate prognostic factors for survival, including age, gender, presenting white blood cell count, immunophenotype, DNA index and early response to treatment.METHODS: Seventy-six under 19-year-old patients with newly diagnosed acute lymphoblastic leukemia treated with the Grupo Brasileiro de Tratamento de Leucemia da Infância - acute lymphoblastic leukemia-93 and -99 protocols between September 2007 and December 2009 were analyzed. The diagnosis was based on cytological, immunophenotypic and cytogenetic criteria. Associations between variables, prognostic factors and response to treatment were analyzed using the chi-square test and Fisher's exact test. Overall and event-free survival were estimated by Kaplan-Meier analysis and compared using the log-rank test. A Cox proportional hazards model was used to identify independent prognostic factors.RESULTS: The average age at diagnosis was 6.3 ± 0.5 years and males were predominant (65%. The most frequently observed clinical features were hepatomegaly, splenomegaly and lymphadenopathy. Central nervous system involvement and mediastinal enlargement occurred in 6.6% and 11.8%, respectively. B-acute lymphoblastic leukemia was more common (89.5% than T-acute lymphoblastic leukemia. A DNA index >1.16 was found in 19% of patients and was associated with favorable prognosis. On Day 8 of induction therapy, 95% of the patients had lymphoblast counts <1000/µL and white blood cell counts <5.0 Ã- 109/L. The remission induction rate was 95%, the induction mortality rate was 2.6% and overall survival was 72%.CONCLUSION: The prognostic factors identified are compatible with the literature. The 5-year overall and event-free survival rates were lower than those reported for developed countries. As shown by the multivariate analysis, age

  1. 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.

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

    Directory of Open Access Journals (Sweden)

    Justine B. Nasejje

    2017-07-01

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

  3. 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.

  4. 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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2001-10-01

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

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

    Science.gov (United States)

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

    2010-02-01

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

  7. 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.

  8. 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.

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

    Science.gov (United States)

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

    2009-06-01

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

  10. Analysis of the Survival of Children Under Five in Indonesia and Associated Factors

    Science.gov (United States)

    Nur Islami Warrohmah, Annisa; Maniar Berliana, Sarni; Nursalam, Nursalam; Efendi, Ferry; Haryanto, Joni; Has, Eka Misbahatul M.; Ulfiana, Elida; Dwi Wahyuni, Sylvia

    2018-02-01

    The under-five mortality rate (U5MR) remains a challenge for developing nations, including Indonesia. This study aims to assess the key factors associated with mortality of Indonesian infants using survival analysis. Data taken from 14,727 live-born infants (2007-2012) was examined from the nationally representative Indonesian Demographic Health Survey. The Weibull hazard model was performed to analyse the socioeconomic status and related determinants of infant mortality. The findings indicated that mother factors (education, working status, autonomy, economic status, maternal age at birth, birth interval, type of births, complications, history of previous mortality, breastfeeding, antenatal care and place of delivery); infant factors (birth size); residence; and environmental conditions were associated with the childhood mortality. Rural or urban residence was an important determining factor of infant mortality. For example, considering the factor of a mother’s education, rural educated mothers had a significant association with the survival of their infants. In contrast, there was no significant association between urban educated mothers and their infants’ mortality. The results showed obvious contextual differences which determine the childhood mortality. Socio-demographic and economic factors remain critical in determining the death of infants. This study provides evidence for designing targeted interventions, as well as suggesting specific needs based on the population’s place of residence, in the issue of U5MR. Further interventions should also consider other identified variables while developing programmes to address infant’s needs.

  11. A Multiscale Survival Process for Modeling Human Activity Patterns.

    Science.gov (United States)

    Zhang, Tianyang; Cui, Peng; Song, Chaoming; Zhu, Wenwu; Yang, Shiqiang

    2016-01-01

    Human activity plays a central role in understanding large-scale social dynamics. It is well documented that individual activity pattern follows bursty dynamics characterized by heavy-tailed interevent time distributions. Here we study a large-scale online chatting dataset consisting of 5,549,570 users, finding that individual activity pattern varies with timescales whereas existing models only approximate empirical observations within a limited timescale. We propose a novel approach that models the intensity rate of an individual triggering an activity. We demonstrate that the model precisely captures corresponding human dynamics across multiple timescales over five orders of magnitudes. Our model also allows extracting the population heterogeneity of activity patterns, characterized by a set of individual-specific ingredients. Integrating our approach with social interactions leads to a wide range of implications.

  12. Neuron-specific antioxidant OXR1 extends survival of a mouse model of amyotrophic lateral sclerosis.

    Science.gov (United States)

    Liu, Kevin X; Edwards, Benjamin; Lee, Sheena; Finelli, Mattéa J; Davies, Ben; Davies, Kay E; Oliver, Peter L

    2015-05-01

    Amyotrophic lateral sclerosis is a devastating neurodegenerative disorder characterized by the progressive loss of spinal motor neurons. While the aetiological mechanisms underlying the disease remain poorly understood, oxidative stress is a central component of amyotrophic lateral sclerosis and contributes to motor neuron injury. Recently, oxidation resistance 1 (OXR1) has emerged as a critical regulator of neuronal survival in response to oxidative stress, and is upregulated in the spinal cord of patients with amyotrophic lateral sclerosis. Here, we tested the hypothesis that OXR1 is a key neuroprotective factor during amyotrophic lateral sclerosis pathogenesis by crossing a new transgenic mouse line that overexpresses OXR1 in neurons with the SOD1(G93A) mouse model of amyotrophic lateral sclerosis. Interestingly, we report that overexpression of OXR1 significantly extends survival, improves motor deficits, and delays pathology in the spinal cord and in muscles of SOD1(G93A) mice. Furthermore, we find that overexpression of OXR1 in neurons significantly delays non-cell-autonomous neuroinflammatory response, classic complement system activation, and STAT3 activation through transcriptomic analysis of spinal cords of SOD1(G93A) mice. Taken together, these data identify OXR1 as the first neuron-specific antioxidant modulator of pathogenesis and disease progression in SOD1-mediated amyotrophic lateral sclerosis, and suggest that OXR1 may serve as a novel target for future therapeutic strategies. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.

  13. Risk factors for dental caries in childhood: a five-year survival analysis.

    Science.gov (United States)

    Lee, Hyo-Jin; Kim, Jin-Bom; Jin, Bo-Hyoung; Paik, Dai-Il; Bae, Kwang-Hak

    2015-04-01

    The purpose of this study was to examine the risk factors of dental caries at the level of an individual person with survival analysis of the prospective data for 5 years. A total of 249 first-grade students participated in a follow-up study for 5 years. All participants responded to a questionnaire inquiring about socio-demographic variables and oral health behaviors. They also received an oral examination and were tested for Dentocult SM and LB. Over 5 years, the participants received yearly oral follow-up examinations to determine the incidence of dental caries. The incidence of one or more dental caries (DC1) and four or more dental caries (DC4) were defined as one or more and four or more decayed, missing, and filled permanent teeth increments, respectively. Socio-demographic variables, oral health behaviors, and status and caries activity tests were assessed as risk factors for DC1 and DC4. The adjusted hazard ratios (HRs) of risk factors for DC1 and DC4 were calculated using Cox proportional hazard regression models. During the 5-year follow-up period, DC1 and DC4 occurred in 87 and 25 participants, respectively. In multivariate hazard models, five or more decayed, missing, and filled primary molar teeth [HR 1.93, 95% confidence interval (CI) 1.19-3.13], and Dentocult LB of two or three (HR 2.21, 95% CI 1.37-3.56) were independent risk factors of DC1. For DC4, only Dentocult LB of two or three was an independent risk factor (HR 2.95, 95% CI 1.11-7.79). Our results suggest that dental caries incidence at an individual level can be associated with the experience of dental caries in primary teeth and Dentocult LB based on the survival models for the 5-year prospective data. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  14. Sociocultural Factors of Survival of Males and Females in Economically Active Age: a Regional Analysis

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    Evgeniya Khasanovna Tukhtarova

    2018-03-01

    Full Text Available The period, when a person starts and completes his or her professional carrier and labour participation, in general, coincides with the age when the self-preservation behaviour develops. It is a time when a person aims for a healthy and safe lifestyle. During this period, an individual assumes the main standards, values of the self-preservation behaviour inherent in an ethnic, social and cultural macro-environment. To research the sociocultural factors of survival, we applied econometric modelling to demographic processes using the discrete and probabilistic indicators of the mortality tables of male and female in economically active age. The econometric model included the elements of spatiotemporal characteristics of territories. These characteristics are interrelated with the indicators of survival probability and the indicator of average life expectancy in the regions of Russia. We choose the major sociocultural factors by the correlation ratio of indicators and their sensitivity. The econometric analysis has revealed a high degree of sensitivity of a territorial variation of demographic and sociocultural factors in the regions of Russia, including a gender aspect. The most significant socio-economic factors, which determine the self-preservation behaviour of males, are the following: 1 the size of Gross Regional Product per capita; 2 quality of health infrastructure; 3 fixed investments; 4 population with monetary income under the subsistence minimum (share coefficient of income differentials. The female have the same hierarchy of socio-economic factors, except for the sensitivity of variables to the regional differentiation of signs. The household poverty factor has little significance for the women and it is the main difference between male and female. The built model has shown the predictive importance in the assessment of the above-mentioned factors in short and medium-term prospects.

  15. Introduction to SURPH.1 analysis of release-recapture data for survival studies

    International Nuclear Information System (INIS)

    Smith, S.G.; Skalski, J.R.; Schlechte, J.W.; Hoffmann, A.; Cassen, V.

    1994-12-01

    Program SURPH is the culmination of several years of research to develop a comprehensive computer program to analyze survival studies of fish and wildlife populations. Development of this software was motivated by the advent of the PIT-tag (Passive Integrated Transponder) technology that permits the detection of salmonid smolt as they pass through hydroelectric facilities on the Snake and Columbia Rivers in the Pacific Northwest. Repeated detections of individually tagged smolt and analysis of their capture-histories permits estimates of downriver survival probabilities. Eventual installation of detection facilities at adult fish ladders will also permit estimation of ocean survival and upstream survival of returning salmon using the statistical methods incorporated in SURPH.1. However, the utility of SURPH.1 far exceeds solely the analysis of salmonid tagging studies. Release-recapture and radiotelemetry studies from a wide range of terrestrial and aquatic species have been analyzed using SURPH.1 to estimate discrete time survival probabilities and investigate survival relationships. The interactive computing environment of SURPH.1 was specifically developed to allow researchers to investigate the relationship between survival and capture processes and environmental, experimental and individual-based covariates. Program SURPH.1 represents a significant advancement in the ability of ecologists to investigate the interplay between morphologic, genetic, environmental and anthropogenic factors on the survival of wild species. It is hoped that this better understanding of risk factors affecting survival will lead to greater appreciation of the intricacies of nature and to improvements in the management of wild resources. This technical report is an introduction to SURPH.1 and provides a user guide for both the UNIX and MS-Windows reg-sign applications of the SURPH software

  16. Survival after Second and Subsequent Recurrences in Osteosarcoma: A Retrospective Multicenter Analysis.

    Science.gov (United States)

    Tirtei, Elisa; Asaftei, Sebastian D; Manicone, Rosaria; Cesari, Marilena; Paioli, Anna; Rocca, Michele; Ferrari, Stefano; Fagioli, Franca

    2017-05-01

    Purpose Osteosarcoma (OS) is the most common primary bone tumor. Despite complete surgical removal and intensive chemotherapeutic treatment, 30%-35% of patients with OS have local or systemic recurrence. Some patients survive multiple recurrences, but overall survival after OS recurrence is poor. This analysis aims to describe and identify factors influencing post-relapse survival (PRS) after a second OS relapse. Methods This is a retrospective analysis of 60 patients with a second relapse of OS of the extremities in 2 Italian centers between 2003 and 2013. Results Treatment for first and subsequent relapses was planned according to institutional guidelines. After complete surgical remission (CSR) following the first recurrence, patients experienced a second OS relapse with a median disease-free interval (DFI) of 6 months. Lung disease was prevalent: 44 patients (76%) had pulmonary metastases. Survival after the second relapse was 22% at 5 years. Lung disease only correlated with better survival at 5 years (33.6%) compared with other sites of recurrence (5%; p = 0.008). Patients with a single pulmonary lesion had a better 5-year second PRS (42%; p = 0.02). Patients who achieved a second CSR had a 5-year second PRS of 33.4%. Chemotherapy (p<0.001) benefited patients without a third CSR. Conclusions This analysis confirms the importance of an aggressive, repeated surgical approach. Lung metastases only, the number of lesions, DFI and CSR influenced survival. It also confirms the importance of chemotherapy in patients in whom surgical treatment is not feasible.

  17. Mediation analysis of the relationship between institutional research activity and patient survival

    DEFF Research Database (Denmark)

    Rochon, Justine; du Bois, Andreas; Lange, Theis

    2014-01-01

    BACKGROUND: Recent studies have suggested that patients treated in research-active institutions have better outcomes than patients treated in research-inactive institutions. However, little attention has been paid to explaining such effects, probably because techniques for mediation analysis...... existing so far have not been applicable to survival data. METHODS: We investigated the underlying mechanisms using a recently developed method for mediation analysis of survival data. Our analysis of the effect of research activity on patient survival was based on 352 patients who had been diagnosed...... mediated through either optimal surgery or chemotherapy. Taken together, about 26% of the beneficial effect of research activity was mediated through the proposed pathways. CONCLUSIONS: Mediation analysis allows proceeding from the question "Does it work?" to the question "How does it work?" In particular...

  18. 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.

  19. 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.

  20. Breastfeeding practices in a public health field practice area in Sri Lanka: a survival analysis

    Directory of Open Access Journals (Sweden)

    Agampodi Thilini C

    2007-10-01

    Full Text Available Abstract Background Exclusive breastfeeding up to the completion of the sixth month of age is the national infant feeding recommendation for Sri Lanka. The objective of the present study was to collect data on exclusive breastfeeding up to six months and to describe the association between exclusive breastfeeding and selected socio-demographic factors. Methods A clinic based cross-sectional study was conducted in the Medical Officer of Health area, Beruwala, Sri Lanka in June 2006. Mothers with infants aged 4 to 12 months, attending the 19 child welfare clinics in the area were included in the study. Infants with specific feeding problems (cleft lip and palate and primary lactose intolerance were excluded. Cluster sampling technique was used and consecutive infants fulfilling the inclusion criteria were enrolled. A total of 219 mothers participated in the study. The statistical tests used were survival analysis (Kaplan-Meier survival curves and Cox proportional Hazard model. Results All 219 mothers had initiated breastfeeding. The median duration of exclusive breastfeeding was four months (95% CI 3.75, 4.25. The rates of exclusive breastfeeding at 4 and 6 months were 61.6% (135/219 and 15.5% (24/155 respectively. Bivariate analysis showed that the Muslim ethnicity (p = 0.004, lower levels of parental education (p Conclusion The rate of breastfeeding initiation and exclusive breastfeeding up to the fourth month is very high in Medical Officer of Health area, Beruwala, Sri Lanka. However exclusive breastfeeding up to six months is still low and the prevalence of inappropriate feeding practices is high.

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

    Science.gov (United States)

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

    1999-01-01

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

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

    Science.gov (United States)

    Nikbakht, Roya; Bahrampour, Abbas

    2017-01-01

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

  3. Survival Analysis and its Associated Factors of Beta Thalassemia Major in Hamadan Province

    Directory of Open Access Journals (Sweden)

    Reza Zamani

    2015-05-01

    Full Text Available Background: There currently is a lack of knowledge about the long-term survival of patients with beta thalassemia (BT, particularly in regions with low incidence of the disease. The aim of the present study was to determine the survival rate of the patients with BT major and the factors associated with the survival time. Methods: This retrospective cohort study was performed in Hamadan province, located in the west of Iran. The study included patients that referred to the provincial hospitals during 16 year period from 1997 to 2013. The follow up of each subject was calculated from the date of birth to the date of death. Demographic and clinical data were extracted from patients’ medical records using a checklist. Statistical analysis included the Kaplan-Meier method to analyze survivals, log-rank to compare curves between groups, and Cox regression for multivariate prognostic analysis. Results: A total of 133 patients with BT major were enrolled, 54.9% of whom were male and 66.2% were urban. The 10-, 20- and 30-year survival rate for all patients were 98.3%, 88.4% and 80.5%, respectively. Based on hazard ratio (HR, we found that accompanied diseases (P=0.01, blood type (P=0.03 and residency status (P=0.01 were significant predictors for the survival time of patients. Conclusion: The survival rate of BT patients has improved. Future researches such as prospective designs are required for the estimation of survival rate and to find other prognostic factors, which have reliable sources of data.

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

    Science.gov (United States)

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

    2018-02-01

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

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

    Science.gov (United States)

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

    2018-02-01

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

  6. Dropout during a driving simulator study: A survival analysis.

    Science.gov (United States)

    Matas, Nicole A; Nettelbeck, Ted; Burns, Nicholas R

    2015-12-01

    Simulator sickness is the occurrence of motion-sickness like symptoms that can occur during use of simulators and virtual reality technologies. This study investigated individual factors that contributed to simulator sickness and dropout while using a desktop driving simulator. Eighty-eight older adult drivers (mean age 72.82±5.42years) attempted a practice drive and two test drives. Participants also completed a battery of cognitive and visual assessments, provided information on their health and driving habits, and reported their experience of simulator sickness symptoms throughout the study. Fifty-two participants dropped out before completing the driving tasks. A time-dependent Cox Proportional Hazards model showed that female gender (HR=2.02), prior motion sickness history (HR=2.22), and Mini-SSQ score (HR=1.55) were associated with dropout. There were no differences between dropouts and completers on any of the cognitive abilities tests. Older adults are a high-risk group for simulator sickness. Within this group, female gender and prior motion sickness history are related to simulator dropout. Higher reported experience of symptoms of simulator sickness increased rates of dropout. The results highlight the importance of screening and monitoring of participants in driving simulation studies. Older adults, females, and those with a prior history of motion sickness may be especially at risk. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

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

    International Nuclear Information System (INIS)

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

    1990-01-01

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

  8. SURVIVAL ANALYSIS AND GROWTH OF Cordia trichotoma, BORAGINACEAE, LAMIALES, IN MATO GROSSO DO SUL STATE, BRAZIL

    Directory of Open Access Journals (Sweden)

    Sergio Luiz Salvadori

    2013-12-01

    Full Text Available http://dx.doi.org/10.5902/1980509812357The evaluation of a plant survival percentage and growth may reflect its competitive ability in plantcommunity. Cordia trichotoma is a common native tree in Mato Grosso do Sul State and one of the mostpromising for planting. This study monitored the survival percentage and growth of Cordia trichotomaunder different conditions such as weeding and receiving or not fertilization. The experiment started inSeptember 2008 and it was concluded in March 2010. The seeds collection and sowing were held in urbanarea of Mundo Novo Municipality and the area for permanent planting to measure seedlings survival andgrowth was set at Japorã Municipality, Fazenda Santa Clara. Seedlings were planted in two categories: theuse or not of fertilizer and crowing resulting in four distinct groups: block fertilizer bare earth (ATN, bareland block without fertilizer (BTN, fertilizer and crown block (AC and without fertilizer and crownedblock (BC. The results indicated high survival of Cordia trichotoma in the seedling transplant system from bed to bags. The BC block showed the highest percentage of survival, but the smaller increments in height.The AC, ATN and BTN blocks presented the same survival pattern and similar average growth. However,there may be differences in nutritional and chemical composition of the soil suggesting sector analysis forfuture studies.

  9. Re-analysis of survival data of cancer patients utilizing additive homeopathy.

    Science.gov (United States)

    Gleiss, Andreas; Frass, Michael; Gaertner, Katharina

    2016-08-01

    In this short communication we present a re-analysis of homeopathic patient data in comparison to control patient data from the same Outpatient´s Unit "Homeopathy in malignant diseases" of the Medical University of Vienna. In this analysis we took account of a probable immortal time bias. For patients suffering from advanced stages of cancer and surviving the first 6 or 12 months after diagnosis, respectively, the results show that utilizing homeopathy gives a statistically significant (p<0.001) advantage over control patients regarding survival time. In conclusion, bearing in mind all limitations, the results of this retrospective study suggest that patients with advanced stages of cancer might benefit from additional homeopathic treatment until a survival time of up to 12 months after diagnosis. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

    International Nuclear Information System (INIS)

    Sontag, W.

    1990-01-01

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

  11. A nonparametric approach to medical survival data: Uncertainty in the context of risk in mortality analysis

    International Nuclear Information System (INIS)

    Janurová, Kateřina; Briš, Radim

    2014-01-01

    Medical survival right-censored data of about 850 patients are evaluated to analyze the uncertainty related to the risk of mortality on one hand and compare two basic surgery techniques in the context of risk of mortality on the other hand. Colorectal data come from patients who underwent colectomy in the University Hospital of Ostrava. Two basic surgery operating techniques are used for the colectomy: either traditional (open) or minimally invasive (laparoscopic). Basic question arising at the colectomy operation is, which type of operation to choose to guarantee longer overall survival time. Two non-parametric approaches have been used to quantify probability of mortality with uncertainties. In fact, complement of the probability to one, i.e. survival function with corresponding confidence levels is calculated and evaluated. First approach considers standard nonparametric estimators resulting from both the Kaplan–Meier estimator of survival function in connection with Greenwood's formula and the Nelson–Aalen estimator of cumulative hazard function including confidence interval for survival function as well. The second innovative approach, represented by Nonparametric Predictive Inference (NPI), uses lower and upper probabilities for quantifying uncertainty and provides a model of predictive survival function instead of the population survival function. The traditional log-rank test on one hand and the nonparametric predictive comparison of two groups of lifetime data on the other hand have been compared to evaluate risk of mortality in the context of mentioned surgery techniques. The size of the difference between two groups of lifetime data has been considered and analyzed as well. Both nonparametric approaches led to the same conclusion, that the minimally invasive operating technique guarantees the patient significantly longer survival time in comparison with the traditional operating technique

  12. HIV testing in the maternity ward and the start of breastfeeding: a survival analysis

    Directory of Open Access Journals (Sweden)

    Glaucia T. Possolli

    2015-08-01

    Full Text Available OBJECTIVE: The purpose of this study was to analyze the factors that influence of the time between birth and the beginning of breastfeeding, especially at the moment of the rapid HIV test results at hospital admission for delivery.METHODS: Cohort study of 932 pregnant women who underwent rapid HIV test admitted in the hospital for delivery in Baby-Friendly Hospitals. The survival curves of time from birth to the first feeding were estimated by the Kaplan-Meier method and the joint effect of independent variables by the Cox model with a hierarchical analysis. As the survival curves were not homogeneous among the five hospitals, hindering the principle of proportionality of risks, the data were divided into two groups according to the median time of onset of breastfeeding at birth in women undergoing rapid HIV testing.RESULTS: Hospitals with median time to breastfeeding onset at birth of up to 60 min were considered as early breastfeeding onset and those with higher medians were considered as late breastfeeding onset at birth. Risk factors common to hospitals considered to be with early and late breastfeeding onset at birth were: cesarean section (RR = 1.75 [95% CI: 1.38-2.22]; RR = 3.83 [95% CI: 3.03-4.85] and rapid test result after birth (RR = 1.45 [95% CI: 1.12-1.89]; RR = 1.65 [95% CI: 1.35-2.02], respectively; and hospitals with late onset: starting prenatal care in the third trimester (RR = 1.86 [95% CI: 1.16-2.97].CONCLUSIONS: The onset of breastfeeding is postponed, even in Baby-Friendly Hospitals, when the results of the rapid HIV test requested in the maternity are not available at the time of delivery.

  13. Improved Survival With Radiation Therapy in High-Grade Soft Tissue Sarcomas of the Extremities: A SEER Analysis

    International Nuclear Information System (INIS)

    Koshy, Matthew; Rich, Shayna E.; Mohiuddin, Majid M.

    2010-01-01

    Purpose: The benefit of radiation therapy in extremity soft tissue sarcomas remains controversial. The purpose of this study was to determine the effect of radiation therapy on overall survival among patients with primary soft tissue sarcomas of the extremity who underwent limb-sparing surgery. Methods and Materials: A retrospective study from the Surveillance, Epidemiology, and End Results (SEER) database that included data from January 1, 1988, to December 31, 2005. A total of 6,960 patients constituted the study population. Overall survival curves were constructed using the Kaplan-Meir method and for patients with low- and high-grade tumors. Hazard ratios were calculated based on multivariable Cox proportional hazards models. Results: Of the cohort, 47% received radiation therapy. There was no significant difference in overall survival among patients with low-grade tumors by radiation therapy. In high-grade tumors, the 3-year overall survival was 73% in patients who received radiation therapy vs. 63% for those who did not receive radiation therapy (p < 0.001). On multivariate analysis, patients with high-grade tumors who received radiation therapy had an improved overall survival (hazard ratio 0.67, 95% confidence interval 0.57-0.79). In patients receiving radiation therapy, 13.5% received it in a neoadjuvant setting. The incidence of patients receiving neoadjuvant radiation did not change significantly between 1988 and 2005. Conclusions: To our knowledge, this is the largest population-based study reported in patients undergoing limb-sparing surgery for soft tissue sarcomas of the extremities. It reports that radiation was associated with improved survival in patients with high-grade tumors.

  14. Flux balance modeling to predict bacterial survival during pulsed-activity events

    Science.gov (United States)

    Jose, Nicholas A.; Lau, Rebecca; Swenson, Tami L.; Klitgord, Niels; Garcia-Pichel, Ferran; Bowen, Benjamin P.; Baran, Richard; Northen, Trent R.

    2018-04-01

    Desert biological soil crusts (BSCs) are cyanobacteria-dominated surface soil microbial communities common to plant interspaces in arid environments. The capability to significantly dampen their metabolism allows them to exist for extended periods in a desiccated dormant state that is highly robust to environmental stresses. However, within minutes of wetting, metabolic functions reboot, maximizing activity during infrequent permissive periods. Microcoleus vaginatus, a primary producer within the crust ecosystem and an early colonizer, initiates crust formation by binding particles in the upper layer of soil via exopolysaccharides, making microbial dominated biological soil crusts highly dependent on the viability of this organism. Previous studies have suggested that biopolymers play a central role in the survival of this organism by powering resuscitation, rapidly forming compatible solutes, and fueling metabolic activity in dark, hydrated conditions. To elucidate the mechanism of this phenomenon and provide a basis for future modeling of BSCs, we developed a manually curated, genome-scale metabolic model of Microcoleus vaginatus (iNJ1153). To validate this model, gas chromatography-mass spectroscopy (GC-MS) and liquid chromatography-mass spectroscopy (LC-MS) were used to characterize the rate of biopolymer accumulation and depletion in in hydrated Microcoleus vaginatus under light and dark conditions. Constraint-based flux balance analysis showed agreement between model predictions and experimental reaction fluxes. A significant amount of consumed carbon and light energy is invested into storage molecules glycogen and polyphosphate, while β-polyhydroxybutyrate may function as a secondary resource. Pseudo-steady-state modeling suggests that glycogen, the primary carbon source with the fastest depletion rate, will be exhausted if M. vaginatus experiences dark wetting events 4 times longer than light wetting events.

  15. Survival Impact of Adjuvant Radiation Therapy in Masaoka Stage II to IV Thymomas: A Systematic Review and Meta-analysis

    International Nuclear Information System (INIS)

    Lim, Yu Jin; Kim, Eunji; Kim, Hak Jae; Wu, Hong-Gyun; Yan, Jinchun; Liu, Qin; Patel, Shilpen

    2016-01-01

    Purpose: To evaluate the survival impact of postoperative radiation therapy (PORT) in stage II to IV thymomas, using systematic review and meta-analysis. Methods and Materials: A database search was conducted with EMBASE, PubMed, Web of Science, Cochrane Library, and Ovid from inception to August 2015. Thymic carcinomas were excluded, and studies comparing overall survival (OS) with and without PORT in thymomas were included. The hazard ratios (HRs) of OS were extracted, and a random-effects model was used in the pooled analysis. Results: Seven retrospective series with a total of 1724 patients were included and analyzed. Almost all of the patients underwent macroscopically complete resection, and thymoma histology was confirmed by the World Health Organization criteria. In the overall analysis of stage II to IV thymomas, OS was not altered with the receipt of PORT (HR 0.79, 95% confidence interval [CI] 0.58-1.08). Although PORT was not associated with survival difference in Masaoka stage II disease (HR 1.45, 95% CI 0.83-2.55), improved OS was observed with the addition of PORT in the discrete pooled analysis of stage III to IV (HR 0.63, 95% CI 0.40-0.99). Significant heterogeneity and publication bias were not found in the analyses. Conclusions: From the present meta-analysis of sole primary thymomas, we suggest the potential OS benefit of PORT in locally advanced tumors with macroscopically complete resection, but not in stage II disease. Further investigations with sufficient survival data are needed to establish detailed treatment indications.

  16. Survival Impact of Adjuvant Radiation Therapy in Masaoka Stage II to IV Thymomas: A Systematic Review and Meta-analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lim, Yu Jin; Kim, Eunji [Department of Radiation Oncology, Seoul National University College of Medicine, Seoul (Korea, Republic of); Kim, Hak Jae, E-mail: khjae@snu.ac.kr [Department of Radiation Oncology, Seoul National University College of Medicine, Seoul (Korea, Republic of); Wu, Hong-Gyun [Department of Radiation Oncology, Seoul National University College of Medicine, Seoul (Korea, Republic of); Cancer Research Institute, Seoul National University College of Medicine, Seoul (Korea, Republic of); Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul (Korea, Republic of); Yan, Jinchun [Department of Radiation Oncology, Dalian Medical University, Liaoning (China); Department of Radiation Oncology, Fudan University Cancer Hospital, Shanghai (China); Liu, Qin [The Wistar Institute, Philadelphia, Pennsylvania (United States); Patel, Shilpen [Department of Radiation Oncology, University of Washington Medical Center, Seattle, Washington (United States)

    2016-04-01

    Purpose: To evaluate the survival impact of postoperative radiation therapy (PORT) in stage II to IV thymomas, using systematic review and meta-analysis. Methods and Materials: A database search was conducted with EMBASE, PubMed, Web of Science, Cochrane Library, and Ovid from inception to August 2015. Thymic carcinomas were excluded, and studies comparing overall survival (OS) with and without PORT in thymomas were included. The hazard ratios (HRs) of OS were extracted, and a random-effects model was used in the pooled analysis. Results: Seven retrospective series with a total of 1724 patients were included and analyzed. Almost all of the patients underwent macroscopically complete resection, and thymoma histology was confirmed by the World Health Organization criteria. In the overall analysis of stage II to IV thymomas, OS was not altered with the receipt of PORT (HR 0.79, 95% confidence interval [CI] 0.58-1.08). Although PORT was not associated with survival difference in Masaoka stage II disease (HR 1.45, 95% CI 0.83-2.55), improved OS was observed with the addition of PORT in the discrete pooled analysis of stage III to IV (HR 0.63, 95% CI 0.40-0.99). Significant heterogeneity and publication bias were not found in the analyses. Conclusions: From the present meta-analysis of sole primary thymomas, we suggest the potential OS benefit of PORT in locally advanced tumors with macroscopically complete resection, but not in stage II disease. Further investigations with sufficient survival data are needed to establish detailed treatment indications.

  17. When will I succeed in my first-year diploma? Survival analysis in Dutch higher education

    NARCIS (Netherlands)

    Bruinsma, Marjon; Jansen, Ellen P. W. A.

    2009-01-01

    The goal of this study was to illustrate survival analysis with higher education data and gain insight into a limited set of factors that predict when students passed their first-year examination at a Dutch university. Study participants consisted of 565 first-year students in four departments. Data

  18. Survival analysis of postoperative nausea and vomiting in patients receiving patient-controlled epidural analgesia

    Directory of Open Access Journals (Sweden)

    Shang-Yi Lee

    2014-11-01

    Conclusion: Survival analysis using Cox regression showed that the average consumption of opioids played an important role in postoperative nausea and vomiting, a result not found by logistic regression. Therefore, the incidence of postoperative nausea and vomiting in patients cannot be reliably determined on the basis of a single visit at one point in time.

  19. 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)

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

    Science.gov (United States)

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

    2015-01-01

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

  1. The survival analysis on localized prostate cancer treated with neoadjuvant endocrine therapy followed by intensity modulated radiation therapy

    International Nuclear Information System (INIS)

    Gao Hong; Li Gaofeng; Wu Qinhong; Li Xuenan; Zhong Qiuzi; Xu Yonggang

    2010-01-01

    Objective: To retrospectively investigate clinical outcomes and prognostic factors in localized prostate cancer treated with neoadjuvant endocrine therapy followed by intensity modulated radiotherapy (IMRT). Methods: Between March 2003 and October 2008, 54 localized prostate cancer treated by IMRT were recruited. All patients had received endocrine therapy before IMRT. The endocrine therapy included surgical castration or medical castration in combination with antiandrogens. The target of IMRT was the prostate and seminal vesicles with or without pelvis. The biochemical failure was defined according to the phoenix definition. By using the risk grouping standard proposed by D'Amico, patients were divided into three groups: low-risk group (n = 5), intermediate-risk group (n = 12), and high-risk group (n = 37). Kaplan-Meier method was used to calculate the overall survival rate. Prognostic factors were analyzed by univariate and multiple Cox regression analysis. Results: The follow-up rate was 98%. The number of patients under follow-up was 39 at 3 years and 25 at 5 years. Potential prognostic factors, including risk groups, mode of endocrine therapy, time of endocrine therapy, phoenix grouping before IMRT, the prostate specific antigen doubling time (PSADT) before radiotherapy, PSA value before IMRT, interval of endocrine therapy and IMRT, irradiation region, and irradiation dose were analyzed by survival analysis. In univariate analysis, time of endocrine therapy (75 % vs 95 %, χ 2 = 6. 45, P = 0. 011), phoenix grouping before IMRT (87% vs 96%, χ 2 = 4. 36, P = 0. 037), interval of endocrine therapy and IMRT (80% vs 95%, χ 2 = 11.60, P= 0. 001), irradiation dose (75% vs 91%, χ 2 =5.92, P= 0. 015) were statistically significant prognostic factors for 3 - year overall survival , and risk groups (85 vs 53 vs 29, χ 2 = 6. 40, P =0. 041) and PSADT before IMRT (62 vs 120, U =24. 50, P =0. 003) were significant factors for the median survival time. In the multiple Cox

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

    NARCIS (Netherlands)

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

    2010-01-01

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

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

    NARCIS (Netherlands)

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

    2011-01-01

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

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

    NARCIS (Netherlands)

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

    2011-01-01

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

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

    NARCIS (Netherlands)

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

    2016-01-01

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

  6. Fissure sealants in caries prevention:a practice-based study using survival analysis

    OpenAIRE

    Leskinen, K. (Kaja)

    2010-01-01

    Abstract The purpose of this study was to analyse the effectiveness and cost of fissure sealant treatment in preventing dental caries in children in a practice-based research network using survival analysis. The survival times of first permanent molars in children were analysed in three countries: in Finland (age cohorts 1970–1972 and 1980–1982), in Sweden (1980–1982) and in Greece (1980–1982), and additionally at two municipal health centres in Finland (age cohorts 1988–1990 in Kemi...

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

    Science.gov (United States)

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

    2018-05-01

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

  8. Two-stage meta-analysis of survival data from individual participants using percentile ratios

    Science.gov (United States)

    Barrett, Jessica K; Farewell, Vern T; Siannis, Fotios; Tierney, Jayne; Higgins, Julian P T

    2012-01-01

    Methods for individual participant data meta-analysis of survival outcomes commonly focus on the hazard ratio as a measure of treatment effect. Recently, Siannis et al. (2010, Statistics in Medicine 29:3030–3045) proposed the use of percentile ratios as an alternative to hazard ratios. We describe a novel two-stage method for the meta-analysis of percentile ratios that avoids distributional assumptions at the study level. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22825835

  9. Gene expression meta-analysis identifies chromosomal regions involved in ovarian cancer survival

    DEFF Research Database (Denmark)

    Thomassen, Mads; Jochumsen, Kirsten M; Mogensen, Ole

    2009-01-01

    the relation of gene expression and chromosomal position to identify chromosomal regions of importance for early recurrence of ovarian cancer. By use of *Gene Set Enrichment Analysis*, we have ranked chromosomal regions according to their association to survival. Over-representation analysis including 1...... using death (P = 0.015) and recurrence (P = 0.002) as outcome. The combined mutation score is strongly associated to upregulation of several growth factor pathways....

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

    Science.gov (United States)

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

    2014-02-01

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

  11. Effects of non-surgical factors on digital replantation survival rate: a meta-analysis.

    Science.gov (United States)

    Ma, Z; Guo, F; Qi, J; Xiang, W; Zhang, J

    2016-02-01

    This study aimed to evaluate the risk factors affecting survival rate of digital replantation by a meta-analysis. A computer retrieval of MEDLINE, OVID, EMBASE, and CNKI databases was conducted to identify citations for digital replantation with digit or finger or thumb or digital or fingertip and replantation as keywords. RevMan 5.2 software was used to calculate the pooled odds ratios. In total, there were 4678 amputated digits in 2641 patients. Gender and ischemia time had no significant influence on the survival rate of amputation replantation (P > 0.05). Age, injured hand, injury type, zone, and the method of preservation the amputated digit significantly influence the survival rate of digital replantation (P < 0.05). Children, right hand, crush, or avulsion and little finger are the risk factors that adversely affect the outcome. Level 5*. © The Author(s) 2015.

  12. Clinicopathological analysis of recurrence patterns and prognostic factors for survival after hepatectomy for colorectal liver metastasis

    Directory of Open Access Journals (Sweden)

    Okuda Junji

    2010-09-01

    Full Text Available Abstract Background Hepatectomy is recommended as the most effective therapy for liver metastasis from colorectal cancer (CRCLM. It is crucial to elucidate the prognostic clinicopathological factors. Methods Eighty-three patients undergoing initial hepatectomy for CRCLM were retrospectively analyzed with respect to characteristics of primary colorectal and metastatic hepatic tumors, operation details and prognosis. Results The overall 5-year survival rate after initial hepatectomy for CRCLM was 57.5%, and the median survival time was 25 months. Univariate analysis clarified that the significant prognostic factors for poor survival were depth of primary colorectal cancer (≥ serosal invasion, hepatic resection margin ( Conclusions Optimal surgical strategies in conjunction with effective chemotherapeutic regimens need to be established in patients with risk factors for recurrence and poor outcomes as listed above.

  13. Impact of Interstitial Pneumonia on the Survival and Risk Factors Analysis of Patients with Hematological Malignancy

    Directory of Open Access Journals (Sweden)

    Wei-Liang Chen

    2013-01-01

    Full Text Available Background. The emergence of interstitial pneumonia (IP in patients with hematological malignancy (HM is becoming a challenging scenario in current practice. However, detailed characterization and investigation of outcomes and risk factors on survival have not been addressed. Methods. We conducted a retrospective study of 42,584 cancer patients covering the period between 1996 and 2008 using the institutional cancer registry system. Among 816 HM patients, 61 patients with IP were recognized. The clinical features, laboratory results, and histological types were studied to determine the impact of IP on survival and identify the profile of prognostic factors. Results. HM patients with IP showed a significant worse survival than those without IP in the 5-year overall survival (P=0.027. The overall survival showed no significant difference between infectious pneumonia and noninfectious interstitial pneumonia (IIP versus nIIP (P=0.323. In a multivariate Cox regression model, leukocyte and platelet count were associated with increased risk of death. Conclusions. The occurrence of IP in HM patients is associated with increased mortality. Of interest, nIIP is a prognostic indicator in patients with lymphoma but not in patients with leukemia. However, aggressive management of IP in patients with HM is strongly advised, and further prospective survey is warranted.

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

    Science.gov (United States)

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

    2016-10-01

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

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

    Directory of Open Access Journals (Sweden)

    Antonio Fernandez-Morales

    2011-03-01

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

  16. 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.

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

    Science.gov (United States)

    Warren, Joshua L; Gordon-Larsen, Penny

    2018-06-01

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

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

    Science.gov (United States)

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

    2015-08-01

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

  19. Direct lexical control of eye movements in reading: Evidence from a survival analysis of fixation durations

    Science.gov (United States)

    Reingold, Eyal M.; Reichle, Erik D.; Glaholt, Mackenzie G.; Sheridan, Heather

    2013-01-01

    Participants’ eye movements were monitored in an experiment that manipulated the frequency of target words (high vs. low) as well as their availability for parafoveal processing during fixations on the pre-target word (valid vs. invalid preview). The influence of the word-frequency by preview validity manipulation on the distributions of first fixation duration was examined by using ex-Gaussian fitting as well as a novel survival analysis technique which provided precise estimates of the timing of the first discernible influence of word frequency on first fixation duration. Using this technique, we found a significant influence of word frequency on fixation duration in normal reading (valid preview) as early as 145 ms from the start of fixation. We also demonstrated an equally rapid non-lexical influence on first fixation duration as a function of initial landing position (location) on target words. The time-course of frequency effects, but not location effects was strongly influenced by preview validity, demonstrating the crucial role of parafoveal processing in enabling direct lexical control of reading fixation times. Implications for models of eye-movement control are discussed. PMID:22542804

  20. The tourism and travel industry and its effect on the Great Recession: A multilevel survival analysis

    Directory of Open Access Journals (Sweden)

    Zdravko Šergo

    2017-12-01

    Full Text Available Does a country with a heavy dependence on a tourism economy have a tendency to succumb to more risk in a recession? With the shift from manufacturing-based economies in the developing world toward service-based industries, including tourism, a reliance on the tourism industry may erode economic stability in tourism-based countries, making them more prone to fall into a recession due to higher risks. In this paper, we wish to emphasise the positive impact of tourism specialisation indices in the international economy on the probability occurrence of a so-called Great Recession. This article uses a multilevel survival analysis and a generalised linear mixed-effect (GLMM structure modelling to investigate the impact of tourism development on the probability of recession frequency (risk in terms of months of duration and severity, by using data collected from 2007 to 2013 from 71 countries around the world, to see if recession frequency is positively correlated with the various indicators of tourism development. Two GLMMs were fitted to this data: logistic regression and count regression with a Poisson distribution. Results for both regressions show considerable evidence that the ratio between the number of overnight stays and the resident population and travel services as a percentage of commercial service exports positively impacts the probability for a country (from our sample to experience a recession event and can make recession worse in terms of severity, measured in months.

  1. Survival Analysis of Faculty Retention and Promotion in the Social Sciences by Gender.

    Directory of Open Access Journals (Sweden)

    Janet M Box-Steffensmeier

    Full Text Available Recruitment and retention of talent is central to the research performance of universities. Existing research shows that, while men are more likely than women to be promoted at the different stages of the academic career, no such difference is found when it comes to faculty retention rates. Current research on faculty retention, however, focuses on careers in science, technology, engineering, and mathematics (STEM. We extend this line of inquiry to the social sciences.We follow 2,218 tenure-track assistant professors hired since 1990 in seven social science disciplines at nineteen U.S. universities from time of hire to time of departure. We also track their time to promotion to associate and full professor. Using survival analysis, we examine gender differences in time to departure and time to promotion. Our methods account for censoring and unobserved heterogeneity, as well as effect heterogeneity across disciplines and cohorts.We find no statistically significant differences between genders in faculty retention. However, we do find that men are more likely to be granted tenure than women. When it comes to promotion to full professor, the results are less conclusive, as the effect of gender is sensitive to model specification.The results corroborate previous findings about gender patterns in faculty retention and promotion. They suggest that advances have been made when it comes to gender equality in retention and promotion, but important differences still persist.

  2. Arthritis and the Risk of Falling Into Poverty: A Survival Analysis Using Australian Data.

    Science.gov (United States)

    Callander, Emily J; Schofield, Deborah J

    2016-01-01

    Low income is known to be associated with having arthritis. However, no longitudinal studies have documented the relationship between developing arthritis and falling into poverty. The purpose of this study was to evaluate Australians who developed arthritis to determine if they had an elevated risk of falling into poverty. Survival analysis using Cox regression models was applied to nationally representative, longitudinal survey data obtained between January 1, 2007 and December 31, 2012 from Australian adults who were ages 21 years and older in 2007. The hazard ratio for falling into income poverty was 1.08 (95% confidence interval [95% CI] 1.06-1.09) in women who were diagnosed as having arthritis and 1.15 (95% CI 1.13-1.16) in men who were diagnosed as having arthritis, as compared to those who were never diagnosed as having arthritis. The hazard ratio for falling into multidimensional poverty was 1.15 (95% CI 1.14-1.17) in women who were diagnosed as having arthritis and 1.88 (95% CI 1.85-1.91) in men who were diagnosed as having arthritis. Developing arthritis increases the risk of falling into income poverty and multidimensional poverty. The risk of multidimensional poverty is greater than the risk of income poverty. Given the high prevalence of arthritis, the condition is likely an overlooked driver of poverty. © 2016, American College of Rheumatology.

  3. Survival analysis of patients with uveal melanoma after organ preserving and liquidation treatment

    Directory of Open Access Journals (Sweden)

    E. E. Grishina

    2018-01-01

    Full Text Available Rationale: Uveal melanoma is the most common primary malignancy of the eye.Aim: To evaluate survival in patients with uveal melanoma stratified according to the type of treatment and to identify factors significantly associated with their survival.Materials and methods: The study was performed on the data extracted from medical files and follow-up forms of patients with uveal melanoma seen in the Ophthalmological Clinical Hospital of the Department of Healthcare, Moscow, from 1977 to 2012. Analysis of survival was used to assess the life longevity of patients with uveal melanoma. The analysis was censored at January 2013, when vital status (dead or alive of all patients was assessed. The factors included into the study analysis, were those taken from the follow-up forms. The incidence of uveal melanoma in Moscow (2012 was 0.9 per 100,000 of the population, whereas its prevalence was 11.1 per 100,000.Results: 698 patients with uveal melanoma were included into the study, among them 260 (37% men (aged from 19 to 87 years, median age 60 years and 438 (63% women (aged from 18 to 93 years, median age 63 years; therefore, the proportion of women under the follow-up monitoring was by 26% higher than that of men. The liquidation treatment (mostly enucleation was performed in 358 (51% of the patients, whereas the organ preserving treatment in 340 (49%. At 5, 7, and 10 years of the follow-up, the disease-specific survival of patients with uveal melanoma after the organ preserving treatment (median survival has not been reached and after the liquidation treatment (median, 88 months were 89 ± 2, 83 ± 3, and 75 ± 4% versus 63 ± 3, 52 ± 4, and 47 ± 5%, respectively (р = 0.001. Overall survival and disease-specific survival of the patients after the liquidation treatment were significantly lower than in the patients after the organ-preserving treatment. According to multiple regression analysis, this was associated not with the type of

  4. Survival analysis of dialysis patients in selected hospitals of lahore city

    International Nuclear Information System (INIS)

    Ahmad, Z.; Shahzad, I.

    2015-01-01

    There are several reasons which are directly or indirectly relate to affect the survival time of End Stage Renal Disease (ESRD) patients. This study was done to analyse the survival rate of ESRD patients in Lahore city, and to evaluate the influence of various risk factors and prognostic factors on survival of these patients. Methods: A sample of 40 patients was taken from the Jinnah Hospital Lahore and Lahore General Hospital by using the convenience sampling technique. The Log Rank Test was used to determine the significant difference between the categories of qualitative variables of ESRD patients. Multivariate Cox Regression Analysis was used to analyse the effect of different clinical and socio-economic variables on the survival time of these patients. Results: Different qualitative variables like: age, marital status, BMI, comorbid factors, diabetes type, gender, income level, place, risk factor like diabetes, ischemic heart disease, hypertension and Hepatitis status were analysed on the basis of Log Rank Test. While age and comorbid factors were found to be statistically significant which showed that the distribution of age and comorbid factors were different. By using the Cox Regression analysis the coefficient of Mass, serum albumin and family history of diabetes were found to be significant. Conclusions: There were some of the factors which had been taken for the analysis came out less or more significant in patients of ESRD. So it was concluded that mostly clinical factors which were Mass of the Patient, Serum Albumin and Family History of Diabetes made significant contribution towards the survival status of patients. (author)

  5. ATM and p53 combined analysis predicts survival in glioblastoma multiforme patients: A clinicopathologic study.

    Science.gov (United States)

    Romano, Francesco Jacopo; Guadagno, Elia; Solari, Domenico; Borrelli, Giorgio; Pignatiello, Sara; Cappabianca, Paolo; Del Basso De Caro, Marialaura

    2018-06-01

    Glioblastoma is one of the most malignant cancers, with a distinguishing dismal prognosis: surgery followed by chemo- and radiotherapy represents the current standard of care, and chemo- and radioresistance underlie disease recurrence and short overall survival of patients suffering from this malignancy. ATM is a kinase activated by autophosphorylation upon DNA doublestrand breaks arising from errors during replication, byproducts of metabolism, chemotherapy or ionizing radiations; TP53 is one of the most popular tumor suppressor, with a preeminent role in DNA damage response and repair. To study the effects of the immunohistochemical expression of p-ATM and p53 in glioblastoma patients, 21 cases were retrospectively examined. In normal brain tissue, p-ATM was expressed only in neurons; conversely, in tumors cells, the protein showed a variable cytoplasmic expression (score: +,++,+++), with being completely undetectable in three cases. Statistical analysis revealed that high p-ATM score (++/+++) strongly correlated to shorter survival (P = 0.022). No difference in overall survival was registered between p53 normally expressed (NE) and overexpressed (OE) glioblastoma patients (P = 0.669). Survival analysis performed on the results from combined assessment of the two proteins showed that patients with NE p53 /low pATM score had longer overall survival than the NE p53/ high pATM score counterpart. Cox-regression analysis confirmed this finding (HR = 0.025; CI 95% = 0.002-0.284; P = 0.003). Our study outlined the immunohistochemical expression of p-ATM/p53 in glioblastomas and provided data on their possible prognostic/predictive of response role. A "non-oncogene addiction" to ATM for NEp53 glioblastoma could be postulated, strengthening the rationale for development of ATM inhibiting drugs. © 2018 Wiley Periodicals, Inc.

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

    Science.gov (United States)

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

    2014-01-01

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

  7. Survival analysis of female dogs with mammary tumors after mastectomy: epidemiological, clinical and morphological aspects

    Directory of Open Access Journals (Sweden)

    Maria Luíza de M. Dias

    2016-03-01

    Full Text Available Abstract: Mammary gland tumors are the most common type of tumors in bitches but research on survival time after diagnosis is scarce. The purpose of this study was to investigate the relationship between survival time after mastectomy and a number of clinical and morphological variables. Data was collected retrospectively on bitches with mammary tumors seen at the Small Animal Surgery Clinic Service at the University of Brasília. All subjects had undergone mastectomy. Survival analysis was conducted using Cox's proportional hazard method. Of the 139 subjects analyzed, 68 died and 71 survived until the end of the study (64 months. Mean age was 11.76 years (SD=2.71, 53.84% were small dogs. 76.92% of the tumors were malignant, and 65.73% had both thoracic and inguinal glands affected. Survival time in months was associated with age (hazard rate ratios [HRR] =1.23, p-value =1.4x10-4, animal size (HRR between giant and small animals =2.61, p-value =0.02, nodule size (HRR =1.09, p-value =0.03, histological type (HRR between solid carcinoma and carcinoma in a mixed tumor =2.40, p-value =0.02, time between diagnosis and surgery (TDS, with HRR =1.21, p-value =2.7x10-15, and the interaction TDS*follow-up time (HRR =0.98, p-value =1.6x10-11. The present study is one of the few on the subject matter. Several important covariates were evaluated and age, animal size, nodule size, histological type, TDS and TDS*follow up time were identified as significantly associated to survival time.

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

    International Nuclear Information System (INIS)

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

    2002-01-01

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

  9. Outcome predictors in the management of intramedullary classic ependymoma: An integrative survival analysis.

    Science.gov (United States)

    Wang, Yinqing; Cai, Ranze; Wang, Rui; Wang, Chunhua; Chen, Chunmei

    2018-06-01

    This is a retrospective study.The aim of this study was to illustrate the survival outcomes of patients with classic ependymoma (CE) and identify potential prognostic factors.CE is the most common category of spinal ependymomas, but few published studies have discussed predictors of the survival outcome.A Boolean search of the PubMed, Embase, and OVID databases was conducted by 2 investigators independently. The objects were intramedullary grade II ependymoma according to 2007 WHO classification. Univariate Kaplan-Meier analysis and Log-Rank tests were performed to identify variables associated with progression-free survival (PFS) or overall survival (OS). Multivariate Cox regression was performed to assess hazard ratios (HRs) with 95% confidence intervals (95% CIs). Statistical analysis was performed by SPSS version 23.0 (IBM Corp.) with statistical significance defined as P analysis showed that patients who had undergone total resection (TR) had better PFS and OS than those with subtotal resection (STR) and biopsy (P = .002, P = .004, respectively). Within either univariate or multivariate analysis (P = .000, P = .07, respectively), histological type was an independent prognostic factor for PFS of CE [papillary type: HR 0.002, 95% CI (0.000-0.073), P = .001, tanycytic type: HR 0.010, 95% CI (0.000-0.218), P = .003].It was the first integrative analysis of CE to elucidate the correlation between kinds of factors and prognostic outcomes. Definite histological type and safely TR were foundation of CE's management. 4.

  10. Survival analysis of factors affecting incidence risk of Salmonella Dublin in Danish dairy herds during a 7-year surveillance period

    DEFF Research Database (Denmark)

    Nielsen, Liza Rosenbaum; Dohoo, Ian

    2012-01-01

    , proportional hazard model allowing for recurrence within herds. During October to December the hazard of failures was higher (hazard ratio HR=3.4, P=0.0005) than the rest of the year. Accounting for the delay in bulk-tank milk antibody responses to S. Dublin infection, this indicates that introduction......-quarters (YQs), either at the start of the study period or after recovery from infection. Survival analysis was performed on a dataset including 6931 dairy herds with 118969 YQs at risk, in which 1523 failures (new infection events) occurred. Predictors obtained from register data were tested in a multivariable...

  11. 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

  12. Tracheostomy mechanical ventilation in patients with amyotrophic lateral sclerosis: clinical features and survival analysis.

    Science.gov (United States)

    Spataro, Rossella; Bono, Valeria; Marchese, Santino; La Bella, Vincenzo

    2012-12-15

    Tracheostomy mechanical ventilation (TMV) is performed in amyotrophic lateral sclerosis (ALS) patients with a respiratory failure or when the non-invasive ventilation (NIV) is no longer effective. We evaluated the clinical characteristics and survival of a cohort of tracheostomized ALS patients, followed in a single ALS Clinical Center. Between 2001 and 2010, 87 out of 279 ALS patients were submitted to TMV. Onset was spinal in 62 and bulbar in 25. After tracheostomy, most patients were followed up through telephone interviews to caregivers. A complete survival analysis could be performed in fifty-two TMV patients. 31.3% ALS patients underwent tracheostomy, with a male prevalence (M/F=1.69) and a median age of 61 years (interquartile range=47-66). After tracheostomy, nearly all patients were under home care. TMV ALS patients were more likely than non-tracheostomized (NT) patients to be implanted with a PEG device, although the bulbar-/spinal-onset ratio did not differ between the two groups. Kaplan-Meyer analysis showed that tracheostomy increases median survival (TMV, 47 months vs NT, 31 months, p=0.008), with the greatest effect in patients younger than 60 at onset (TMV ≤ 60 years, 57.5 months vs NT ≤ 60 years, 38.5 months, p=0.002). TMV is increasingly performed in ALS patients. Nearly all TMV patients live at home and most of them are fed through a PEG device. Survival after tracheostomy is generally increased, with the stronger effect in patients younger than 60. This survival advantage is apparently lost when TMV is performed in patients older than 60. The results of this study might be useful for the decision-making process of patients and their families about this advanced palliative care. Copyright © 2012. Published by Elsevier B.V.

  13. Chemoembolization With Doxorubicin-Eluting Beads for Unresectable Hepatocellular Carcinoma: Five-Year Survival Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Malagari, Katerina, E-mail: kmalag@otonet.gr [University of Athens, Second Department of Radiology (Greece); Pomoni, Mary [University of Athens, Imaging and Research Unit (Greece); Moschouris, Hippocrates, E-mail: hipmosch@gmail.com [Tzanion Hospital, Department of Radiology (Greece); Bouma, Evanthia [University of Athens, Imaging and Research Unit (Greece); Koskinas, John [Ippokration Hospital, University of Athens, Department of Internal Medicine and Hepatology (Greece); Stefaniotou, Aspasia [University of Athens, Imaging and Research Unit (Greece); Marinis, Athanasios [Tzanion Hospital, Department of Surgery (Greece); Kelekis, Alexios; Alexopoulou, Efthymia [University of Athens, Second Department of Radiology (Greece); Chatziioannou, Achilles [University of Athens, First Department of Radiology (Greece); Chatzimichael, Katerina [University of Athens, Second Department of Radiology (Greece); Dourakis, Spyridon [Ippokration Hospital, University of Athens, Department of Internal Medicine and Hepatology (Greece); Kelekis, Nikolaos [University of Athens, Second Department of Radiology (Greece); Rizos, Spyros [Tzanion Hospital, Department of Surgery (Greece); Kelekis, Dimitrios [University of Athens, Imaging and Research Unit (Greece)

    2012-10-15

    Purpose: The purpose of this study was to report on the 5-year survival of hepatocellular carcinoma (HCC) patients treated with DC Bead loaded with doxorubicin (DEB-DOX) in a scheduled scheme in up to three treatments and thereafter on demand. Materials and Methods: 173 HCC patients not suitable for curable treatments were prospectively enrolled (mean age 70.4 {+-} 7.4 years). Child-Pugh (Child) class was A/B (102/71 [59/41 %]), Okuda stage was 0/1/2 (91/61/19 [53.2/35.7/11.1 %]), and mean lesion diameter was 7.6 {+-} 2.1 cm. Lesion morphology was one dominant {<=}5 cm (22 %), one dominant >5 cm (41.6 %), multifocal {<=}5 (26 %), and multifocal >5 (10.4 %). Results: Overall survival at 1, 2, 3, 4, and 5 years was 93.6, 83.8, 62, 41.04, and 22.5 %, with higher rates achieved in Child class A compared with Child class B patients (95, 88.2, 61.7, 45, and 29.4 % vs. 91.5, 75, 50.7, 35.2, and 12.8 %). Mean overall survival was 43.8 months (range 1.2-64.8). Cumulative survival was better for Child class A compared with Child class B patients (p = 0.029). For patients with dominant lesions {<=}5 cm 1-, 2-, 3-, 4-, and 5-year survival rates were 100, 95.2, 71.4, 66.6, and 47.6 % for Child class A and 94.1, 88.2, 58.8, 41.2, 29.4, and 23.5 % for Child class B patients. Regarding DEB-DOX treatment, multivariate analysis identified number of lesions (p = 0.033), lesion vascularity (p < 0.0001), initially achieved complete response (p < 0.0001), and objective response (p = 0.046) as significant and independent determinants of 5-year survival. Conclusion: DEB-DOX results, with high rates of 5-year survival for patients, not amenable to curative treatments. Number of lesions, lesion vascularity, and local response were significant independent determinants of 5-year survival.

  14. Chemoembolization With Doxorubicin-Eluting Beads for Unresectable Hepatocellular Carcinoma: Five-Year Survival Analysis

    International Nuclear Information System (INIS)

    Malagari, Katerina; Pomoni, Mary; Moschouris, Hippocrates; Bouma, Evanthia; Koskinas, John; Stefaniotou, Aspasia; Marinis, Athanasios; Kelekis, Alexios; Alexopoulou, Efthymia; Chatziioannou, Achilles; Chatzimichael, Katerina; Dourakis, Spyridon; Kelekis, Nikolaos; Rizos, Spyros; Kelekis, Dimitrios

    2012-01-01

    Purpose: The purpose of this study was to report on the 5-year survival of hepatocellular carcinoma (HCC) patients treated with DC Bead loaded with doxorubicin (DEB-DOX) in a scheduled scheme in up to three treatments and thereafter on demand. Materials and Methods: 173 HCC patients not suitable for curable treatments were prospectively enrolled (mean age 70.4 ± 7.4 years). Child-Pugh (Child) class was A/B (102/71 [59/41 %]), Okuda stage was 0/1/2 (91/61/19 [53.2/35.7/11.1 %]), and mean lesion diameter was 7.6 ± 2.1 cm. Lesion morphology was one dominant ≤5 cm (22 %), one dominant >5 cm (41.6 %), multifocal ≤5 (26 %), and multifocal >5 (10.4 %). Results: Overall survival at 1, 2, 3, 4, and 5 years was 93.6, 83.8, 62, 41.04, and 22.5 %, with higher rates achieved in Child class A compared with Child class B patients (95, 88.2, 61.7, 45, and 29.4 % vs. 91.5, 75, 50.7, 35.2, and 12.8 %). Mean overall survival was 43.8 months (range 1.2–64.8). Cumulative survival was better for Child class A compared with Child class B patients (p = 0.029). For patients with dominant lesions ≤5 cm 1-, 2-, 3-, 4-, and 5-year survival rates were 100, 95.2, 71.4, 66.6, and 47.6 % for Child class A and 94.1, 88.2, 58.8, 41.2, 29.4, and 23.5 % for Child class B patients. Regarding DEB-DOX treatment, multivariate analysis identified number of lesions (p = 0.033), lesion vascularity (p < 0.0001), initially achieved complete response (p < 0.0001), and objective response (p = 0.046) as significant and independent determinants of 5-year survival. Conclusion: DEB-DOX results, with high rates of 5-year survival for patients, not amenable to curative treatments. Number of lesions, lesion vascularity, and local response were significant independent determinants of 5-year survival.

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

    Science.gov (United States)

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

    2017-10-01

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

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

    International Nuclear Information System (INIS)

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

    2009-01-01

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

  17. Tracheostomy and invasive mechanical ventilation in amyotrophic lateral sclerosis: decision-making factors and survival analysis.

    Science.gov (United States)

    Kimura, Fumiharu

    2016-04-28

    Invasive and/or non-invasive mechanical ventilation are most important options of respiratory management in amyotrophic lateral sclerosis. We evaluated the frequency, clinical characteristics, decision-making factors about ventilation and survival analysis of 190 people with amyotrophic lateral sclerosis patients from 1990 until 2013. Thirty-one percentage of patients underwent tracheostomy invasive ventilation with the rate increasing more than the past 20 years. The ratio of tracheostomy invasive ventilation in patients >65 years old was significantly increased after 2000 (25%) as compared to before (10%). After 2010, the standard use of non-invasive ventilation showed a tendency to reduce the frequency of tracheostomy invasive ventilation. Mechanical ventilation prolonged median survival (75 months in tracheostomy invasive ventilation, 43 months in non-invasive ventilation vs natural course, 32 months). The life-extending effects by tracheostomy invasive ventilation were longer in younger patients ≤65 years old at the time of ventilation support than in older patients. Presence of partners and care at home were associated with better survival. Following factors related to the decision to perform tracheostomy invasive ventilation: patients ≤65 years old: greater use of non-invasive ventilation: presence of a spouse: faster tracheostomy: higher progression rate; and preserved motor functions. No patients who underwent tracheostomy invasive ventilation died from a decision to withdraw mechanical ventilation. The present study provides factors related to decision-making process and survival after tracheostomy and help clinicians and family members to expand the knowledge about ventilation.

  18. Survival analysis of colorectal cancer patients with tumor recurrence using global score test methodology

    Energy Technology Data Exchange (ETDEWEB)

    Zain, Zakiyah, E-mail: zac@uum.edu.my; Ahmad, Yuhaniz, E-mail: yuhaniz@uum.edu.my [School of Quantitative Sciences, Universiti Utara Malaysia, UUM Sintok 06010, Kedah (Malaysia); Azwan, Zairul, E-mail: zairulazwan@gmail.com, E-mail: farhanaraduan@gmail.com, E-mail: drisagap@yahoo.com; Raduan, Farhana, E-mail: zairulazwan@gmail.com, E-mail: farhanaraduan@gmail.com, E-mail: drisagap@yahoo.com; Sagap, Ismail, E-mail: zairulazwan@gmail.com, E-mail: farhanaraduan@gmail.com, E-mail: drisagap@yahoo.com [Surgery Department, Universiti Kebangsaan Malaysia Medical Centre, Jalan Yaacob Latif, 56000 Bandar Tun Razak, Kuala Lumpur (Malaysia); Aziz, Nazrina, E-mail: nazrina@uum.edu.my

    2014-12-04

    Colorectal cancer is the third and the second most common cancer worldwide in men and women respectively, and the second in Malaysia for both genders. Surgery, chemotherapy and radiotherapy are among the options available for treatment of patients with colorectal cancer. In clinical trials, the main purpose is often to compare efficacy between experimental and control treatments. Treatment comparisons often involve several responses or endpoints, and this situation complicates the analysis. In the case of colorectal cancer, sets of responses concerned with survival times include: times from tumor removal until the first, the second and the third tumor recurrences, and time to death. For a patient, the time to recurrence is correlated to the overall survival. In this study, global score test methodology is used in combining the univariate score statistics for comparing treatments with respect to each survival endpoint into a single statistic. The data of tumor recurrence and overall survival of colorectal cancer patients are taken from a Malaysian hospital. The results are found to be similar to those computed using the established Wei, Lin and Weissfeld method. Key factors such as ethnic, gender, age and stage at diagnose are also reported.

  19. The survival analysis of beta thalassemia major patients in South East of Iran

    International Nuclear Information System (INIS)

    Roudbari, M.; Soltani-Rad, M.; Roudbari, S.

    2008-01-01

    The objective was to determine the survival of beta-thalassemia major patients with transfusion, and its related factors in Southeast of Iran. This cross-sectional study was performed in Zahedan, Iran in 2007. The sample included patients who were referred from all over the Zahedan Thalassemia Center from 1998 to 2006. The data were collected using the patient's records, which were recorded by the staff during transfusion. The data included demographic and medical information blood group, blood RH, the kind of transfused blood [KTB], annual number of transfusions [ANOT], accompanied disease [AD], Hemoglobin [Hb] and ferritin level. For data analysis, the Kaplan-Meyer method, and Long Rank test together with Cox Regression were used. Forty-six of 578 patients died and 99% survived for the first year. The ages survival proportions were 5 (97.9%), 10 (97%), 15 (92.1%), and 20 (81.2%) years. The survival time showed significant relationships with the ANOT p=0.0053, KTB p=0.003, Hb=0.002 and ferritin level p=0.0087, and AD p=0.00. Using regular transfusion, paying attention to screening of transfused blood, increasing the families knowledge on the disease to prevent the bearing of thalassemia fetus, are recommended; finally, the detection and treating of the AD, are of great importance to extend the lifetime of the patients. (author)

  20. Rituximab is associated with improved survival in Burkitt lymphoma: a retrospective analysis from two US academic medical centers.

    Science.gov (United States)

    Wildes, Tanya M; Farrington, Laura; Yeung, Cecilia; Harrington, Alexandra M; Foyil, Kelley V; Liu, Jingxia; Kreisel, Friederike; Bartlett, Nancy L; Fenske, Timothy S

    2014-02-01

    Burkitt lymphoma (BL) is a rare, highly aggressive B-cell malignancy treated most successfully with brief-duration, high-intensity chemotherapeutic regimens. The benefit of the addition of rituximab to these regimens remains uncertain. We sought to examine the effectiveness of chemotherapy with and without rituximab in patients with BL. This study is a retrospective cohort study of all adult patients with BL diagnosed and treated with modern, dose-intense chemotherapeutic regimens from 1998-2008 at two tertiary care institutions. All cases were confirmed by application of WHO 2008 criteria by hematopathologists. Medical records were reviewed for patient-, disease-, and treatment- related factors as well as treatment response and survival. Factors associated with survival were analyzed using Cox proportional hazards modeling. A total of 35 patients were analyzed: 18 patients received rituximab with chemotherapy (R-chemo) and 17 received chemotherapy (chemo) alone. The median age was 42 (range 20-74 years); 57% were male; 71% had Ann Arbor Stage IV disease; 33% had central nervous system involvement; 78% had an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1. R-chemo was associated with significantly longer overall survival (OS) than chemo alone (5-year OS 70% and 29%, respectively, p = 0.040). On multivariate regression analysis, poor performance status and central nervous system involvement were associated with poorer survival. The addition of rituximab to chemotherapy was associated with improved OS in patients with Burkitt lymphoma. Poor performance status and central nervous system involvement were prognostically significant on multivariate analysis.

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  2. 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

  3. 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

  4. Surrogacy of progression-free survival (PFS) for overall survival (OS) in esophageal cancer trials with preoperative therapy: Literature-based meta-analysis.

    Science.gov (United States)

    Kataoka, K; Nakamura, K; Mizusawa, J; Kato, K; Eba, J; Katayama, H; Shibata, T; Fukuda, H

    2017-10-01

    There have been no reports evaluating progression-free survival (PFS) as a surrogate endpoint in resectable esophageal cancer. This study was conducted to evaluate the trial level correlations between PFS and overall survival (OS) in resectable esophageal cancer with preoperative therapy and to explore the potential benefit of PFS as a surrogate endpoint for OS. A systematic literature search of randomized trials with preoperative chemotherapy or preoperative chemoradiotherapy for esophageal cancer reported from January 1990 to September 2014 was conducted using PubMed and the Cochrane Library. Weighted linear regression using sample size of each trial as a weight was used to estimate coefficient of determination (R 2 ) within PFS and OS. The primary analysis included trials in which the HR for both PFS and OS was reported. The sensitivity analysis included trials in which either HR or median survival time of PFS and OS was reported. In the sensitivity analysis, HR was estimated from the median survival time of PFS and OS, assuming exponential distribution. Of 614 articles, 10 trials were selected for the primary analysis and 15 for the sensitivity analysis. The primary analysis did not show a correlation between treatment effects on PFS and OS (R 2 0.283, 95% CI [0.00-0.90]). The sensitivity analysis did not show an association between PFS and OS (R 2 0.084, 95% CI [0.00-0.70]). Although the number of randomized controlled trials evaluating preoperative therapy for esophageal cancer is limited at the moment, PFS is not suitable for primary endpoint as a surrogate endpoint for OS. Copyright © 2017 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

  5. NTCP modelling of lung toxicity after SBRT comparing the universal survival curve and the linear quadratic model for fractionation correction

    International Nuclear Information System (INIS)

    Wennberg, Berit M.; Baumann, Pia; Gagliardi, Giovanna

    2011-01-01

    Background. In SBRT of lung tumours no established relationship between dose-volume parameters and the incidence of lung toxicity is found. The aim of this study is to compare the LQ model and the universal survival curve (USC) to calculate biologically equivalent doses in SBRT to see if this will improve knowledge on this relationship. Material and methods. Toxicity data on radiation pneumonitis grade 2 or more (RP2+) from 57 patients were used, 10.5% were diagnosed with RP2+. The lung DVHs were corrected for fractionation (LQ and USC) and analysed with the Lyman- Kutcher-Burman (LKB) model. In the LQ-correction α/β = 3 Gy was used and the USC parameters used were: α/β = 3 Gy, D 0 = 1.0 Gy, n = 10, α 0.206 Gy-1 and d T = 5.8 Gy. In order to understand the relative contribution of different dose levels to the calculated NTCP the concept of fractional NTCP was used. This might give an insight to the questions of whether 'high doses to small volumes' or 'low doses to large volumes' are most important for lung toxicity. Results and Discussion. NTCP analysis with the LKB-model using parameters m = 0.4, D50 = 30 Gy resulted for the volume dependence parameter (n) with LQ correction n = 0.87 and with USC correction n = 0.71. Using parameters m = 0.3, D 50 = 20 Gy n = 0.93 with LQ correction and n 0.83 with USC correction. In SBRT of lung tumours, NTCP modelling of lung toxicity comparing models (LQ,USC) for fractionation correction, shows that low dose contribute less and high dose more to the NTCP when using the USC-model. Comparing NTCP modelling of SBRT data and data from breast cancer, lung cancer and whole lung irradiation implies that the response of the lung is treatment specific. More data are however needed in order to have a more reliable modelling

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

    DEFF Research Database (Denmark)

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

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

  7. Analysis on Lung Cancer Survival from 2001 to 2007 in Qidong, China

    Directory of Open Access Journals (Sweden)

    Jian ZHU

    2011-01-01

    Full Text Available Background and objective Lung cancer is one of the most important malignancies in China. Survival rates of lung cancer on the population-based cancer registry for the years 2001-2007 in Qidong were analysed in order to provide the basis for the prognosis assessment and the control of this cancer. Methods Total 4,451 registered lung cancer cases was followed up to December 31st, 2009. Death certificates only (DCO cases were excluded, leaving 4,382 cases for survival analysis. Cumulative observed survival rate (OS and relative survival rate (RS were calculated using Hakulinen’s method performed by the SURV 3.01 software developed at the Finnish Cancer Registry. Results The 1-, 3-, and 5-year OS rates were 23.73%, 11.89%, 10.01%, and the RS rates were 24.86%, 13.69%, 12.73%, respectively. The 1-, 3-, and 5-year RS of males vs females were 23.70% vs 27.89%, 12.58% vs 16.53%, and 11.73% vs 15.21%, respectively, with statisitically significant differences (χ2=13.77, P=0.032. RS of age groups of 15-34, 35-44, 45-54, 55-64, 65-74 and 75+ were 35.46%, 17.66%, 11.97%, 13.49%, 10.61%, 15.14%, respectively. Remarkable improvement could be seen for the 5-year RS in this setting if compared with that for the years 1972-2000. Conclusion The lung cancer survival outcomes in Qidong have been improved gradually for the past decades. Further measures on the prevention, diagnosis and treatment of lung cancer should be taken.

  8. The costs of treating acute heart failure: an economic analysis of the SURVIVE trial.

    Science.gov (United States)

    de Lissovoy, Gregory; Fraeman, Kathy; Salon, Jeff; Chay Woodward, Tatia; Sterz, Raimund

    2008-01-01

    To estimate the incremental cost per life year gained with levosimendan relative to dobutamine in treatment of acute heart failure based on the Survival of Patients with Acute Heart Failure in Need of Intravenous Inotropic Support (SURVIVE) trial. SURVIVE enrolled 1,327 patients (levosimendan 664, dobutamine 663) from nine nations with 180-day survival from date of randomisation as the primary endpoint. Hospital resource utilisation was determined via clinical case reports. Unit costs were derived from hospital payment schedules for France, Germany and the UK, and represent a third-party payer perspective. Cost-effectiveness analysis was performed for a subset of the SURVIVE patient population selected in accordance with current levosimendan labeling. Mortality in the levosimendan group was 26 versus 28% for dobutamine (hazard ratio 0.91, 95% confidence interval 0.74-1.13, p=0.40). Initial hospitalisation length of stay was identical (levosimendan 14.4, dobutamine 14.5, p=0.98). Slightly lower rates of readmission were observed for levosimendan relative to dobutamine at 31 (p=0.13) and 180 days (p=0.23). Mean costs excluding study drug were equivalent for the index admission (levosimendan euro5,060, dobutamine euro4,952; p=0.91) and complete episode (levosimendan euro5,396, dobutamine euro5,275; p=0.93). At an acquisition cost of euro600 per vial, there is at least 50% likelihood that levosimendan is cost effective relative to dobutamine if willingness to pay is equal to or greater than euro15,000 per life year gained.

  9. 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.

  10. Dedifferentiated chondrosarcoma: A survival analysis of 159 cases from the SEER database (2001-2011).

    Science.gov (United States)

    Strotman, Patrick K; Reif, Taylor J; Kliethermes, Stephanie A; Sandhu, Jasmin K; Nystrom, Lukas M

    2017-08-01

    Dedifferentiated chondrosarcoma is a rare malignancy with reported 5-year overall survival rates ranging from 7% to 24%. The purpose of this investigation is to determine the overall survival of dedifferentiated chondrosarcoma in a modern patient series and how it is impacted by patient demographics, tumor characteristics, and surgical treatment factors. This is a retrospective review of the Surveillance, Epidemiology, and End Results (SEER) database from 2001 to 2011. Kaplan Meier analyses were used for overall and disease-specific survival. Univariable and multivariable cox regression models were used to identify prognostic factors. Five year overall- and disease-specific survival was 18% (95% CI: 12-26%) and 28% (95% CI: 18-37%), respectively. Individuals with extremity tumors had a worse prognosis than individuals with a primary tumor in the chest wall or axial skeleton (HR 0.20, 95% CI: 0.07-0.56; P = 0.002 and HR 0.60, 95% CI: 0.36-0.99; P = 0.04, respectively). Patients with AJCC stage III or IV disease (HR 2.51, 95% CI: 1.50-4.20; P = 0.001), tumors larger than 8 cm (HR 2.17, 95% CI: 1.11-4.27; P = 0.046), metastatic disease at diagnosis (HR 3.25, 95% CI: 1.98-5.33; P chondrosarcoma is poor with a 5-year overall survival of 18%. Patients with a primary tumor located in the chest wall had a better prognosis. Tumors larger than 8 cm, presence of metastases at diagnosis, and treatment without surgical resection were significant predictors of mortality. © 2017 Wiley Periodicals, Inc.

  11. 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.

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

    International Nuclear Information System (INIS)

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

    2004-01-01

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

  13. 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.

  14. Vitamin C and survival among women with breast cancer: a meta-analysis.

    Science.gov (United States)

    Harris, Holly R; Orsini, Nicola; Wolk, Alicja

    2014-05-01

    The association between dietary vitamin C intake and breast cancer survival is inconsistent and few studies have specifically examined vitamin C supplement use among women with breast cancer. The purpose of this study was to summarise results from prospective studies on the association between vitamin C supplement use and dietary vitamin C intake and breast cancer-specific mortality and total mortality. Studies were identified using the PubMed database through February 6, 2014 and by examining the references of retrieved articles. Prospective studies were included if they reported relative risks (RR) with 95% confidence intervals (95% CIs) for at least two categories or as a continuous exposure. Random-effects models were used to combine study-specific results. The ten identified studies examined vitamin C supplement use (n=6) and dietary vitamin C intake (n=7) and included 17,696 breast cancer cases, 2791 total deaths, and 1558 breast cancer-specific deaths. The summary RR (95% CI) for post-diagnosis vitamin C supplement use was 0.81 (95% CI 0.72-0.91) for total mortality and 0.85 (95% CI 0.74-0.99) for breast cancer-specific mortality. The summary RR for a 100mg per day increase in dietary vitamin C intake was 0.73 (95% CI 0.59-0.89) for total mortality and 0.78 (95% CI 0.64-0.94) for breast cancer-specific mortality. Results from this meta-analysis suggest that post-diagnosis vitamin C supplement use may be associated with a reduced risk of mortality. Dietary vitamin C intake was also statistically significantly associated with a reduced risk of total mortality and breast cancer-specific mortality. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. The risk of falling into poverty after developing heart disease: a survival analysis.

    Science.gov (United States)

    Callander, Emily J; Schofield, Deborah J

    2016-07-15

    Those with a low income are known to have a higher risk of developing heart disease. However, the inverse relationship - falling into income poverty after developing heart disease has not been explored with longitudinal data. This paper aims to determine if those with heart disease have an elevated risk of falling into poverty. Survival analysis was conducted using the longitudinal Household Income and Labour Dynamics in Australia survey, between the years 2007 and 2012. The study focused on the Australian population aged 21 years and over in 2007 who were not already in poverty and did not already have heart disease, who were followed from 2007 to 2012. Cox regression models adjusting for age, sex and time-varying co-variates (marital status, home ownership and remoteness of area of residence) were constructed to assess the risk of falling into poverty. For those aged 20 who developed heart disease, the hazard ratio for falling into income poverty was 9.24 (95 % CI: 8.97-9.51) and for falling into multidimensional poverty the hazard ratio was 14.21 (95 % CI: 13.76-14.68); for those aged 40 the hazard ratio for falling into income poverty was 3.45 (95 % CI: 3.39-3.51) and for multidimensional poverty, 5.20 (95 % CI: 5.11-5.29); and for those aged 60 the hazard ratio for falling into income poverty was 1.29 (95 % CI: 1.28-1.30) and for multidimensional poverty, 1.52 (95 % CI: 1.51-1.54), relative those who never developed heart disease. The risk for both income and multidimensional poverty decreases with age up to the age of 70, over which, those who developed heart disease had a reduced risk of poverty. For those under the age of 70, developing heart disease is associated with an increased risk of falling into both income poverty and multidimensional poverty.

  16. Hypofractionated radiation therapy for invasive thyroid carcinoma in dogs: a retrospective analysis of survival

    International Nuclear Information System (INIS)

    Brearley, M.J.; Hayes, A.M.; Murphy, S.

    1999-01-01

    Thirteen dogs with invasive thyroid carcinoma (WHO classification T2b or T3b) seen between January 1991 and October 1997 were treated by external beam Irradiation. Four once-weekly fractions of 9 gray of 4 MeV X-rays were administered. Four of the dogs died of progression of the primary disease and four from metastatic spread. Of the remaining dogs, three died of unrelated problems, although two were still alive at the time of the censor. Kaplan-Meier analysis of the survival time from first dose to death from either primary or metastatic disease gave a median survival time of 96 weeks (mean 85 weeks, range six to 247 weeks). Radiographic evidence of pulmonary metastatic disease at presentation had no prognostic value whereas crude growth rate was a highly significant factor. The present series Indicates that radiation therapy should be considered an important modality for the control of invasive thyroid carcinoma in the dog

  17. Mathematical analysis of 51Cr-labelled red cell survival curves in congenital haemolytic anaemias

    International Nuclear Information System (INIS)

    Kasfiki, A.G.; Antipas, S.E.; Dimitriou, P.A.; Gritzali, F.A.; Melissinos, K.G.

    1982-01-01

    The parameters of 51 Cr labelled red cell survival curves were calculated in 26 patients with homozygous β-thalassaemia, 8 with sickle-cell anaemia and 3 with s-β-thalassaemia, using a non-linear weighted least squares analysis computer program. In thalassaemic children the calculated parameters denote that the shorting of the mean cell life is due to early senescence alone, while there is some evidence that in thalassaemic adults additional extracellular destruction mechanisms participate as well. Red cell survival curves from patients with sickle-cell anaemia and s-β-thalassaemia resemble each other, while their parameters indicate an initial rapid loss of radioactivity, early senescence and the presence of extracellular red cell destruction factors. (orig.)

  18. Retrospective Analysis of the Survival Benefit of Induction Chemotherapy in Stage IVa-b Nasopharyngeal Carcinoma.

    Science.gov (United States)

    Lan, Xiao-Wen; Zou, Xue-Bin; Xiao, Yao; Tang, Jie; OuYang, Pu-Yun; Su, Zhen; Xie, Fang-Yun

    2016-01-01

    The value of adding induction chemotherapy to chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma (LA-NPC) remains controversial, yet high-risk patients with LA-NPC have poor outcomes after chemoradiotherapy. We aimed to assess the survival benefits of induction chemotherapy in stage IVa-b NPC. A total of 602 patients with stage IVa-b NPC treated with intensity-modulated radiation therapy (IMRT) and concurrent chemotherapy with or without induction chemotherapy were retrospectively analyzed. Overall survival (OS), locoregional relapse-free survival (LRFS), distant metastasis-free survival (DMFS) and progression-free survival (PFS) were evaluated using the Kaplan-Meier method, log-rank test and Cox regression analysis. In univariate analysis, 5-year OS was 83.2% for induction chemotherapy plus concurrent chemotherapy and 74.8% for concurrent chemotherapy alone, corresponding to an absolute risk reduction of 8.4% (P = 0.022). Compared to concurrent chemotherapy alone, addition of induction chemotherapy improved 5-year DMFS (83.2% vs. 74.4%, P = 0.018) but not 5-year LRFS (83.7% vs. 83.0%, P = 0.848) or PFS (71.9% vs. 66.0%, P = 0.12). Age, T category, N category, chemotherapy strategy and clinical stage were associated with 5-year OS (P = 0.017, P = 0.031, P = 0.007, P = 0.022, P = 0.001, respectively). In multivariate analysis, induction chemotherapy plus concurrent chemotherapy was an independent favorable prognostic factor for OS (HR, 0.62; 95% CI, 0.43-0.90, P = 0.012) and DMFS (HR, 0.57; 95% CI, 0.38-0.83, P = 0.004). In subgroup analysis, induction chemotherapy significantly improved 5-year DMFS in stage IVa (86.8% vs. 77.3%, P = 0.008), but provided no significant benefit in stage IVb. In patients with stage IVa-b NPC treated with IMRT, addition of induction chemotherapy to concurrent chemotherapy significantly improved 5-year OS and 5-year DMFS. This study provides a basis for selection of high risk patients in future clinical therapeutic

  19. A multi-year analysis of spillway survival for juvenile salmonids as a function of spill bay operations at McNary Dam, Washington and Oregon, 2004-09

    Science.gov (United States)

    Adams, Noah S.; Hansel, Hal C.; Perry, Russell W.; Evans, Scott D.

    2012-01-01

    spillway survival for this species. Bypass survival of yearling Chinook salmon could be improved by optimizing conditions to facilitate bypass passage at night, but the method to do so is not apparent from this analysis because photoperiod was the only factor affecting bypass survival based on the best and only supported model. Bypass survival of juvenile steelhead would benefit from lower water temperatures and increased total and spillway discharge. Likewise, subyearling Chinook salmon bypass survival would improve with lower water temperatures, increased total discharge, and a uniform spill pattern.

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

    Science.gov (United States)

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

    2015-02-01

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

  1. 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.

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

    International Nuclear Information System (INIS)

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

    1991-01-01

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

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

    Science.gov (United States)

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

    2018-03-01

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

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

    Science.gov (United States)

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

    2011-01-01

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

  5. SDI CFD MODELING ANALYSIS

    Energy Technology Data Exchange (ETDEWEB)

    Lee, S.

    2011-05-05

    The Savannah River Remediation (SRR) Organization requested that Savannah River National Laboratory (SRNL) develop a Computational Fluid Dynamics (CFD) method to mix and blend the miscible contents of the blend tanks to ensure the contents are properly blended before they are transferred from the blend tank; such as, Tank 50H, to the Salt Waste Processing Facility (SWPF) feed tank. The work described here consists of two modeling areas. They are the mixing modeling analysis during miscible liquid blending operation, and the flow pattern analysis during transfer operation of the blended liquid. The transient CFD governing equations consisting of three momentum equations, one mass balance, two turbulence transport equations for kinetic energy and dissipation rate, and one species transport were solved by an iterative technique until the species concentrations of tank fluid were in equilibrium. The steady-state flow solutions for the entire tank fluid were used for flow pattern analysis, for velocity scaling analysis, and the initial conditions for transient blending calculations. A series of the modeling calculations were performed to estimate the blending times for various jet flow conditions, and to investigate the impact of the cooling coils on the blending time of the tank contents. The modeling results were benchmarked against the pilot scale test results. All of the flow and mixing models were performed with the nozzles installed at the mid-elevation, and parallel to the tank wall. From the CFD modeling calculations, the main results are summarized as follows: (1) The benchmark analyses for the CFD flow velocity and blending models demonstrate their consistency with Engineering Development Laboratory (EDL) and literature test results in terms of local velocity measurements and experimental observations. Thus, an application of the established criterion to SRS full scale tank will provide a better, physically-based estimate of the required mixing time, and

  6. Integrative analysis of survival-associated gene sets in breast cancer.

    Science.gov (United States)

    Varn, Frederick S; Ung, Matthew H; Lou, Shao Ke; Cheng, Chao

    2015-03-12

    Patient gene expression information has recently become a clinical feature used to evaluate breast cancer prognosis. The emergence of prognostic gene sets that take advantage of these data has led to a rich library of information that can be used to characterize the molecular nature of a patient's cancer. Identifying robust gene sets that are consistently predictive of a patient's clinical outcome has become one of the main challenges in the field. We inputted our previously established BASE algorithm with patient gene expression data and gene sets from MSigDB to develop the gene set activity score (GSAS), a metric that quantitatively assesses a gene set's activity level in a given patient. We utilized this metric, along with patient time-to-event data, to perform survival analyses to identify the gene sets that were significantly correlated with patient survival. We then performed cross-dataset analyses to identify robust prognostic gene sets and to classify patients by metastasis status. Additionally, we created a gene set network based on component gene overlap to explore the relationship between gene sets derived from MSigDB. We developed a novel gene set based on this network's topology and applied the GSAS metric to characterize its role in patient survival. Using the GSAS metric, we identified 120 gene sets that were significantly associated with patient survival in all datasets tested. The gene overlap network analysis yielded a novel gene set enriched in genes shared by the robustly predictive gene sets. This gene set was highly correlated to patient survival when used alone. Most interestingly, removal of the genes in this gene set from the gene pool on MSigDB resulted in a large reduction in the number of predictive gene sets, suggesting a prominent role for these genes in breast cancer progression. The GSAS metric provided a useful medium by which we systematically investigated how gene sets from MSigDB relate to breast cancer patient survival. We used

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

    Science.gov (United States)

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

    2015-10-20

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

  8. Transcriptome analysis of Neisseria meningitidis in human whole blood and mutagenesis studies identify virulence factors involved in blood survival.

    Directory of Open Access Journals (Sweden)

    Hebert Echenique-Rivera

    2011-05-01

    Full Text Available During infection Neisseria meningitidis (Nm encounters multiple environments within the host, which makes rapid adaptation a crucial factor for meningococcal survival. Despite the importance of invasion into the bloodstream in the meningococcal disease process, little is known about how Nm adapts to permit survival and growth in blood. To address this, we performed a time-course transcriptome analysis using an ex vivo model of human whole blood infection. We observed that Nm alters the expression of ≈30% of ORFs of the genome and major dynamic changes were observed in the expression of transcriptional regulators, transport and binding proteins, energy metabolism, and surface-exposed virulence factors. In particular, we found that the gene encoding the regulator Fur, as well as all genes encoding iron uptake systems, were significantly up-regulated. Analysis of regulated genes encoding for surface-exposed proteins involved in Nm pathogenesis allowed us to better understand mechanisms used to circumvent host defenses. During blood infection, Nm activates genes encoding for the factor H binding proteins, fHbp and NspA, genes encoding for detoxifying enzymes such as SodC, Kat and AniA, as well as several less characterized surface-exposed proteins that might have a role in blood survival. Through mutagenesis studies of a subset of up-regulated genes we were able to identify new proteins important for survival in human blood and also to identify additional roles of previously known virulence factors in aiding survival in blood. Nm mutant strains lacking the genes encoding the hypothetical protein NMB1483 and the surface-exposed proteins NalP, Mip and NspA, the Fur regulator, the transferrin binding protein TbpB, and the L-lactate permease LctP were sensitive to killing by human blood. This increased knowledge of how Nm responds to adaptation in blood could also be helpful to develop diagnostic and therapeutic strategies to control the devastating

  9. Optimal exploitation of a renewable resource with stochastic nonconvex technology: An analysis of extinction and survival

    International Nuclear Information System (INIS)

    Mitra, Tapan; Roy, Santanu

    1992-11-01

    This paper analyzes the possibilities of extinction and survival of a renewable resource whose technology of reproduction is both stochastic and nonconvex. In particular, the production function is subject to random shocks over time and is allowed to be nonconcave, though it eventually exhibits bounded growth. The existence of a minimum biomass below which the resource can only decrease, is allowed for. Society harvests a part of the current stock every time period over an infinite horizon so as to maximize the expected discounted sum of one period social utilities from the harvested resource. The social utility function is strictly concave. The stochastic process of optimal stocks generated by the optimal stationary policy is analyzed. The nonconvexity in the optimization problem implies that the optimal policy functions are not 'well behaved'. The behaviour of the probability of extinction (and the expected time to extinction), as a function of initial stock, is characterized for various possible configurations of the optimal policy and the technology. Sufficient conditions on the utility and production functions and the rate of impatience, are specified in order to ensure survival of the resource with probability one from some stock level (the minimum safe standard of conservation). Sufficient conditions for almost sure extinction and almost sure survival from all stock levels are also specified. These conditions are related to the corresponding conditions derived in models with deterministic and/or convex technology. 4 figs., 29 refs

  10. Optimal exploitation of a renewable resource with stochastic nonconvex technology: An analysis of extinction and survival

    Energy Technology Data Exchange (ETDEWEB)

    Mitra, Tapan [Department of Economics, Cornell University, Ithaca, NY (United States); Roy, Santanu [Econometric Institute, Erasmus University, Rotterdam (Netherlands)

    1992-11-01

    This paper analyzes the possibilities of extinction and survival of a renewable resource whose technology of reproduction is both stochastic and nonconvex. In particular, the production function is subject to random shocks over time and is allowed to be nonconcave, though it eventually exhibits bounded growth. The existence of a minimum biomass below which the resource can only decrease, is allowed for. Society harvests a part of the current stock every time period over an infinite horizon so as to maximize the expected discounted sum of one period social utilities from the harvested resource. The social utility function is strictly concave. The stochastic process of optimal stocks generated by the optimal stationary policy is analyzed. The nonconvexity in the optimization problem implies that the optimal policy functions are not `well behaved`. The behaviour of the probability of extinction (and the expected time to extinction), as a function of initial stock, is characterized for various possible configurations of the optimal policy and the technology. Sufficient conditions on the utility and production functions and the rate of impatience, are specified in order to ensure survival of the resource with probability one from some stock level (the minimum safe standard of conservation). Sufficient conditions for almost sure extinction and almost sure survival from all stock levels are also specified. These conditions are related to the corresponding conditions derived in models with deterministic and/or convex technology. 4 figs., 29 refs.

  11. Survival, causes of death, and prognostic factors in systemic sclerosis: analysis of 947 Brazilian patients.

    Science.gov (United States)

    Sampaio-Barros, Percival D; Bortoluzzo, Adriana B; Marangoni, Roberta G; Rocha, Luiza F; Del Rio, Ana Paula T; Samara, Adil M; Yoshinari, Natalino H; Marques-Neto, João Francisco

    2012-10-01

    To analyze survival, prognostic factors, and causes of death in a large cohort of patients with systemic sclerosis (SSc). From 1991 to 2010, 947 patients with SSc were treated at 2 referral university centers in Brazil. Causes of death were considered SSc-related and non-SSc-related. Multiple logistic regression analysis was used to identify prognostic factors. Survival at 5 and 10 years was estimated using the Kaplan-Meier method. One hundred sixty-eight patients died during the followup. Among the 110 deaths considered related to SSc, there was predominance of lung (48.1%) and heart (24.5%) involvement. Most of the 58 deaths not related to SSc were caused by infection, cardiovascular or cerebrovascular disease, and cancer. Male sex, modified Rodnan skin score (mRSS) > 20, osteoarticular involvement, lung involvement, and renal crisis were the main prognostic factors associated to death. Overall survival rate was 90% for 5 years and 84% for 10 years. Patients presented worse prognosis if they had diffuse SSc (85% vs 92% at 5 yrs, respectively, and 77% vs 87% at 10 yrs, compared to limited SSc), male sex (77% vs 90% at 5 yrs and 64% vs 86% at 10 yrs, compared to female sex), and mRSS > 20 (83% vs 90% at 5 yrs and 66% vs 86% at 10 yrs, compared to mRSS < 20). Survival was worse in male patients with diffuse SSc, and lung and heart involvement represented the main causes of death in this South American series of patients with SSc.

  12. Association of body mass index and survival in pediatric leukemia: a meta-analysis.

    Science.gov (United States)

    Orgel, Etan; Genkinger, Jeanine M; Aggarwal, Divya; Sung, Lillian; Nieder, Michael; Ladas, Elena J

    2016-03-01

    Obesity is a worldwide epidemic in children and adolescents. Adult cohort studies have reported an association between higher body mass index (BMI) and increased leukemia-related mortality; whether a similar effect exists in childhood leukemia remains controversial. We conducted a meta-analysis to determine whether a higher BMI at diagnosis of pediatric acute lymphoblastic leukemia (ALL) or acute myeloid leukemia (AML) is associated with worse event-free survival (EFS), overall survival (OS), and cumulative incidence of relapse (CIR). We searched 4 electronic databases from inception through March 2015 without language restriction and included studies in pediatric ALL or AML (0-21 y of age) reporting BMI as a predictor of survival or relapse. Higher BMI, defined as obese (≥95%) or overweight/obese (≥85%), was compared with lower BMI [nonoverweight/obese (children with a higher BMI (RR: 1.35; 95% CI: 1.20, 1.51) than in those at a lower BMI. A higher BMI was associated with significantly increased mortality (RR: 1.31; 95% CI: 1.09, 1.58) and a statistically nonsignificant trend toward greater risk of relapse (RR: 1.17; 95% CI: 0.99, 1.38) compared with a lower BMI. In AML, a higher BMI was significantly associated with poorer EFS and OS (RR: 1.36; 95% CI: 1.16, 1.60 and RR: 1.56; 95% CI: 1.32, 1.86, respectively) than was a lower BMI. Higher BMI at diagnosis is associated with poorer survival in children with pediatric ALL or AML. © 2016 American Society for Nutrition.

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

    Science.gov (United States)

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

    2008-01-01

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

  14. Survival analysis using primary care electronic health record data: A systematic review of the literature.

    Science.gov (United States)

    Hodgkins, Adam Jose; Bonney, Andrew; Mullan, Judy; Mayne, Darren John; Barnett, Stephen

    2018-01-01

    An emerging body of research involves observational studies in which survival analysis is applied to data obtained from primary care electronic health records (EHRs). This systematic review of these studies examined the utility of using this approach. An electronic literature search of the Scopus, PubMed, Web of Science, CINAHL, and Cochrane databases was conducted. Search terms and exclusion criteria were chosen to select studies where survival analysis was applied to the data extracted wholly from EHRs used in primary care medical practice. A total of 46 studies that met the inclusion criteria for the systematic review were examined. All were published within the past decade (2005-2014) with a majority ( n = 26, 57%) being published between 2012 and 2014. Even though citation rates varied from nil to 628, over half ( n = 27, 59%) of the studies were cited 10 times or more. The median number of subjects was 18,042 with five studies including over 1,000,000 patients. Of the included studies, 35 (76%) were published in specialty journals and 11 (24%) in general medical journals. The many conditions studied largely corresponded well with conditions important to general practice. Survival analysis applied to primary care electronic medical data is a research approach that has been frequently used in recent times. The utility of this approach was demonstrated by the ability to produce research with large numbers of subjects, across a wide range of conditions and with the potential of a high impact. Importantly, primary care data were thus available to inform primary care practice.

  15. 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.

  16. STI571 (Gleevec) improves tumor growth delay and survival in irradiated mouse models of glioblastoma

    International Nuclear Information System (INIS)

    Geng Ling; Shinohara, Eric T.; Kim, Dong; Tan Jiahuai; Osusky, Kate; Shyr, Yu; Hallahan, Dennis E.

    2006-01-01

    Purpose: Glioblastoma multiforme (GBM) is a devastating brain neoplasm that is essentially incurable. Although radiation therapy prolongs survival, GBMs progress within areas of irradiation. Recent studies in invertebrates have shown that STI571 (Gleevec; Novartis, East Hanover, NJ) enhances the cytotoxicity of ionizing radiation. In the present study, the effectiveness of STI571 in combination with radiation was studied in mouse models of GBM. Methods and Materials: Murine GL261 and human D54 GBM cell lines formed tumors in brains and hind limbs of C57BL6 and nude mice, respectively. GL261 and D54 cells were treated with 5 μmol/L of STI571 for 1 h and/or irradiated with 3 Gy. Protein was analyzed by Western immunoblots probed with antibodies to caspase 3, cleaved caspase 3, phospho-Akt, Akt, and platelet-derived growth factor receptor (PDGFR) α and β. Tumor volumes were assessed in mice bearing GL261 or D54 tumors treated with 21 Gy administered in seven fractionated doses. Histologic sections from STI571-treated mice were stained with phospho-Akt and phospho-PDGFR β antibodies. Kaplan-Meier survival curves were used to study the response of mice bearing intracranial implants of GL261. Results: STI571 penetrated the blood-brain barrier, which resulted in a reduction in phospho-PDGFR in GBM. STI571-induced apoptosis in GBM was significantly enhanced by irradiation. STI571 combined with irradiation induced caspase 3 cleavage in GBM cells. Glioblastoma multiforme response to therapy correlated with an increase in tumor growth delay and survival when STI571 was administered in conjunction with daily irradiation. Conclusion: These findings suggest that STI571 has the potential to augment radiotherapy and thereby improve median survival

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

    Science.gov (United States)

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

    2018-01-01

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

  18. Demographic and Socio-economic Determinants of Birth Interval Dynamics in Manipur: A Survival Analysis

    Directory of Open Access Journals (Sweden)

    Sanajaoba Singh N,

    2011-01-01

    Full Text Available The birth interval is a major determinant of levels of fertility in high fertility populations. A house-to-house survey of 1225 women in Manipur, a tiny state in North Eastern India was carried out to investigate birth interval patterns and its determinants. Using survival analysis, among the nine explanatory variables of interest, only three factors – infant mortality, Lactation and use of contraceptive devices have highly significant effect (P<0.01 on the duration of birth interval and only three factors – age at marriage of wife, parity and sex of child are found to be significant (P<0.05 on the duration variable.

  19. Survival analysis to explore the characteristics of employee assistance program (EAP) referrals that remain employed.

    Science.gov (United States)

    Macdonald, S; Albert, W; Maynard, M; French, P

    1989-02-01

    This study examined characteristics of referrals to employee assistance programs (EAP) associated with subsequent termination of employment. As well, relationships between characteristics of the referrals and program characteristics were explored. Longitudinal data were collected at several time periods for 163 referrals to EAPs from five organizations. Survival analysis was conducted to determine which variables were associated with termination of employment. Females, cohabitating couples, and employees who worked for the organization for 5 or more years were most likely to remain employed. One interesting finding was that people with alcohol problems were significantly more likely to be formal referrals.

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

    Science.gov (United States)

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

    2014-02-01

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

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

    DEFF Research Database (Denmark)

    Martinussen, Torben; Vansteelandt, Stijn; Gerster, Mette

    2011-01-01

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

  2. The synthetic parasite-derived peptide GK1 increases survival in a preclinical mouse melanoma model.

    Science.gov (United States)

    Pérez-Torres, Armando; Vera-Aguilera, Jesús; Hernaiz-Leonardo, Juan Carlos; Moreno-Aguilera, Eduardo; Monteverde-Suarez, Diego; Vera-Aguilera, Carlos; Estrada-Bárcenas, Daniel

    2013-11-01

    The therapeutic efficacy of a synthetic parasite-derived peptide GK1, an immune response booster, was evaluated in a mouse melanoma model. This melanoma model correlates with human stage IIb melanoma, which is treated with wide surgical excision; a parallel study employing a surgical treatment was carried out as an instructive goal. C57BL/6 mice were injected subcutaneously in the flank with 2×10(5) B16-F10 murine melanoma cells. When the tumors reached 20 mm3, mice were separated into two different groups; the GK1 group, treated weekly with peritumoral injections of GK1 (10 μg/100 μL of sterile saline solution) and the control group, treated weekly with an antiseptic peritumoral injection of 100 μL of sterile saline solution without further intervention. All mice were monitored daily for clinical appearance, tumor size, and survival. Surgical treatment was performed in parallel when the tumor size was 20 mm3 (group A), 500 mm3 (group B), and >500 mm3 (group C). The GK1 peptide effectively increased the mean survival time by 9.05 days, corresponding to an increase of 42.58%, and significantly delayed tumor growth from day 3 to 12 of treatment. In addition, tumor necrosis was significantly increased (pcancers remains to be determined, and surgical removal remains a challenge for any new experimental treatment of melanoma in mouse models.

  3. PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer.

    Science.gov (United States)

    Wishart, Gordon C; Azzato, Elizabeth M; Greenberg, David C; Rashbass, Jem; Kearins, Olive; Lawrence, Gill; Caldas, Carlos; Pharoah, Paul D P

    2010-01-01

    The aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK. Using the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation. Differences in overall actual and predicted mortality were detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort.

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

    DEFF Research Database (Denmark)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2015-07-01

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

  6. Exponential Decay Nonlinear Regression Analysis of Patient Survival Curves: Preliminary Assessment in Non-Small Cell Lung Cancer

    Science.gov (United States)

    Stewart, David J.; Behrens, Carmen; Roth, Jack; Wistuba, Ignacio I.

    2010-01-01

    Background For processes that follow first order kinetics, exponential decay nonlinear regression analysis (EDNRA) may delineate curve characteristics and suggest processes affecting curve shape. We conducted a preliminary feasibility assessment of EDNRA of patient survival curves. Methods EDNRA was performed on Kaplan-Meier overall survival (OS) and time-to-relapse (TTR) curves for 323 patients with resected NSCLC and on OS and progression-free survival (PFS) curves from selected publications. Results and Conclusions In our resected patients, TTR curves were triphasic with a “cured” fraction of 60.7% (half-life [t1/2] >100,000 months), a rapidly-relapsing group (7.4%, t1/2=5.9 months) and a slowly-relapsing group (31.9%, t1/2=23.6 months). OS was uniphasic (t1/2=74.3 months), suggesting an impact of co-morbidities; hence, tumor molecular characteristics would more likely predict TTR than OS. Of 172 published curves analyzed, 72 (42%) were uniphasic, 92 (53%) were biphasic, 8 (5%) were triphasic. With first-line chemotherapy in advanced NSCLC, 87.5% of curves from 2-3 drug regimens were uniphasic vs only 20% of those with best supportive care or 1 drug (p<0.001). 54% of curves from 2-3 drug regimens had convex rapid-decay phases vs 0% with fewer agents (p<0.001). Curve convexities suggest that discontinuing chemotherapy after 3-6 cycles “synchronizes” patient progression and death. With postoperative adjuvant chemotherapy, the PFS rapid-decay phase accounted for a smaller proportion of the population than in controls (p=0.02) with no significant difference in rapid-decay t1/2, suggesting adjuvant chemotherapy may move a subpopulation of patients with sensitive tumors from the relapsing group to the cured group, with minimal impact on time to relapse for a larger group of patients with resistant tumors. In untreated patients, the proportion of patients in the rapid-decay phase increased (p=0.04) while rapid-decay t1/2 decreased (p=0.0004) with increasing

  7. Impact of T and N substage on survival and disease relapse in adjuvant rectal cancer: a pooled analysis

    International Nuclear Information System (INIS)

    Gunderson, Leonard L.; Sargent, Daniel J.; Tepper, Joel E.; O'Connell, Michael J.; Allmer, Cristine; Smalley, Steven R.; Martenson, James A.; Haller, Daniel G.; Mayer, Robert J.; Rich, Tyvin A.; Ajani, Jaffer A.; Macdonald, John S.; Goldberg, Richard M.

    2002-01-01

    Purpose: To determine the rates of survival and disease control by TNM and MAC stage in three randomized North American rectal adjuvant studies. Materials and Methods: Data were merged from 2551 eligible patients on NCCTG 79-47-51 (n=200), NCCTG 86-47-51 (n=656), and INT 114 (n=1695). All patients received postoperative radiation, and 96% were randomized to receive concomitant and maintenance chemotherapy. Five-year follow-up was available in 94% of patients and 7-yr follow-up in 84%. Kaplan-Meier curves were used to estimate the distribution of overall survival (OS) and disease-free survival (DFS), and p values were derived using the log-rank test. Time to local and distant relapse was estimated using cumulative incidence methodology. Analyses were adjusted for treatment effect using Cox proportional hazards models. Results: OS and DFS were dependent on both TN stage and NT stage (N substage within T stage and T substage within N stage). Even among N2 patients (4 or more LN+), T stage influenced 5-yr OS (T1-2, 69%; T3, 48%; T4, 38%). Three risk groups of patients were defined: (1) intermediate: T3N0, T1-2N1; (2) moderately high: T4N0, T1-2N2, T3N1; and (3) high: T3N2, T4N1, T4N2. For Group 1, 5-yr OS was 74% and 81%, and 5-yr DFS was 66% and 74%. For Group 2, 5-yr OS ranged from 61% to 69%, and for Group 3, OS ranged from 33% to 48%. Cumulative incidence rates of local relapse and distant metastases revealed similar differences by TN and NT stage, as seen in the survival analyses. Conclusion: Patients with a single high-risk factor of either extension beyond the rectal wall (T3N0) or nodal involvement (T1-2N1) have improved OS, DFS, and disease control when compared to those with both high risk factors. Different treatment strategies may be indicated for intermediate- (T3N0, T1-2N1) vs. moderately high or high-risk patients in view of differential survival and rates of relapse. For future trial design, it may be preferable to perform separate studies, or a planned

  8. Operations and Modeling Analysis

    Science.gov (United States)

    Ebeling, Charles

    2005-01-01

    The Reliability and Maintainability Analysis Tool (RMAT) provides NASA the capability to estimate reliability and maintainability (R&M) parameters and operational support requirements for proposed space vehicles based upon relationships established from both aircraft and Shuttle R&M data. RMAT has matured both in its underlying database and in its level of sophistication in extrapolating this historical data to satisfy proposed mission requirements, maintenance concepts and policies, and type of vehicle (i.e. ranging from aircraft like to shuttle like). However, a companion analyses tool, the Logistics Cost Model (LCM) has not reached the same level of maturity as RMAT due, in large part, to nonexistent or outdated cost estimating relationships and underlying cost databases, and it's almost exclusive dependence on Shuttle operations and logistics cost input parameters. As a result, the full capability of the RMAT/LCM suite of analysis tools to take a conceptual vehicle and derive its operations and support requirements along with the resulting operating and support costs has not been realized.

  9. 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

  10. Creation of a Prognostic Index for Spine Metastasis to Stratify Survival in Patients Treated With Spinal Stereotactic Radiosurgery: Secondary Analysis of Mature Prospective Trials

    International Nuclear Information System (INIS)

    Tang, Chad; Hess, Kenneth; Bishop, Andrew J.; Pan, Hubert Y.; Christensen, Eva N.; Yang, James N.; Tannir, Nizar; Amini, Behrang; Tatsui, Claudio; Rhines, Laurence; Brown, Paul; Ghia, Amol

    2015-01-01

    Purpose: There exists uncertainty in the prognosis of patients following spinal metastasis treatment. We sought to create a scoring system that stratifies patients based on overall survival. Methods and Materials: Patients enrolled in 2 prospective trials investigating stereotactic spine radiation surgery (SSRS) for spinal metastasis with ≥3-year follow-up were analyzed. A multivariate Cox regression model was used to create a survival model. Pretreatment variables included were race, sex, age, performance status, tumor histology, extent of vertebrae involvement, previous therapy at the SSRS site, disease burden, and timing of diagnosis and metastasis. Four survival groups were generated based on the model-derived survival score. Results: Median follow-up in the 206 patients included in this analysis was 70 months (range: 37-133 months). Seven variables were selected: female sex (hazard ratio [HR] = 0.7, P=.02), Karnofsky performance score (HR = 0.8 per 10-point increase above 60, P=.007), previous surgery at the SSRS site (HR = 0.7, P=.02), previous radiation at the SSRS site (HR = 1.8, P=.001), the SSRS site as the only site of metastatic disease (HR = 0.5, P=.01), number of organ systems involved outside of bone (HR = 1.4 per involved system, P<.001), and >5 year interval from initial diagnosis to detection of spine metastasis (HR = 0.5, P<.001). The median survival among all patients was 25.5 months and was significantly different among survival groups (in group 1 [excellent prognosis], median survival was not reached; group 2 reached 32.4 months; group 3 reached 22.2 months; and group 4 [poor prognosis] reached 9.1 months; P<.001). Pretreatment symptom burden was significantly higher in the patient group with poor survival than in the group with excellent survival (all metrics, P<.05). Conclusions: We developed the prognostic index for spinal metastases (PRISM) model, a new model that identified patient subgroups with poor and excellent prognoses

  11. Creation of a Prognostic Index for Spine Metastasis to Stratify Survival in Patients Treated With Spinal Stereotactic Radiosurgery: Secondary Analysis of Mature Prospective Trials

    Energy Technology Data Exchange (ETDEWEB)

    Tang, Chad [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Hess, Kenneth [Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Bishop, Andrew J.; Pan, Hubert Y.; Christensen, Eva N. [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Yang, James N. [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Tannir, Nizar [Department of Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Amini, Behrang [Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Tatsui, Claudio; Rhines, Laurence [Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Brown, Paul [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Ghia, Amol, E-mail: ajghia@mdanderson.org [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States)

    2015-09-01

    Purpose: There exists uncertainty in the prognosis of patients following spinal metastasis treatment. We sought to create a scoring system that stratifies patients based on overall survival. Methods and Materials: Patients enrolled in 2 prospective trials investigating stereotactic spine radiation surgery (SSRS) for spinal metastasis with ≥3-year follow-up were analyzed. A multivariate Cox regression model was used to create a survival model. Pretreatment variables included were race, sex, age, performance status, tumor histology, extent of vertebrae involvement, previous therapy at the SSRS site, disease burden, and timing of diagnosis and metastasis. Four survival groups were generated based on the model-derived survival score. Results: Median follow-up in the 206 patients included in this analysis was 70 months (range: 37-133 months). Seven variables were selected: female sex (hazard ratio [HR] = 0.7, P=.02), Karnofsky performance score (HR = 0.8 per 10-point increase above 60, P=.007), previous surgery at the SSRS site (HR = 0.7, P=.02), previous radiation at the SSRS site (HR = 1.8, P=.001), the SSRS site as the only site of metastatic disease (HR = 0.5, P=.01), number of organ systems involved outside of bone (HR = 1.4 per involved system, P<.001), and >5 year interval from initial diagnosis to detection of spine metastasis (HR = 0.5, P<.001). The median survival among all patients was 25.5 months and was significantly different among survival groups (in group 1 [excellent prognosis], median survival was not reached; group 2 reached 32.4 months; group 3 reached 22.2 months; and group 4 [poor prognosis] reached 9.1 months; P<.001). Pretreatment symptom burden was significantly higher in the patient group with poor survival than in the group with excellent survival (all metrics, P<.05). Conclusions: We developed the prognostic index for spinal metastases (PRISM) model, a new model that identified patient subgroups with poor and excellent prognoses.

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

    Science.gov (United States)

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

    2016-01-01

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

  13. Missing data and censoring in the analysis of progression-free survival in oncology clinical trials.

    Science.gov (United States)

    Denne, J S; Stone, A M; Bailey-Iacona, R; Chen, T-T

    2013-01-01

    Progression-free survival (PFS) is increasingly used as a primary endpoint in oncology clinical trials. However, trial conduct is often such that PFS data on some patients may be partially missing either due to incomplete follow-up for progression, or due to data that may be collected but confounded by patients stopping randomized therapy or starting alternative therapy prior to progression. Regulatory guidance on how to handle these patients in the analysis and whether to censor these patients differs between agencies. We present results of a reanalysis of 28 Phase III trials from 12 companies or institutions performed by the Pharmaceutical Research and Manufacturers Association-sponsored PFS Expert Team. We show that analyses not adhering to the intention-to-treat principle tend to give hazard ratio estimates further from unity and describe several factors associated with this shift. We present illustrative simulations to support these findings and provide recommendations for the analysis of PFS.

  14. Association between response rates and survival outcomes in patients with newly diagnosed multiple myeloma. A systematic review and meta-regression analysis.

    Science.gov (United States)

    Mainou, Maria; Madenidou, Anastasia-Vasiliki; Liakos, Aris; Paschos, Paschalis; Karagiannis, Thomas; Bekiari, Eleni; Vlachaki, Efthymia; Wang, Zhen; Murad, Mohammad Hassan; Kumar, Shaji; Tsapas, Apostolos

    2017-06-01

    We performed a systematic review and meta-regression analysis of randomized control trials to investigate the association between response to initial treatment and survival outcomes in patients with newly diagnosed multiple myeloma (MM). Response outcomes included complete response (CR) and the combined outcome of CR or very good partial response (VGPR), while survival outcomes were overall survival (OS) and progression-free survival (PFS). We used random-effect meta-regression models and conducted sensitivity analyses based on definition of CR and study quality. Seventy-two trials were included in the systematic review, 63 of which contributed data in meta-regression analyses. There was no association between OS and CR in patients without autologous stem cell transplant (ASCT) (regression coefficient: .02, 95% confidence interval [CI] -0.06, 0.10), in patients undergoing ASCT (-.11, 95% CI -0.44, 0.22) and in trials comparing ASCT with non-ASCT patients (.04, 95% CI -0.29, 0.38). Similarly, OS did not correlate with the combined metric of CR or VGPR, and no association was evident between response outcomes and PFS. Sensitivity analyses yielded similar results. This meta-regression analysis suggests that there is no association between conventional response outcomes and survival in patients with newly diagnosed MM. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  15. A multi-year analysis of passage and survival at McNary Dam, 2004-09

    Science.gov (United States)

    Adams, Noah S.; Walker, C.E.; Perry, R.W.

    2011-01-01

    We analyzed 6 years (2004–09) of passage and survival data collected at McNary Dam to determine how dam operations and environmental conditions affect passage and survival of juvenile salmonids. A multinomial logistic regression was used to examine how environmental variables and dam operations relate to passage behavior of juvenile salmonids at McNary Dam. We used the Cormack-Jolly-Seber release-recapture model to determine how the survival of juvenile salmonids passing through McNary Dam relates to environmental variables and dam operations. Total project discharge and the proportion of flow passing the spillway typically had a positive effect on survival for all species and routes. As the proportion of water through the spillway increased, the number of fish passing the spillway increased, as did overall survival. Additionally, survival generally was higher at night. There was no meaningful difference in survival for fish that passed through the north or south portions of the spillway or powerhouse. Similarly, there was no difference in survival for fish released in the north, middle, or south portions of the tailrace. For subyearling Chinook salmon migrating during the summer season, increased temperatures had a drastic effect on passage and survival. As temperature increased, survival of subyearling Chinook salmon decreased through all passage routes and the number of fish that passed through the turbines increased. During years when the temporary spillway weirs (TSWs) were installed, passage through the spillway increased for spring migrants. However, due to the changes made in the location of the TSW between years and the potential effect of other confounding environmental conditions, it is not certain if the increase in spillway passage was due solely to the presence of the TSWs. The TSWs appeared to improve forebay survival during years when they were operated.

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2011-11-19

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

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

    Science.gov (United States)

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

    2016-02-01

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

  19. Nutritional intra-amniotic therapy increases survival in a rabbit model of fetal growth restriction

    Science.gov (United States)

    Illa, Miriam; Pla, Laura; Zamora, Monica; Crispi, Fatima; Gratacos, Eduard

    2018-01-01

    Objective To evaluate the perinatal effects of a prenatal therapy based on intra-amniotic nutritional supplementation in a rabbit model of intrauterine growth restriction (IUGR). Methods IUGR was surgically induced in pregnant rabbits at gestational day 25 by ligating 40–50% of uteroplacental vessels of each gestational sac. At the same time, modified-parenteral nutrition solution (containing glucose, amino acids and electrolytes) was injected into the amniotic sac of nearly half of the IUGR fetuses (IUGR-T group n = 106), whereas sham injections were performed in the rest of fetuses (IUGR group n = 118). A control group without IUGR induction but sham injection was also included (n = 115). Five days after the ligation procedure, a cesarean section was performed to evaluate fetal cardiac function, survival and birth weight. Results Survival was significantly improved in the IUGR fetuses that were treated with intra-amniotic nutritional supplementation as compared to non-treated IUGR animals (survival rate: controls 71% vs. IUGR 44% p = 0.003 and IUGR-T 63% vs. IUGR 44% p = 0.02), whereas, birth weight (controls mean 43g ± SD 9 vs. IUGR 36g ± SD 9 vs. IUGR-T 35g ± SD 8, p = 0.001) and fetal cardiac function were similar among the IUGR groups. Conclusion Intra-amniotic injection of a modified-parenteral nutrient solution appears to be a promising therapy for reducing mortality among IUGR. These results provide an opportunity to develop new intra-amniotic nutritional strategies to reach the fetus by bypassing the placental insufficiency. PMID:29466434

  20. Impact of anastomotic leak on recurrence and survival after colorectal cancer surgery: a BioGrid Australia analysis.

    Science.gov (United States)

    Sammour, Tarik; Hayes, Ian P; Jones, Ian T; Steel, Malcolm C; Faragher, Ian; Gibbs, Peter

    2018-01-01

    There is conflicting evidence regarding the oncological impact of anastomotic leak following colorectal cancer surgery. This study aims to test the hypothesis that anastomotic leak is independently associated with local recurrence and overall and cancer-specific survival. Analysis of prospectively collected data from multiple centres in Victoria between 1988 and 2015 including all patients who underwent colon or rectal resection for cancer with anastomosis was presented. Overall and cancer-specific survival rates and rates of local recurrence were compared using Cox regression analysis. A total of 4892 patients were included, of which 2856 had completed 5-year follow-up. The overall anastomotic leak rate was 4.0%. Cox regression analysis accounting for differences in age, sex, body mass index, American Society of Anesthesiologists score and tumour stage demonstrated that anastomotic leak was associated with significantly worse 5-year overall survival (χ 2 = 6.459, P = 0.011) for colon cancer, but only if early deaths were included. There was no difference in 5-year colon cancer-specific survival (χ 2 = 0.582, P = 0.446) or local recurrence (χ 2 = 0.735, P = 0.391). For rectal cancer, there was no difference in 5-year overall survival (χ 2 = 0.266, P = 0.606), cancer-specific survival (χ 2 = 0.008, P = 0.928) or local recurrence (χ 2 = 2.192, P = 0.139). Anastomotic leak may reduce 5-year overall survival in colon cancer patients but does not appear to influence the 5-year overall survival in rectal cancer patients. There was no effect on local recurrence or cancer-specific survival. © 2016 Royal Australasian College of Surgeons.

  1. 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

  2. Effect of donor ethnicity on kidney survival in different recipient pairs: an analysis of the OPTN/UNOS database.

    Science.gov (United States)

    Callender, C O; Cherikh, W S; Traverso, P; Hernandez, A; Oyetunji, T; Chang, D

    2009-12-01

    Previous multivariate analysis performed between April 1, 1994, and December 31, 2000 from the Organ Procurement Transplant Network/United Network for Organ Sharing (OPTN/UNOS) database has shown that kidneys from black donors were associated with lower graft survival. We compared graft and patient survival of different kidney donor-to-recipient ethnic combinations to see if this result still holds on a recent cohort of US kidney transplants. We included 72,495 recipients of deceased and living donor kidney alone transplants from 2001 to 2005. A multivariate Cox regression method was used to analyze the effect of donor-recipient ethnicity on graft and patient survival within 5 years of transplant, and to adjust for the effect of other donor, recipient, and transplant characteristics. Results are presented as hazard ratios (HR) with the 95% confidence limit (CL) and P values. Adjusted HRs of donor-recipient patient survival were: white to white (1); and white to black (1.22; P = .001). Graft survival HRs were black to black (1.40; P recipients. The graft and patient survival rates for Asian and Latino/Hispanic recipients, however, were not affected by donor ethnicity. This analysis underscores the need for research to better understand the reasons for these disparities and how to improve the posttransplant graft survival rates of black kidney recipients.

  3. Explorative data analysis of MCL reveals gene expression networks implicated in survival and prognosis supported by explorative CGH analysis

    International Nuclear Information System (INIS)

    Blenk, Steffen; Engelmann, Julia C; Pinkert, Stefan; Weniger, Markus; Schultz, Jörg; Rosenwald, Andreas; Müller-Hermelink, Hans K; Müller, Tobias; Dandekar, Thomas

    2008-01-01

    Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmark translocation t(11;14) that is present in most cases with few exceptions. Both gene expression and comparative genomic hybridization (CGH) data vary considerably between patients with implications for their prognosis. We compare patients over and below the median of survival. Exploratory principal component analysis of gene expression data showed that the second principal component correlates well with patient survival. Explorative analysis of CGH data shows the same correlation. On chromosome 7 and 9 specific genes and bands are delineated which improve prognosis prediction independent of the previously described proliferation signature. We identify a compact survival predictor of seven genes for MCL patients. After extensive re-annotation using GEPAT, we established protein networks correlating with prognosis. Well known genes (CDC2, CCND1) and further proliferation markers (WEE1, CDC25, aurora kinases, BUB1, PCNA, E2F1) form a tight interaction network, but also non-proliferative genes (SOCS1, TUBA1B CEBPB) are shown to be associated with prognosis. Furthermore we show that aggressive MCL implicates a gene network shift to higher expressed genes in late cell cycle states and refine the set of non-proliferative genes implicated with bad prognosis in MCL. The results from explorative data analysis of gene expression and CGH data are complementary to each other. Including further tests such as Wilcoxon rank test we point both to proliferative and non-proliferative gene networks implicated in inferior prognosis of MCL and identify suitable markers both in gene expression and CGH data

  4. Survival Analysis of Occipital Nerve Stimulator Leads Placed under Fluoroscopic Guidance with and without Ultrasonography.

    Science.gov (United States)

    Jones, James H; Brown, Alison; Moyse, Daniel; Qi, Wenjing; Roy, Lance

    2017-11-01

    Electrical stimulation of the greater occipital nerves is performed to treat pain secondary to chronic daily headaches and occipital neuralgia. The use of fluoroscopy alone to guide the surgical placement of electrodes near the greater occipital nerves disregards the impact of tissue planes on lead stability and stimulation efficacy. We hypothesized that occipital neurostimulator (ONS) leads placed with ultrasonography combined with fluoroscopy would demonstrate increased survival rates and times when compared to ONS leads placed with fluoroscopy alone. A 2-arm retrospective chart review. A single academic medical center. This retrospective chart review analyzed the procedure notes and demographic data of patients who underwent the permanent implant of an ONS lead between July 2012 and August 2015. Patient data included the diagnosis (reason for implant), smoking tobacco use, disability, and age. ONS lead data included the date of permanent implant, the imaging modality used during permanent implant (fluoroscopy with or without ultrasonography), and, if applicable, the date and reason for lead removal. A total of 21 patients (53 leads) were included for the review. Chi-squared tests, Fishers exact tests, 2-sample t-tests, and Wilcoxon rank-sum tests were used to compare fluoroscopy against combined fluoroscopy and ultrasonography as implant methods with respect to patient demographics. These tests were also used to evaluate the primary aim of this study, which was to compare the survival rates and times of ONS leads placed with combined ultrasonography and fluoroscopy versus those placed with fluoroscopy alone. Survival analysis was used to assess the effect of implant method, adjusted for patient demographics (age, smoking tobacco use, and disability), on the risk of lead explant. Data from 21 patients were collected, including a total of 53 ONS leads. There was no statistically significant difference in the lead survival rate or time, disability, or patient age

  5. Bronchus-associated lymphoid tissue (BALT and survival in a vaccine mouse model of tularemia.

    Directory of Open Access Journals (Sweden)

    Damiana Chiavolini

    2010-06-01

    Full Text Available Francisella tularensis causes severe pulmonary disease, and nasal vaccination could be the ideal measure to effectively prevent it. Nevertheless, the efficacy of this type of vaccine is influenced by the lack of an effective mucosal adjuvant.Mice were immunized via the nasal route with lipopolysaccharide isolated from F. tularensis and neisserial recombinant PorB as an adjuvant candidate. Then, mice were challenged via the same route with the F. tularensis attenuated live vaccine strain (LVS. Mouse survival and analysis of a number of immune parameters were conducted following intranasal challenge. Vaccination induced a systemic antibody response and 70% of mice were protected from challenge as showed by their improved survival and weight regain. Lungs from mice recovering from infection presented prominent lymphoid aggregates in peribronchial and perivascular areas, consistent with the location of bronchus-associated lymphoid tissue (BALT. BALT areas contained proliferating B and T cells, germinal centers, T cell infiltrates, dendritic cells (DCs. We also observed local production of antibody generating cells and homeostatic chemokines in BALT areas.These data indicate that PorB might be an optimal adjuvant candidate for improving the protective effect of F. tularensis antigens. The presence of BALT induced after intranasal challenge in vaccinated mice might play a role in regulation of local immunity and long-term protection, but more work is needed to elucidate mechanisms that lead to its formation.

  6. Validation of Progression‐Free Survival as a Surrogate Endpoint for Overall Survival in Malignant Mesothelioma: Analysis of Cancer and Leukemia Group B and North Central Cancer Treatment Group (Alliance) Trials

    Science.gov (United States)

    Wang, Xiaoyi; Hodgson, Lydia; George, Stephen L.; Sargent, Daniel J.; Foster, Nate R.; Ganti, Apar Kishor; Stinchcombe, Thomas E.; Crawford, Jeffrey; Kratzke, Robert; Adjei, Alex A.; Kindler, Hedy L.; Vokes, Everett E.; Pang, Herbert

    2017-01-01

    Abstract Purpose. The aim of this study was to investigate whether progression‐free survival (PFS) can be considered a surrogate endpoint for overall survival (OS) in malignant mesothelioma. Materials and Methods. Individual data were collected from 15 Cancer and Leukemia Group B (615 patients) and 2 North Central Cancer Treatment Group (101 patients) phase II trials. The effects of 5 risk factors for OS and PFS, including age, histology, performance status (PS), white blood cell count, and European Organisation for Research and Treatment of Cancer (EORTC) risk score, were used in the analysis. Individual‐level surrogacy was assessed by Kendall's tau through a Clayton bivariate Copula survival (CBCS) model. Summary‐level surrogacy was evaluated via the association between logarithms of the hazard ratio (log HR)—log HROS and log HRPFS—measured in R2 from a weighted least‐square (WLS) regression model and the CBCS model. Results. The median PFS for all patients was 3.0 months (95% confidence interval [CI], 2.8–3.5 months) and the median OS was 7.2 months (95% CI, 6.5–8.0 months). Moderate correlations between PFS and OS were observed across all risk factors at the individual level, with Kendall's tau ranging from 0.46 to 0.47. The summary‐level surrogacy varied among risk factors. The Copula R2 ranged from 0.51 for PS to 0.78 for histology. The WLS R2 ranged from 0.26 for EORTC and PS to 0.67 for age. Conclusions. The analyses demonstrated low to moderate individual‐level surrogacy between PFS and OS. At the summary level, the surrogacy between PFS and OS varied significantly across different risk factors. With a short postprogression survival and a moderate correlation between PFS and OS, there is no evidence that PFS is a valid surrogate endpoint for OS in malignant mesothelioma. Implications for Practice. For better disease management and for more efficient clinical trial designs, it is important to know if progression‐free survival (PFS) is

  7. Survival of patients with non-small cell lung cancer without treatment: a systematic review and meta-analysis

    Directory of Open Access Journals (Sweden)

    Wao Hesborn

    2013-02-01

    Full Text Available Abstract Background Lung cancer is considered a terminal illness with a five-year survival rate of about 16%. Informed decision-making related to the management of a disease requires accurate prognosis of the disease with or without treatment. Despite the significance of disease prognosis in clinical decision-making, systematic assessment of prognosis in patients with lung cancer without treatment has not been performed. We conducted a systematic review and meta-analysis of the natural history of patients with confirmed diagnosis of lung cancer without active treatment, to provide evidence-based recommendations for practitioners on management decisions related to the disease. Specifically, we estimated overall survival when no anticancer therapy is provided. Methods Relevant studies were identified by search of electronic databases and abstract proceedings, review of bibliographies of included articles, and contacting experts in the field. All prospective or retrospective studies assessing prognosis of lung cancer patients without treatment were eligible for inclusion. Data on mortality was extracted from all included studies. Pooled proportion of mortality was calculated as a back-transform of the weighted mean of the transformed proportions using the random-effects model. To perform meta-analysis of median survival, published methods were used to pool the estimates as mean and standard error under the random-effects model. Methodological quality of the studies was examined. Results Seven cohort studies (4,418 patients and 15 randomized controlled trials (1,031 patients were included in the meta-analysis. All studies assessed mortality without treatment in patients with non-small cell lung cancer (NSCLC. The pooled proportion of mortality without treatment in cohort studies was 0.97 (95% CI: 0.96 to 0.99 and 0.96 in randomized controlled trials (95% CI: 0.94 to 0.98 over median study periods of eight and three years, respectively. When data

  8. The analysis of survival data in nephrology: basic concepts and methods of Cox regression

    NARCIS (Netherlands)

    van Dijk, Paul C.; Jager, Kitty J.; Zwinderman, Aeilko H.; Zoccali, Carmine; Dekker, Friedo W.

    2008-01-01

    How much does the survival of one group differ from the survival of another group? How do differences in age in these two groups affect such a comparison? To obtain a quantity to compare the survival of different patient groups and to account for confounding effects, a multiple regression technique

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

    Directory of Open Access Journals (Sweden)

    Winyoo Chowanadisai

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

  10. Method for widespread microRNA-155 inhibition prolongs survival in ALS-model mice

    Science.gov (United States)

    Koval, Erica D.; Shaner, Carey; Zhang, Peter; du Maine, Xavier; Fischer, Kimberlee; Tay, Jia; Chau, B. Nelson; Wu, Gregory F.; Miller, Timothy M.

    2013-01-01

    microRNAs (miRNAs) are dysregulated in a variety of disease states, suggesting that this newly discovered class of gene expression repressors may be viable therapeutic targets. A microarray of miRNA changes in ALS-model superoxide dismutase 1 (SOD1)G93A rodents identified 12 miRNAs as significantly changed. Six miRNAs tested in human ALS tissues were confirmed increased. Specifically, miR-155 was increased 5-fold in mice and 2-fold in human spinal cords. To test miRNA inhibition in the central nervous system (CNS) as a potential novel therapeutic, we developed oligonucleotide-based miRNA inhibitors (anti-miRs) that could inhibit miRNAs throughout the CNS and in the periphery. Anti-miR-155 caused global derepression of targets in peritoneal macrophages and, following intraventricular delivery, demonstrated widespread functional distribution in the brain and spinal cord. After treating SOD1G93A mice with anti-miR-155, we significantly extended survival by 10 days and disease duration by 15 days (38%) while a scrambled control anti-miR did not significantly improve survival or disease duration. Therefore, antisense oligonucleotides may be used to successfully inhibit miRNAs throughout the brain and spinal cord, and miR-155 is a promising new therapeutic target for human ALS. PMID:23740943

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

    Science.gov (United States)

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

    2014-01-01

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

  12. Imaging Flow Cytometry Analysis to Identify Differences of Survival Motor Neuron Protein Expression in Patients With Spinal Muscular Atrophy.

    Science.gov (United States)

    Arakawa, Reiko; Arakawa, Masayuki; Kaneko, Kaori; Otsuki, Noriko; Aoki, Ryoko; Saito, Kayoko

    2016-08-01

    Spinal muscular atrophy is a neurodegenerative disorder caused by the deficient expression of survival motor neuron protein in motor neurons. A major goal of disease-modifying therapy is to increase survival motor neuron expression. Changes in survival motor neuron protein expression can be monitored via peripheral blood cells in patients; therefore we tested the sensitivity and utility of imaging flow cytometry for this purpose. After the immortalization of peripheral blood lymphocytes from a human healthy control subject and two patients with spinal muscular atrophy type 1 with two and three copies of SMN2 gene, respectively, we used imaging flow cytometry analysis to identify significant differences in survival motor neuron expression. A bright detail intensity analysis was used to investigate differences in the cellular localization of survival motor neuron protein. Survival motor neuron expression was significantly decreased in cells derived from patients with spinal muscular atrophy relative to those derived from a healthy control subject. Moreover, survival motor neuron expression correlated with the clinical severity of spinal muscular atrophy according to SMN2 copy number. The cellular accumulation of survival motor neuron protein was also significantly decreased in cells derived from patients with spinal muscular atrophy relative to those derived from a healthy control subject. The benefits of imaging flow cytometry for peripheral blood analysis include its capacities for analyzing heterogeneous cell populations; visualizing cell morphology; and evaluating the accumulation, localization, and expression of a target protein. Imaging flow cytometry analysis should be implemented in future studies to optimize its application as a tool for spinal muscular atrophy clinical trials. Copyright © 2016 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

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

    2017-09-01

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

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

    Science.gov (United States)

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

    2018-01-01

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

  15. Snow model analysis.

    Science.gov (United States)

    2014-01-01

    This study developed a new snow model and a database which warehouses geometric, weather and traffic : data on New Jersey highways. The complexity of the model development lies in considering variable road : width, different spreading/plowing pattern...

  16. Survival benefit of postoperative radiation in papillary meningioma: Analysis of the National Cancer Data Base.

    Science.gov (United States)

    Sumner, Whitney A; Amini, Arya; Hankinson, Todd C; Foreman, Nicholas K; Gaspar, Laurie E; Kavanagh, Brian D; Karam, Sana D; Rusthoven, Chad G; Liu, Arthur K

    2017-01-01

    Papillary meningioma represents a rare subset of World Health Organization (WHO) Grade III meningioma that portends an overall poor prognosis. There is relatively limited data regarding the benefit of postoperative radiation therapy (PORT). We used the National Cancer Data Base (NCDB) to compare overall survival (OS) outcomes of surgically resected papillary meningioma cases undergoing PORT compared to post-operative observation. The NCDB was queried for patients with papillary meningioma, diagnosed between 2004 and 2013, who underwent upfront surgery with or without PORT. Overall survival (OS) was determined using the Kaplan-Meier method. Univariate (UVA) and multivariate (MVA) analyses were performed. In total, 190 patients were identified; 89 patients underwent PORT, 101 patients were observed. Eleven patients received chemotherapy (6 with PORT, 5 without). 2-Year OS was significantly improved with PORT vs. no PORT (93.0% vs. 74.4%), as was 5-year OS (78.5% vs. 62.5%) (hazard ratio [HR], 0.48; 95% confidence interval [CI], 0.27-0.85; p  = 0.01). On MVA, patients receiving PORT had improved OS compared to observation (HR, 0.41; 95% CI, 0.22-0.76; p  = 0.005). On subset analysis by age group, the benefit of PORT vs. no PORT was significant in patients ≤18 years ( n  = 13), with 2-year OS of 85.7% vs. 50.0% (HR, 0.08; 95% CI, 0.01-0.80; p  = 0.032) and for patients >18 years ( n  = 184), with 2-year OS of 94.7% vs. 76.1% (HR, 0.55; 95% CI, 0.31-1.00; p  = 0.049), respectively. In this large contemporary analysis, PORT was associated with improved survival for both adult and pediatric patients with papillary meningioma. PORT should be considered in those who present with this rare, aggressive tumor.

  17. Recursive partitioning analysis (RPA) classification predicts survival in patients with brain metastases from sarcoma.

    Science.gov (United States)

    Grossman, Rachel; Ram, Zvi

    2014-12-01

    Sarcoma rarely metastasizes to the brain, and there are no specific treatment guidelines for these tumors. The recursive partitioning analysis (RPA) classification is a well-established prognostic scale used in many malignancies. In this study we assessed the clinical characteristics of metastatic sarcoma to the brain and the validity of the RPA classification system in a subset of 21 patients who underwent surgical resection of metastatic sarcoma to the brain We retrospectively analyzed the medical, radiological, surgical, pathological, and follow-up clinical records of 21 patients who were operated for metastatic sarcoma to the brain between 1996 and 2012. Gliosarcomas, sarcomas of the head and neck with local extension into the brain, and metastatic sarcomas to the spine were excluded from this reported series. The patients' mean age was 49.6 ± 14.2 years (range, 25-75 years) at the time of diagnosis. Sixteen patients had a known history of systemic sarcoma, mostly in the extremities, and had previously received systemic chemotherapy and radiation therapy for their primary tumor. The mean maximal tumor diameter in the brain was 4.9 ± 1.7 cm (range 1.7-7.2 cm). The group's median preoperative Karnofsky Performance Scale was 80, with 14 patients presenting with Karnofsky Performance Scale of 70 or greater. The median overall survival was 7 months (range 0.2-204 months). The median survival time stratified by the Radiation Therapy Oncology Group RPA classes were 31, 7, and 2 months for RPA class I, II, and III, respectively (P = 0.0001). This analysis is the first to support the prognostic utility of the Radiation Therapy Oncology Group RPA classification for sarcoma brain metastases and may be used as a treatment guideline tool in this rare disease. Copyright © 2014 Elsevier Inc. All rights reserved.

  18. Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees

    Science.gov (United States)

    Kenah, Eben; Britton, Tom; Halloran, M. Elizabeth; Longini, Ira M.

    2016-01-01

    Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission. In a survival analysis framework, estimation of transmission parameters is based on sums or averages over the possible transmission trees. A phylogeny can increase the precision of these estimates by providing partial information about who infected whom. The leaves of the phylogeny represent sampled pathogens, which have known hosts. The interior nodes represent common ancestors of sampled pathogens, which have unknown hosts. Starting from assumptions about disease biology and epidemiologic study design, we prove that there is a one-to-one correspondence between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person, place, and time. We develop algorithms to enumerate these transmission trees and show these can be used to calculate likelihoods that incorporate both epidemiologic data and a phylogeny. A simulation study confirms that this leads to more efficient estimates of hazard ratios for infectiousness and baseline hazards of infectious contact, and we use these methods to analyze data from a foot-and-mouth disease virus outbreak in the United Kingdom in 2001. These results demonstrate the importance of data on individuals who escape infection, which is often overlooked. The combination of survival analysis and algorithms linking phylogenies to transmission trees is a rigorous but flexible statistical foundation for molecular infectious disease epidemiology. PMID:27070316

  19. Estimation of Unemployment Duration in Botoşani County Using Survival Analysis

    Directory of Open Access Journals (Sweden)

    Darabă Gabriel

    2017-01-01

    Full Text Available In this paper we aim at estimating the unemployment duration in Botosani County in order tostudy the impact of individual characteristics (gender, age, place of residence, unemploymentbenefit, etc. on the length of unemployment spells. We use Cox regression model to measure theeffects of gender, age, residential environment, etc. on the hazard rate of leaving unemploymentandKaplan-Meier estimator to compare survival probabilities among different categories ofunemployed persons. The study is carried out on a sample of 200 unemployment spellsregisteredwith the Employment Agency of Botoşani County from January 2012 to December 2015. Theresults reveal that place of residence, unemployment benefit and unemployed category have asignificant impact on unemployment spells.

  20. Can rural electric cooperatives survive in a restructured US electric market? An empirical analysis

    International Nuclear Information System (INIS)

    Greer, Monica Lynne

    2003-01-01

    This paper examines the ability of rural electric distribution cooperatives to continue operating in their present form in a restructured electricity market. More specifically, I develop and estimate a quadratic cost model, which, unlike many of the cost functions employed in studies of this nature, conforms to all of the properties of a proper cost function. Using 1996 data, I find that these firms are not operating in a cost-minimizing fashion. This finding seems to occur because each is too small in terms of the quantity of electricity distributed. As a result, mergers between these firms could yield substantial savings and help ensure their survival in their present form in a deregulated market

  1. Relationships between mastitis and functional longevity in Danish Black and White dairy cattle estimated using survival analysis.

    Science.gov (United States)

    Neerhof, H J; Madsen, P; Ducrocq, V P; Vollema, A R; Jensen, J; Korsgaard, I R

    2000-05-01

    The relationship between mastitis and functional longevity was assessed with survival analysis on data of Danish Black and White dairy cows. Different methods of including the effect of mastitis treatment on the culling decision by a farmer in the model were compared. The model in which mastitis treatment was assumed to have an effect on functional longevity until the end of the lactation had the highest likelihood, and the model in which mastitis treatment had an effect for only a short period had the lowest likelihood. A cow with mastitis had 1.69 times greater risk of being culled than did a healthy herdmate with all other effects being the same. A model without mastitis treatment was used to predict transmitting abilities of bulls for risk of being culled, based on longevity records of their daughters, and was expressed in terms of risk of being culled. The correlation between the risk of being culled and the national evaluations of the bulls for mastitis resistance was approximately -0.4, indicating that resistance against mastitis was genetically correlated with a lower risk of being culled and, thus, a longer functional length of productive life.

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

    Directory of Open Access Journals (Sweden)

    François Niragire

    2017-05-01

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

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

    Science.gov (United States)

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

    2017-05-11

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

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

    Science.gov (United States)

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

    2011-12-01

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

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

    International Nuclear Information System (INIS)

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

    1982-01-01

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

  6. Analysis of survival for patients with chronic kidney disease primarily related to renal cancer surgery.

    Science.gov (United States)

    Wu, Jitao; Suk-Ouichai, Chalairat; Dong, Wen; Antonio, Elvis Caraballo; Derweesh, Ithaar H; Lane, Brian R; Demirjian, Sevag; Li, Jianbo; Campbell, Steven C

    2018-01-01

    To evaluate predictors of long-term survival for patients with chronic kidney disease primarily due to surgery (CKD-S). Patients with CKD-S have generally good survival that approximates patients who do not have CKD even after renal cancer surgery (RCS), yet there may be heterogeneity within this cohort. From 1997 to 2008, 4 246 patients underwent RCS at our centre. The median (interquartile range [IQR]) follow-up was 9.4 (7.3-11.0) years. New baseline glomerular filtration rate (GFR) was defined as highest GFR between nadir and 6 weeks after RCS. We retrospectively evaluated three cohorts: no-CKD (new baseline GFR of ≥60 mL/min/1.73 m 2 ); CKD-S (new baseline GFR of cancer-related survival (NRCRS) for the CKD-S cohort. Kaplan-Meier analysis assessed the longitudinal impact of new baseline GFR (45-60 mL/min/1.73 m 2 vs <45 mL/min/1.73 m 2 ) and Cox regression evaluated relative impact of preoperative GFR, new baseline GFR, and relevant demographics/comorbidities. Of the 4 246 patients who underwent RCS, 931 had CKD-S and 1 113 had CKD-M/S, whilst 2 202 had no-CKD even after RCS. Partial/radical nephrectomy (PN/RN) was performed in 54%/46% of the patients, respectively. For CKD-S, 641 patients had a new baseline GFR of 45-60 mL/min/1.73 m 2 and 290 had a new baseline GFR of <45 mL/min/1.73 m 2 . Kaplan-Meier analysis showed significantly reduced NRCRS for patients with CKD-S with a GFR of <45 mL/min/1.73 m 2 compared to those with no-CKD or CKD-S with a GFR of 45-60 mL/min/1.73 m 2 (both P ≤ 0.004), and competing risk analysis confirmed this (P < 0.001). Age, gender, heart disease, and new baseline GFR were all associated independently with NRCRS for patients with CKD-S (all P ≤ 0.02). Our data suggest that CKD-S is heterogeneous, and patients with a reduced new baseline GFR have compromised survival, particularly if <45 mL/min/1.73 m 2 . Our findings may have implications regarding choice of PN/RN in patients at risk of developing

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

    Science.gov (United States)

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

    2017-05-30

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

  8. Increased survival rate by local release of diclofenac in a murine model of recurrent oral carcinoma

    Directory of Open Access Journals (Sweden)

    Will OM

    2016-10-01

    Full Text Available Olga Maria Will,1,* Nicolai Purcz,2,* Athena Chalaris,3 Carola Heneweer,4,5 Susann Boretius,1 Larissa Purcz,2 Lila Nikkola,6 Nureddin Ashammakhi,6 Holger Kalthoff,7 Claus-Christian Glüer,1 Jörg Wiltfang,2 Yahya Açil,2 Sanjay Tiwari1 1Section Biomedical Imaging, Clinic for Radiology and Neuroradiology, MOIN CC, 2Department of Oral and Maxillofacial Surgery, University Hospital Schleswig-Holstein, 3Institute of Biochemistry, Christian-Albrechts-Universität zu Kiel, 4Clinic for Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Kiel, 5Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany; 6Department of Biomedical Engineering, Tampere University of Technology, Tampere, Finland; 7Institute for Experimental Cancer Research, University Hospital Schleswig-Holstein, Kiel, Germany *These authors contributed equally to this work Abstract: Despite aggressive treatment with radiation and combination chemotherapy following tumor resection, the 5-year survival rate for patients with head and neck cancer is at best only 50%. In this study, we examined the therapeutic potential of localized release of diclofenac from electrospun nanofibers generated from poly(d,l-lactide-co-glycolide polymer. Diclofenac was chosen since anti-inflammatory agents that inhibit cyclooxygenase have shown great potential in their ability to directly inhibit tumor growth as well as suppress inflammation-mediated tumor growth. A mouse resection model of oral carcinoma was developed by establishing tumor growth in the oral cavity by ultrasound-guided injection of 1 million SCC-9 cells in the floor of the mouth. Following resection, mice were allocated into four groups with the following treatment: 1 no treatment, 2 implanted scaffolds without diclofenac, 3 implanted scaffolds loaded with diclofenac, and 4 diclofenac given orally. Small animal ultrasound and magnetic resonance imaging were utilized for longitudinal

  9. Survival benefit of TIPS versus serial paracentesis in patients with refractory ascites: a single institution case-control propensity score analysis

    International Nuclear Information System (INIS)

    Gaba, R.C.; Parvinian, A.; Casadaban, L.C.; Couture, P.M.; Zivin, S.P.; Lakhoo, J.; Minocha, J.; Ray, C.E.; Knuttinen, M.G.; Bui, J.T.

    2015-01-01

    Aim: To compare the impact of covered stent-graft transjugular intrahepatic portosystemic shunt (TIPS) versus serial paracentesis on survival of patients with medically refractory ascites. Materials and methods: In this retrospective study, cirrhotic patients who underwent covered stent-graft TIPS for refractory ascites from 2003–2013 were compared with similar patients who underwent serial paracentesis during 2009–2013. Demographic and liver disease data, Model for End-Stage Liver Disease (MELD) scores, and survival outcomes were obtained from hospital electronic medical records and the social security death index. After propensity score weighting to match study group characteristics, survival outcomes were compared using Kaplan–Meier statistics with log-rank analysis. Results: Seventy TIPS (70% men, mean age 55.7 years, mean MELD 15.1) and 80 paracentesis (58% men, mean age 53.5 years, mean MELD 22.5) patients were compared. The TIPS haemodynamic success rate was 100% (mean portosystemic pressure gradient reduction 13 mmHg). Paracentesis patients underwent a mean of 7.9 procedures. After propensity score weighting to balance group features, TIPS patients showed a trend toward enhanced survival compared with paracentesis patients (median survival 1037 versus 262 days, p = 0.074). TIPS conferred a significant increase or trend toward improved survival compared with paracentesis at 1 (66% versus 44%, p = 0.018), 2 (56% versus 38%, p = 0.057), and 3 year (49% versus 32%, p = 0.077) time points. Thirty and 90 day mortality rates were not statistically increased by TIPS. Conclusion: Covered stent-graft TIPS improves intermediate- to long-term survival without significantly increasing short-term mortality of ascites patients, and suggests a greater potential role for TIPS in properly selected ascitic patients when medical management fails. - Highlights: • The survival benefit of TIPS for patients with refractory ascites remains unproven. • A case

  10. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival

    International Nuclear Information System (INIS)

    Ganeshan, Balaji; Miles, Ken; Panayiotou, Elleny; Burnand, Kate; Dizdarevic, Sabina

    2012-01-01

    To establish the potential for tumour heterogeneity in non-small cell lung cancer (NSCLC) as assessed by CT texture analysis (CTTA) to provide an independent marker of survival for patients with NSCLC. Tumour heterogeneity was assessed by CTTA of unenhanced images of primary pulmonary lesions from 54 patients undergoing 18 F-fluorodeoxyglucose (FDG) PET-CT for staging of NSCLC. CTTA comprised image filtration to extract fine, medium and coarse features with quantification of the distribution of pixel values (uniformity) within the filtered images. Receiver operating characteristics identified thresholds for PET and CTTA parameters that were related to patient survival using Kaplan-Meier analysis. The median (range) survival was 29.5 (1-38) months. 24, 10, 14 and 6 patients had tumour stages I, II, III and IV respectively. PET stage and tumour heterogeneity assessed by CTTA were significant independent predictors of survival (PET stage: Odds ratio 3.85, 95% confidence limits 0.9-8.09, P = 0.002; CTTA: Odds ratio 56.4, 95% confidence limits 4.79-666, p = 0.001). SUV was not a significantly associated with survival. Assessment of tumour heterogeneity by CTTA of non-contrast enhanced images has the potential for to provide a novel, independent predictor of survival for patients with NSCLC. (orig.)

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

    NARCIS (Netherlands)

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

    2017-01-01

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

  12. Texture analysis for survival prediction of pancreatic ductal adenocarcinoma patients with neoadjuvant chemotherapy

    Science.gov (United States)

    Chakraborty, Jayasree; Langdon-Embry, Liana; Escalon, Joanna G.; Allen, Peter J.; Lowery, Maeve A.; O'Reilly, Eileen M.; Do, Richard K. G.; Simpson, Amber L.

    2016-03-01

    Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death in the United States. The five-year survival rate for all stages is approximately 6%, and approximately 2% when presenting with distant disease.1 Only 10-20% of all patients present with resectable disease, but recurrence rates are high with only 5 to 15% remaining free of disease at 5 years. At this time, we are unable to distinguish between resectable PDAC patients with occult metastatic disease from those with potentially curable disease. Early classification of these tumor types may eventually lead to changes in initial management including the use of neoadjuvant chemotherapy or radiation, or in the choice of postoperative adjuvant treatments. Texture analysis is an emerging methodology in oncologic imaging for quantitatively assessing tumor heterogeneity that could potentially aid in the stratification of these patients. The present study derives several texture-based features from CT images of PDAC patients, acquired prior to neoadjuvant chemotherapy, and analyzes their performance, individually as well as in combination, as prognostic markers. A fuzzy minimum redundancy maximum relevance method with leave-one-image-out technique is included to select discriminating features from the set of extracted features. With a naive Bayes classifier, the proposed method predicts the 5-year overall survival of PDAC patients prior to neoadjuvant therapy and achieves the best results in terms of the area under the receiver operating characteristic curve of 0:858 and accuracy of 83:0% with four-fold cross-validation techniques.

  13. Review and evaluation of performance measures for survival prediction models in external validation settings

    Directory of Open Access Journals (Sweden)

    M. Shafiqur Rahman

    2017-04-01

    Full Text Available Abstract Background When developing a prediction model for survival data it is essential to validate its performance in external validation settings using appropriate performance measures. Although a number of such measures have been proposed, there is only limited guidance regarding their use in the context of model validation. This paper reviewed and evaluated a wide range of performance measures to provide some guidelines for their use in practice. Methods An extensive simulation study based on two clinical datasets was conducted to investigate the performance of the measures in external validation settings. Measures were selected from categories that assess the overall performance, discrimination and calibration of a survival prediction model. Some of these have been modified to allow their use with validation data, and a case study is provided to describe how these measures can be estimated in practice. The measures were evaluated with respect to their robustness to censoring and ease of interpretation. All measures are implemented, or are straightforward to implement, in statistical software. Results Most of the performance measures were reasonably robust to moderate levels of censoring. One exception was Harrell’s concordance measure which tended to increase as censoring increased. Conclusions We recommend that Uno’s concordance measure is used to quantify concordance when there are moderate levels of censoring. Alternatively, Gönen and Heller’s measure could be considered, especially if censoring is very high, but we suggest that the prediction model is re-calibrated first. We also recommend that Royston’s D is routinely reported to assess discrimination since it has an appealing interpretation. The calibration slope is useful for both internal and external validation settings and recommended to report routinely. Our recommendation would be to use any of the predictive accuracy measures and provide the corresponding predictive

  14. Survival Outcomes in Resected Extrahepatic Cholangiocarcinoma: Effect of Adjuvant Radiotherapy in a Surveillance, Epidemiology, and End Results Analysis

    International Nuclear Information System (INIS)

    Vern-Gross, Tamara Z.; Shivnani, Anand T.; Chen, Ke; Lee, Christopher M.; Tward, Jonathan D.; MacDonald, O. Kenneth; Crane, Christopher H.; Talamonti, Mark S.; Munoz, Louis L.; Small, William

    2011-01-01

    Purpose: The benefit of adjuvant radiotherapy (RT) after surgical resection for extrahepatic cholangiocarcinoma has not been clearly established. We analyzed survival outcomes of patients with resected extrahepatic cholangiocarcinoma and examined the effect of adjuvant RT. Methods and Materials: Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program between 1973 and 2003. The primary endpoint was the overall survival time. Cox regression analysis was used to perform univariate and multivariate analyses of the following clinical variables: age, year of diagnosis, histologic grade, localized (Stage T1-T2) vs. regional (Stage T3 or greater and/or node positive) stage, gender, race, and the use of adjuvant RT after surgical resection. Results: The records for 2,332 patients were obtained. Patients with previous malignancy, distant disease, incomplete or conflicting records, atypical histologic features, and those treated with preoperative/intraoperative RT were excluded. Of the remaining 1,491 patients eligible for analysis, 473 (32%) had undergone adjuvant RT. After a median follow-up of 27 months (among surviving patients), the median overall survival time for the entire cohort was 20 months. Patients with localized and regional disease had a median survival time of 33 and 18 months, respectively (p < .001). The addition of adjuvant RT was not associated with an improvement in overall or cause-specific survival for patients with local or regional disease. Conclusion: Patients with localized disease had significantly better overall survival than those with regional disease. Adjuvant RT was not associated with an improvement in long-term overall survival in patients with resected extrahepatic bile duct cancer. Key data, including margin status and the use of combined chemotherapy, was not available through the SEER database.

  15. Critical care admission following elective surgery was not associated with survival benefit: prospective analysis of data from 27 countries.

    Science.gov (United States)

    Kahan, Brennan C; Koulenti, Desponia; Arvaniti, Kostoula; Beavis, Vanessa; Campbell, Douglas; Chan, Matthew; Moreno, Rui; Pearse, Rupert M

    2017-07-01

    As global initiatives increase patient access to surgical treatments, there is a need to define optimal levels of perioperative care. Our aim was to describe the relationship between the provision and use of critical care resources and postoperative mortality. Planned analysis of data collected during an international 7-day cohort study of adults undergoing elective in-patient surgery. We used risk-adjusted mixed-effects logistic regression models to evaluate the association between admission to critical care immediately after surgery and in-hospital mortality. We evaluated hospital-level associations between mortality and critical care admission immediately after surgery, critical care admission to treat life-threatening complications, and hospital provision of critical care beds. We evaluated the effect of national income using interaction tests. 44,814 patients from 474 hospitals in 27 countries were available for analysis. Death was more frequent amongst patients admitted directly to critical care after surgery (critical care: 103/4317 patients [2%], standard ward: 99/39,566 patients [0.3%]; adjusted OR 3.01 [2.10-5.21]; p analysis including only high-risk patients yielded similar findings. We did not identify any survival benefit from critical care admission following surgery.

  16. Individual data meta-analysis for the study of survival after pulmonary metastasectomy in colorectal cancer patients: A history of resected liver metastases worsens the prognosis.

    Science.gov (United States)

    Zabaleta, Jon; Iida, Tomohiko; Falcoz, Pierre E; Salah, Samer; Jarabo, José R; Correa, Arlene M; Zampino, Maria G; Matsui, Takashi; Cho, Sukki; Ardissone, Francesco; Watanabe, Kazuhiro; Gonzalez, Michel; Gervaz, Pascal; Emparanza, Jose I; Abraira, Víctor

    2018-03-21

    To assess the impact of a history of liver metastases on survival in patients undergoing surgery for lung metastases from colorectal carcinoma. We reviewed recent studies identified by searching MEDLINE and EMBASE using the Ovid interface, with the following search terms: lung metastasectomy, pulmonary metastasectomy, lung metastases and lung metastasis, supplemented by manual searching. Inclusion criteria were that the research concerned patients with lung metastases from colorectal cancer undergoing surgery with curative intent, and had been published between 2007 and 2014. Exclusion criteria were that the paper was a review, concerned surgical techniques themselves (without follow-up), and included patients treated non-surgically. Using Stata 14, we performed aggregate data and individual data meta-analysis using random-effect and Cox multilevel models respectively. We collected data on 3501 patients from 17 studies. The overall median survival was 43 months. In aggregate data meta-analysis, the hazard ratio for patients with previous liver metastases was 1.19 (95% CI 0.90-1.47), with low heterogeneity (I 2 4.3%). In individual data meta-analysis, the hazard ratio for these patients was 1.37 (95% CI 1.14-1.64; p analysis identified the following factors significantly affecting survival: tumour-infiltrated pulmonary lymph nodes (p analysis protocol in PROSPERO (CRD42015017838). Copyright © 2018 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

  17. ["That flesh, pink and perishable": analysis of disease-free survival analysis in breast cancer in Gipuzkoa (Spain) in the presence of competing risks].

    Science.gov (United States)

    Martínez-Camblor, Pablo; Larrañaga, Nerea; Sarasqueta, Cristina; Mitxelena, María José; Basterretxea, Mikel

    2009-01-01

    To analyze time of disease-free survival and relative survival in women diagnosed with breast cancer in the province of Gipuzkoa within the context of competing risks by assessing differences between the direct use of the Kaplan-Meier estimator and the multiple decrement method on the one hand, and relative survival on the other. All registered breast cancer cases in Gipuzkoa in 1995 and 1996 with stages other than stage IV were included. An 8-year follow-up for recurrence and a 10-year follow-up for survival were performed. Time of disease-free survival was studied by the multiple decrement model. Observed survival and survival corrected by the expected mortality in the population (relative survival) were also studied. Estimation of the probability of recurrence at 8 years with the multiple decrement method was 8.8% lower than that obtained with the Kaplan-Meier method. The difference between the observed and relative survival rates at 10 years was 10.8%. Both results show how, in this case, the Kaplan-Meier estimator overestimates both the probability of recurrence and that of mortality from the disease. Two issues are often overlooked when performing survival analyses: firstly, because of the lack of independence between survival time and censoring time, the results obtained by the Kaplan-Meier estimator are uninterpretable; secondly, it is an incontrovertible fact that one way or another, everyone causes failures. In this approach, survival analyses must take into account the probability of failure in the general population of reference. The results obtained in this study show that superficial use of the Kaplan Meier estimator overestimates both the probability of recurrence and that of mortality caused by the disease.

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

    Science.gov (United States)

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

    2016-06-01

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

  19. Plants modify biological processes to ensure survival following carbon depletion: a Lolium perenne model.

    Directory of Open Access Journals (Sweden)

    Julia M Lee

    Full Text Available BACKGROUND: Plants, due to their immobility, have evolved mechanisms allowing them to adapt to multiple environmental and management conditions. Short-term undesirable conditions (e.g. moisture deficit, cold temperatures generally reduce photosynthetic carbon supply while increasing soluble carbohydrate accumulation. It is not known, however, what strategies plants may use in the long-term to adapt to situations resulting in net carbon depletion (i.e. reduced photosynthetic carbon supply and carbohydrate accumulation. In addition, many transcriptomic experiments have typically been undertaken under laboratory conditions; therefore, long-term acclimation strategies that plants use in natural environments are not well understood. METHODOLOGY/PRINCIPAL FINDINGS: Perennial ryegrass (Lolium perenne L. was used as a model plant to define whether plants adapt to repetitive carbon depletion and to further elucidate their long-term acclimation mechanisms. Transcriptome changes in both lamina and stubble tissues of field-grown plants with depleted carbon reserves were characterised using reverse transcription-quantitative polymerase chain reaction (RT-qPCR. The RT-qPCR data for select key genes indicated that plants reduced fructan degradation, and increased photosynthesis and fructan synthesis capacities following carbon depletion. This acclimatory response was not sufficient to prevent a reduction (P<0.001 in net biomass accumulation, but ensured that the plant survived. CONCLUSIONS: Adaptations of plants with depleted carbon reserves resulted in reduced post-defoliation carbon mobilization and earlier replenishment of carbon reserves, thereby ensuring survival and continued growth. These findings will help pave the way to improve plant biomass production, for either grazing livestock or biofuel purposes.

  20. A novel survival model of cardioplegic arrest and cardiopulmonary bypass in rats: a methodology paper

    Directory of Open Access Journals (Sweden)

    Podgoreanu Mihai V

    2008-08-01

    Full Text Available Abstract Background Given the growing population of cardiac surgery patients with impaired preoperative cardiac function and rapidly expanding surgical techniques, continued efforts to improve myocardial protection strategies are warranted. Prior research is mostly limited to either large animal models or ex vivo preparations. We developed a new in vivo survival model that combines administration of antegrade cardioplegia with endoaortic crossclamping during cardiopulmonary bypass (CPB in the rat. Methods Sprague-Dawley rats were cannulated for CPB (n = 10. With ultrasound guidance, a 3.5 mm balloon angioplasty catheter was positioned via the right common carotid artery with its tip proximal to the aortic valve. To initiate cardioplegic arrest, the balloon was inflated and cardioplegia solution injected. After 30 min of cardioplegic arrest, the balloon was deflated, ventilation resumed, and rats were weaned from CPB and recovered. To rule out any evidence of cerebral ischemia due to right carotid artery ligation, animals were neurologically tested on postoperative day 14, and their brains histologically assessed. Results Thirty minutes of cardioplegic arrest was successfully established in all animals. Functional assessment revealed no neurologic deficits, and histology demonstrated no gross neuronal damage. Conclusion This novel small animal CPB model with cardioplegic arrest allows for both the study of myocardial ischemia-reperfusion injury as well as new cardioprotective strategies. Major advantages of this model include its overall feasibility and cost effectiveness. In future experiments long-term echocardiographic outcomes as well as enzymatic, genetic, and histologic characterization of myocardial injury can be assessed. In the field of myocardial protection, rodent models will be an important avenue of research.

  1. Analysis of Survival of Patients with Chronic Myeloid Leukemia Treated with Imatinib in the Last 15 Years in Lebanon.

    Science.gov (United States)

    Massoud, Marcel; Sakr, Riwa; Kerbage, Fouad; Makdissi, Joseph; Hawi, Jenny; Rached, Layale; Nasr, Fady; Chahine, Georges

    2017-07-01

    In the 2000s, the introduction of the tyrosine kinase inhibitor (TKI), imatinib, improved the survival outcomes of patients with chronic myeloid leukemia (CML). In Lebanon, we rapidly adopted this treatment strategy. To the best of our knowledge, this is the first study reporting the survival rates of Lebanese CML patients. We examined the rates of major molecular response (MMR) and complete cytogenetic response (CCyR) and analyzed the overall survival, progression-free survival, and event-free survival of CML patients treated with front-line imatinib in 3 university hospitals in Lebanon. We retrospectively reviewed the medical records of 46 patients diagnosed with CML and treated with front-line imatinib 400 mg/day from 2000 and followed up to 2015. In all patients, initially, 2 diagnostic tests were performed: cytogenetic analysis and qualitative molecular testing of the BCR-ABL transcript. The male-to-female sex ratio was 3:1. The median age at diagnosis was 49 years, and the mean age was 44.52 years. At diagnosis, 46 patients were in the chronic phase. All patients started imatinib 400 mg/day. Of the 46 patients, 35 had a typical karyotype, 8 an atypical karyotype, and 3 hypoploidism. The MMR rate at 18 months was 58.69%. The cumulative CCyR rate at 18 months of therapy with imatinib at the standard dose was 67.39%. The event-free survival rate was 75.86% and 74.14% at 5 and 8 years, respectively. The progression-free survival rate was 77.59% and 75.86% at 5 and 8 years, respectively. The overall survival rate was 98.27% and 98.27% at 5 and 8 years, respectively. Of the 46 patients, 12 developed disease progression and were salvaged by second-generation TKIs. These 12 patients were still alive with a MMR. In our study population, the achievement of a MMR and CCyR and overall survival, progression-free survival, and event-free survival were similar to previous published data. Reaching high survival rates with a first-generation TKI in a country with limited

  2. Foundations for Survivable System Development: Service Traces, Intrusion Traces, and Evaluation Models

    National Research Council Canada - National Science Library

    Linger, Richard

    2001-01-01

    .... On the system side, survivability specifications can be defined by essential-service traces that map essential-service workflows, derived from user requirements, into system component dependencies...

  3. Auto-SCT improves survival in systemic light chain amyloidosis: a retrospective analysis with 14-year follow-up.

    Science.gov (United States)

    Parmar, S; Kongtim, P; Champlin, R; Dinh, Y; Elgharably, Y; Wang, M; Bashir, Q; Shah, J J; Shah, N; Popat, U; Giralt, S A; Orlowski, R Z; Qazilbash, M H

    2014-08-01

    Optimal treatment approach continues to remain a challenge for systemic light chain amyloidosis (AL). So far, Auto-SCT is the only modality associated with long-term survival. However, failure to show survival benefit in randomized study raises questions regarding its efficacy. We present a comparative outcome analysis of Auto-SCT to conventional therapies (CTR) in AL patients treated over a 14-year period at our institution. Out of the 145 AL amyloidosis patients, Auto-SCT was performed in 80 patients with 1-year non-relapse mortality rate of 12.5%. Novel agents were used as part of induction therapy in 56% of transplant recipients vs 46% of CTR patients. Hematological and organ responses were seen in 74.6% and 39% in the Auto-SCT arm vs 53% and 12% in the CTR arm, respectively. The projected 5-year survival for Auto-SCT vs CTR was 63% vs 38%, respectively. Landmark analysis of patients alive at 1-year after diagnosis showed improved 5-year OS of 72% with Auto-SCT vs 65% in the CTR arm. In the multivariate analysis, age SCT were associated with improved survival. In conclusion, Auto-SCT is associated with long-term survival for patients with AL amyloidosis.

  4. Advanced age negatively impacts survival in an experimental brain tumor model.

    Science.gov (United States)

    Ladomersky, Erik; Zhai, Lijie; Gritsina, Galina; Genet, Matthew; Lauing, Kristen L; Wu, Meijing; James, C David; Wainwright, Derek A

    2016-09-06

    Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with an average age of 64 years at the time of diagnosis. To study GBM, a number of mouse brain tumor models have been utilized. In these animal models, subjects tend to range from 6 to 12 weeks of age, which is analogous to that of a human teenager. Here, we examined the impact of age on host immunity and the gene expression associated with immune evasion in immunocompetent mice engrafted with syngeneic intracranial GL261. The data indicate that, in mice with brain tumors, youth conveys an advantage to survival. While age did not affect the tumor-infiltrating T cell phenotype or quantity, we discovered that old mice express higher levels of the immunoevasion enzyme, IDO1, which was decreased by the presence of brain tumor. Interestingly, other genes associated with promoting immunosuppression including CTLA-4, PD-L1 and FoxP3, were unaffected by age. These data highlight the possibility that IDO1 contributes to faster GBM outgrowth with advanced age, providing rationale for future investigation into immunotherapeutic targeting in the future. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  5. Simvastatin Treatment Improves Survival in a Murine Model of Burn Sepsis

    Science.gov (United States)

    Beffa, David C; Fischman, Alan J.; Fagan, Shawn P.; Hamrahi, Victoria F.; Kaneki, Masao; Yu, Yong-Ming; Tompkins, Ronald G.; Carter, Edward A.

    2014-01-01

    Infection is the most common and most serious complication of a major burn injury related to burn size. Despite improvements in antimicrobial therapies sepsis still accounts for 50–60% of deaths in burn patients. Given the acute onset and unpredictable nature of sepsis, primary prevention was rarely attempted in its management. However, recent studies have demonstrated that statin treatment can decrease mortality is a murine model of sepsis by preservation of cardiac function and reversal of inflammatory alterations. In addition, it has been shown that treatment with statins is associated with reduced incidence of sepsis in human patients. In the current study groups of CD1 male mice (n=12) were anesthetized and subjected to a dorsal 30% TBSA scald burn injury. Starting 2 hours post burn, the animals were divided into a treatment group receiving 0.2 µ/g simvastatin or a sham group receiving placebo. Simvastatin and placebo were administered by intraperitoneal injection with two dosing regimens; once daily and every 12 hours. On Post burn day 7 cecal ligation and puncture with a 21-gauge needle was performed under ketamine/xylazine anesthesia and the two different dosing schedules were continued. A simvastatin dose dependant improvement in survival was observed in the burn sepsis model. PMID:21145172

  6. Irreversible electroporation of the pancreas is feasible and safe in a porcine survival model.

    Science.gov (United States)

    Fritz, Stefan; Sommer, Christof M; Vollherbst, Dominik; Wachter, Miguel F; Longerich, Thomas; Sachsenmeier, Milena; Knapp, Jürgen; Radeleff, Boris A; Werner, Jens

    2015-07-01

    Use of thermal tumor ablation in the pancreatic parenchyma is limited because of the risk of pancreatitis, pancreatic fistula, or hemorrhage. This study aimed to evaluate the feasibility and safety of irreversible electroporation (IRE) in a porcine model. Ten pigs were divided into 2 study groups. In the first group, animals received IRE of the pancreatic tail and were killed after 60 minutes. In the second group, animals received IRE at the head of the pancreas and were followed up for 7 days. Clinical parameters, computed tomography imaging, laboratory results, and histology were obtained. All animals survived IRE ablation, and no cardiac adverse effects were noted. Sixty minutes after IRE, a hypodense lesion on computed tomography imaging indicated the ablation zone. None of the animals developed clinical signs of acute pancreatitis. Only small amounts of ascites fluid, with a transient increase in amylase and lipase levels, were observed, indicating that no pancreatic fistula occurred. This porcine model shows that IRE is feasible and safe in the pancreatic parenchyma. Computed tomography imaging reveals significant changes at 60 minutes after IRE and therefore might serve as an early indicator of therapeutic success. Clinical studies are needed to evaluate the efficacy of IRE in pancreatic cancer.

  7. Stereotactic Radiosurgery in the Management of Brain Metastases: An Institutional Retrospective Analysis of Survival

    International Nuclear Information System (INIS)

    Frazier, James L.; Batra, Sachin; Kapor, Sumit; Vellimana, Ananth; Gandhi, Rahul; Carson, Kathryn A.; Shokek, Ori; Lim, Michael; Kleinberg, Lawrence; Rigamonti, Daniele

    2010-01-01

    Purpose: The objective of this study was to report our experience with stereotactic radiosurgery performed with the Gamma Knife (GK) in the treatment of patients with brain metastases and to compare survival for those treated with radiosurgery alone with survival for those treated with radiosurgery and whole-brain radiotherapy. Methods and Materials: Prospectively collected demographic and clinical characteristics and treatment and survival data on 237 patients with intracranial metastases who underwent radiosurgery with the GK between 2003 and 2007 were reviewed. Kaplan-Meier and Cox proportional hazards regression analyses were used to compare survival by demographic and clinical characteristics and treatment. Results: The mean age of the patient population was 56 years. The most common tumor histologies were non-small-cell lung carcinoma (34.2%) and breast cancer (13.9%). The median overall survival time was 8.5 months from the time of treatment. The median survival times for patients with one, two/three, and four or more brain metastases were 8.5, 9.4, and 6.7 months, respectively. Patients aged 65 years or greater and those aged less than 65 years had median survival times of 7.8 and 9 months, respectively (p = 0.008). The Karnofsky Performance Score (KPS) at the time of treatment was a significant predictor of survival: those patients with a KPS of 70 or less had a median survival of 2.9 months compared with 10.3 months (p = 0.034) for those with a KPS of 80 or greater. There was no statistically significant difference in survival between patients treated with radiosurgery alone and those treated with radiosurgery plus whole-brain radiotherapy. Conclusions: Radiosurgery with the GK is an efficacious treatment modality for brain metastases. A KPS greater than 70, histology of breast cancer, smaller tumor volume, and age less than 65 years were associated with a longer median survival in our study.

  8. Prognostic model for long-term survival of locally advanced non-small-cell lung cancer patients after neoadjuvant radiochemotherapy and resection integrating clinical and histopathologic factors

    International Nuclear Information System (INIS)

    Pöttgen, Christoph; Stuschke, Martin; Graupner, Britta; Theegarten, Dirk; Gauler, Thomas; Jendrossek, Verena; Freitag, Lutz; Jawad, Jehad Abu; Gkika, Eleni; Wohlschlaeger, Jeremias; Welter, Stefan; Hoiczyk, Matthias; Schuler, Martin; Stamatis, Georgios; Eberhardt, Wilfried

    2015-01-01

    Outcome of consecutive patients with locally advanced non-small cell lung cancer and histopathologically proven mediastional lymph node metastases treated with induction chemotherapy, neoadjuvant radiochemotherapy and thoracotomy at the West German Cancer Center between 08/2000 and 06/2012 was analysed. A clinico-pathological prognostic model for survival was built including partial or complete response according to computed tomography imaging (CT) as clinical parameters as well as pathologic complete remission (pCR) and mediastinal nodal clearance (MNC) as histopathologic factors. Proportional hazard analysis (PHA) and recursive partitioning analysis (RPA) were used to identify prognostic factors for survival. Long-term survival was defined as survival ≥ 36 months. A total of 157 patients were treated, median follow-up was 97 months. Among these patients, pCR and MNC were observed in 41 and 85 patients, respectively. Overall survival was 56 ± 4% and 36 ± 4% at 24 and 60 months, respectively. Sensitivities of pCR and MNC to detect long-term survivors were 38% and 61%, specificities were 84% and 52%, respectively. Multivariable survival analysis revealed pCR, cN3 category, and gender, as prognostic factors at a level of α < 0.05. Considering only preoperative available parameters, CT response became significant. Classifying patients with a predicted hazard above the median as high risk group and the remaining as low risk patients yielded better separation of the survival curves by the inclusion of histopathologic factors than by preoperative factors alone (p < 0.0001, log rank test). Using RPA, pCR was identified as the top prognostic factor above clinical factors (p = 0.0006). No long term survivors were observed in patients with cT3-4 cN3 tumors without pCR. pCR is the dominant histopathologic response parameter and improves prognostic classifiers, based on clinical parameters. The validated prognostic model can be used to estimate individual prognosis and

  9. CMS analysis school model

    International Nuclear Information System (INIS)

    Malik, S; Bloom, K; Shipsey, I; Cavanaugh, R; Klima, B; Chan, Kai-Feng; D'Hondt, J; Narain, M; Palla, F; Rolandi, G; Schörner-Sadenius, T

    2014-01-01

    To impart hands-on training in physics analysis, CMS experiment initiated the concept of CMS Data Analysis School (CMSDAS). It was born over three years ago at the LPC (LHC Physics Centre), Fermilab and is based on earlier workshops held at the LPC and CLEO Experiment. As CMS transitioned from construction to the data taking mode, the nature of earlier training also evolved to include more of analysis tools, software tutorials and physics analysis. This effort epitomized as CMSDAS has proven to be a key for the new and young physicists to jump start and contribute to the physics goals of CMS by looking for new physics with the collision data. With over 400 physicists trained in six CMSDAS around the globe, CMS is trying to engage the collaboration in its discovery potential and maximize physics output. As a bigger goal, CMS is striving to nurture and increase engagement of the myriad talents, in the development of physics, service, upgrade, education of those new to CMS and the career development of younger members. An extension of the concept to the dedicated software and hardware schools is also planned, keeping in mind the ensuing upgrade phase.

  10. CMS Analysis School Model

    Energy Technology Data Exchange (ETDEWEB)

    Malik, S. [Nebraska U.; Shipsey, I. [Purdue U.; Cavanaugh, R. [Illinois U., Chicago; Bloom, K. [Nebraska U.; Chan, Kai-Feng [Taiwan, Natl. Taiwan U.; D' Hondt, J. [Vrije U., Brussels; Klima, B. [Fermilab; Narain, M. [Brown U.; Palla, F. [INFN, Pisa; Rolandi, G. [CERN; Schörner-Sadenius, T. [DESY

    2014-01-01

    To impart hands-on training in physics analysis, CMS experiment initiated the concept of CMS Data Analysis School (CMSDAS). It was born over three years ago at the LPC (LHC Physics Centre), Fermilab and is based on earlier workshops held at the LPC and CLEO Experiment. As CMS transitioned from construction to the data taking mode, the nature of earlier training also evolved to include more of analysis tools, software tutorials and physics analysis. This effort epitomized as CMSDAS has proven to be a key for the new and young physicists to jump start and contribute to the physics goals of CMS by looking for new physics with the collision data. With over 400 physicists trained in six CMSDAS around the globe, CMS is trying to engage the collaboration in its discovery potential and maximize physics output. As a bigger goal, CMS is striving to nurture and increase engagement of the myriad talents, in the development of physics, service, upgrade, education of those new to CMS and the career development of younger members. An extension of the concept to the dedicated software and hardware schools is also planned, keeping in mind the ensuing upgrade phase.

  11. Conceptualizing strategic business model innovation leadership for business survival and business model innovation excellence

    DEFF Research Database (Denmark)

    Lindgren, Peter; Abdullah, Maizura Ailin

    2013-01-01

    Too many businesses are being marginalized by blind "business model innovations (BMIs)" and simple "BMIs". As documented in previous research (Markides 2008, Lindgren 2012), most businesses perform BMIs at a reactive level i.e. perceiving what the market, customers and network partners might want...... rather than what they actually demand. Few businesses have the ability to proactively lead BMIs and on a strategic level lead BMIs to something that fits the business’s long term perspective (Hamel 2011). Apple, Ryanair, Facebook, Zappo are some businesses that have shown BMI Leadership (BMIL......) in a proactive way - and more importantly, as some examples of first level BMIL. The overall aim of the BMIL is to prevent businesses from being marginalized by the BMI and thereby to optimize the business’s total BMI investment. The literature research and case research we studied gave us some important...

  12. Analysis of DNA repair gene polymorphisms and survival in low-grade and anaplastic gliomas

    DEFF Research Database (Denmark)

    Berntsson, Shala Ghaderi; Wibom, Carl; Sjöström, Sara

    2011-01-01

    different DNA repair genes (ATM, NEIL1, NEIL2, ERCC6 and RPA4) which were associated with survival. Finally, these eight genetic variants were adjusted for treatment, malignancy grade, patient age and gender, leaving one variant, rs4253079, mapped to ERCC6, with a significant association to survival (OR 0...

  13. Rural factors and survival from cancer: analysis of Scottish cancer registrations.

    Science.gov (United States)

    Campbell, N C; Elliott, A M; Sharp, L; Ritchie, L D; Cassidy, J; Little, J

    2000-06-01

    In this survival study 63,976 patients diagnosed with one of six common cancers in Scotland were followed up. Increasing distance from a cancer centre was associated with less chance of diagnosis before death for stomach, breast and colorectal cancers and poorer survival after diagnosis for prostate and lung cancers.

  14. Clinical variables serve as prognostic factors in a model for survival from glioblastoma multiforme

    DEFF Research Database (Denmark)

    Michaelsen, Signe Regner; Christensen, Ib Jarle; Grunnet, Kirsten

    2013-01-01

    Although implementation of temozolomide (TMZ) as a part of primary therapy for glioblastoma multiforme (GBM) has resulted in improved patient survival, the disease is still incurable. Previous studies have correlated various parameters to survival, although no single parameter has yet been...

  15. ORGANISATIONAL CULTURE ANALYSIS MODEL

    OpenAIRE

    Mihaela Simona Maracine

    2012-01-01

    The studies and researches undertaken have demonstrated the importance of studying organisational culture because of the practical valences it presents and because it contributes to increasing the organisation’s performance. The analysis of the organisational culture’s dimensions allows observing human behaviour within the organisation and highlighting reality, identifying the strengths and also the weaknesses which have an impact on its functionality and development. In this paper, we try to...

  16. The impact of comorbidity on overall survival in elderly nasopharyngeal carcinoma patients: a National Cancer Data Base analysis.

    Science.gov (United States)

    Huang, Ying; Chen, Wei; Haque, Waqar; Verma, Vivek; Xing, Yan; Teh, Bin S; Brian Butler, Edward

    2018-04-01

    The number of elderly patients with cancer is increasing. Medical comorbidities are more common in this population. Little is known regarding the prognostic relevance of comorbidities in elderly patients with nasopharyngeal carcinoma (NPC). Using the National Cancer Data Base (NCDB), we queried patients age >65 years diagnosed with NPC and treated with definitive radiation between 2004 and 2012 to examine the association between comorbidity and survival outcomes. Comorbidity was assessed with the Charlson Comorbidity Index (CCI). The influence of comorbidity on overall survival (OS) was evaluated. Cox proportional hazards model was used to study the impact of comorbidity on OS. A total of 1137 patients met the specified criteria. Median follow-up was 61.2 months. Five-year OS was 50.4%. Comorbidities were present in 22.4% of patients, with 17.6% of patients having a CCI score of 1% and 4.8% having a CCI score of ≥2. Patients with a CCI score of 0 had significantly higher 5-year OS than patients with a CCI score of 1 or ≥2 (53.1% vs. 42.2% vs. 32.9%, P < 0.001). In multivariate analysis, CCI was a statistically significant independent prognostic factor for the risk of death of all causes for patients with a CCI score of 1 (hazard ratio [HR]: 1.242; 95% confidence interval [CI]: 1.002-1.539) or CCI score of ≥2 (HR: 1.625; 95% CI: 1.157-2.283) when compared to patients with a CCI score of 0. Comorbidity as measured by CCI is a strong independent prognostic factor for OS in elderly patients with NPC and lends support to the inclusion of comorbidity assessment due to its prognostic value when treating elderly patients with NPC. © 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

  17. Chemotherapy increases long-term survival in patients with adult medulloblastoma--a literature-based meta-analysis.

    Science.gov (United States)

    Kocakaya, Selin; Beier, Christoph Patrick; Beier, Dagmar

    2016-03-01

    Adult medulloblastoma is a potentially curable malignant entity with an incidence of 0.5-1 per million. Valid data on prognosis, treatment, and demographics are lacking, as most current knowledge stems from retrospective studies. Surgical resection followed by radiotherapy are accepted parts of treatment regimes; however, established prognostic factors and data clarifying the role of chemotherapy are missing. We investigated 227 publications from 1969-2013, with 907 identifiable, individual patients being available for meta-analysis. Demographic data, risk stratification, and treatment of these patients were similar to previous cohorts. The median overall survival (mOS) was 65 months (95% CI: 54.6-75.3) , the 5-year overall survival was 50.9% with 16% of the patients dying more than 5 years after diagnosis. Incomplete resection, clinical and radiological signs for brainstem infiltration, and abstinence from radiotherapy were predictive of worse outcome. Metastatic disease at tumor recurrence was identified as a new prognostic factor, while neither metastasis at initial diagnosis nor desmoplastic/classic histology was correlated with survival. Patients receiving chemotherapy first-line survived significantly longer (mOS: 108 mo, 95% CI: 68.6-148.4) than patients treated with radiation alone (mOS: 57 mo, 95% CI: 39.6-74.4) or patients who received chemotherapy at tumor recurrence. This effect was not biased by tumor stage or decade of treatment. Importantly, (neo)adjuvant chemotherapy also significantly increased the chance for long-term survival (>5 y) compared with radiotherapy alone or chemotherapy at tumor recurrence. This meta-analysis clarifies relevant prognostic factors and suggests that chemotherapy as part of first-line therapy improves overall survival and increases the proportion of patients with long-term survival. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions

  18. Development and validation of technique for in-vivo 3D analysis of cranial bone graft survival

    Science.gov (United States)

    Bernstein, Mark P.; Caldwell, Curtis B.; Antonyshyn, Oleh M.; Ma, Karen; Cooper, Perry W.; Ehrlich, Lisa E.

    1997-05-01

    Bone autografts are routinely employed in the reconstruction of facial deformities resulting from trauma, tumor ablation or congenital malformations. The combined use of post- operative 3D CT and SPECT imaging provides a means for quantitative in vivo evaluation of bone graft volume and osteoblastic activity. The specific objectives of this study were: (1) Determine the reliability and accuracy of interactive computer-assisted analysis of bone graft volumes based on 3D CT scans; (2) Determine the error in CT/SPECT multimodality image registration; (3) Determine the error in SPECT/SPECT image registration; and (4) Determine the reliability and accuracy of CT-guided SPECT uptake measurements in cranial bone grafts. Five human cadaver heads served as anthropomorphic models for all experiments. Four cranial defects were created in each specimen with inlay and onlay split skull bone grafts and reconstructed to skull and malar recipient sites. To acquire all images, each specimen was CT scanned and coated with Technetium doped paint. For purposes of validation, skulls were landmarked with 1/16-inch ball-bearings and Indium. This study provides a new technique relating anatomy and physiology for the analysis of cranial bone graft survival.

  19. Analysis of factors influencing survival in patients with severe acute pancreatitis.

    Science.gov (United States)

    Kim, Yeon Ji; Kim, Dae Bum; Chung, Woo Chul; Lee, Ji Min; Youn, Gun Jung; Jung, Yun Duk; Choi, Sooa; Oh, Jung Hwan

    2017-08-01

    Acute pancreatitis (AP) ranges from a mild and self-limiting disease to a fulminant illness with significant morbidity and mortality. Severe acute pancreatitis (SAP) is defined as persistent organ failure lasting for 48 h. We aimed to determine the factors that predict survival and mortality in patients with SAP. We reviewed a consecutive series of patients who were admitted with acute pancreatitis between January 2003 and January 2013. A total of 1213 cases involving 660 patients were evaluated, and 68 cases with SAP were selected for the study. Patients were graded based on the Computer Tomography Severity Index (CTSI), the bedside index for severity (BISAP), and Ranson's criteria. The frequency of SAP was 5.6% (68/1213 cases). Among these patients, 17 died due to pancreatitis-induced causes. We compared several factors between the survivor (n = 51) and non-survivor (n = 17) groups. On multivariate analysis, there were significant differences in the incidence of diabetes mellitus (p = .04), Ranson score (p = .03), bacteremia (p = .05) and body mass index (BMI) (p = .02) between the survivor and non-survivor groups. Bacteremia, high Ranson score, DM, and lower BMI were closely associated with mortality in patients with SAP. When patients with SAP show evidence of bacteremia or diabetes, aggressive treatment is necessary. For the prediction of disease mortality, the Ranson score might be a useful tool in SAP.

  20. Adoption of SO2 emission control technologies - An application of survival analysis

    International Nuclear Information System (INIS)

    Streeter, Jialu Liu

    2016-01-01

    Using data on coal-fired electric power plants, this article investigates the contributing factors affecting the investment decisions on flue-gas desulfurization (FGD), a capital-intensive emission control technology. The paper makes two contributions to the literature. First, the public regulatory status of electric power plants is found to have a strong influence on whether FGD investment is made. Compared to deregulated power plants, those that are still under rate-of-return regulations by Public Utility Commissions are more likely to install FGD. Second, a higher rate of inspections of polluting facilities (not just electric utility power plants) in a state in the previous year is associated with a higher probability of power plants adopting FGD this year. In addition, sulfur content of coal and plant size are both positively associated with the likelihood of FGD installation. The service length of boilers is negatively associated with the likelihood. - Highlights: • Contributing factors affecting investment decisions on emission control devices. • A survival analysis framework is applied in estimation. • Data cover over 300 coal-fired electric utility power plants, 2002–2012. • Still-regulated power plants are more likely to install FGD than deregulated ones. • State-level inspection frequency leads to more FGD installation.

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

    Directory of Open Access Journals (Sweden)

    Rotella, J. J.

    2004-06-01

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

  2. 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…

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

    International Nuclear Information System (INIS)

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

    2002-01-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

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

    International Nuclear Information System (INIS)

    Shao Chunlin; Gui Qifu; Yu Zengliang

    1995-01-01

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

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

    Science.gov (United States)

    Combescure, Christophe; Foucher, Yohann; Jackson, Daniel

    2014-07-10

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

  7. Codevelopment of conceptual understanding and critical attitude: toward a systemic analysis of the survival blanket

    Science.gov (United States)

    Viennot, Laurence; Décamp, Nicolas

    2016-01-01

    One key objective of physics teaching is the promotion of conceptual understanding. Additionally, the critical faculty is universally seen as a central quality to be developed in students. In recent years, however, teaching objectives have placed stronger emphasis on skills than on concepts, and there is a risk that conceptual structuring may be disregarded. The question therefore arises as to whether it is possible for students to develop a critical stance without a conceptual basis, leading in turn to the issue of possible links between the development of conceptual understanding and critical attitude. In an in-depth study to address these questions, the participants were seven prospective physics and chemistry teachers. The methodology included a ‘teaching interview’, designed to observe participants’ responses to limited explanations of a given phenomenon and their ensuing intellectual satisfaction or frustration. The explanatory task related to the physics of how a survival blanket works, requiring a full and appropriate system analysis of the blanket. The analysis identified five recurrent lines of reasoning and linked these to judgments of adequacy of explanation, based on metacognitive/affective (MCA) factors, intellectual (dis)satisfaction and critical stance. Recurrent themes and MCA factors were used to map the intellectual dynamics that emerged during the interview process. Participants’ critical attitude was observed to develop in strong interaction with their comprehension of the topic. The results suggest that most students need to reach a certain level of conceptual mastery before they can begin to question an oversimplified explanation, although one student’s replies show that a different intellectual dynamics is also possible. The paper ends with a discussion of the implications of these findings for future research and for decisions concerning teaching objectives and the design of learning environments.

  8. Pediatric differentiated thyroid carcinoma in stage I: risk factor analysis for disease free survival

    International Nuclear Information System (INIS)

    Wada, Nobuyuki; Rino, Yasushi; Masuda, Munetaka; Ito, Koichi; Sugino, Kiminori; Mimura, Takashi; Nagahama, Mitsuji; Kitagawa, Wataru; Shibuya, Hiroshi; Ohkuwa, Keiko; Nakayama, Hirotaka; Hirakawa, Shohei

    2009-01-01

    To examine the outcomes and risk factors in pediatric differentiated thyroid carcinoma (DTC) patients who were defined as TNM stage I because some patients develop disease recurrence but treatment strategy for such stage I pediatric patients is still controversial. We reviewed 57 consecutive TNM stage I patients (15 years or less) with DTC (46 papillary and 11 follicular) who underwent initial treatment at Ito Hospital between 1962 and 2004 (7 males and 50 females; mean age: 13.1 years; mean follow-up: 17.4 years). Clinicopathological results were evaluated in all patients. Multivariate analysis was performed to reveal the risk factors for disease-free survival (DFS) in these 57 patients. Extrathyroid extension and clinical lymphadenopathy at diagnosis were found in 7 and 12 patients, respectively. Subtotal/total thyroidectomy was performed in 23 patients, modified neck dissection in 38, and radioactive iodine therapy in 10. Pathological node metastasis was confirmed in 37 patients (64.9%). Fifteen patients (26.3%) exhibited local recurrence and 3 of them also developed metachronous lung metastasis. Ten of these 15 achieved disease-free after further treatments and no patients died of disease. In multivariate analysis, male gender (p = 0.017), advanced tumor (T3, 4a) stage (p = 0.029), and clinical lymphadenopathy (p = 0.006) were risk factors for DFS in stage I pediatric patients. Male gender, tumor stage, and lymphadenopathy are risk factors for DFS in stage I pediatric DTC patients. Aggressive treatment (total thyroidectomy, node dissection, and RI therapy) is considered appropriate for patients with risk factors, whereas conservative or stepwise approach may be acceptable for other patients

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

    Czech Academy of Sciences Publication Activity Database

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

    2004-01-01

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

  10. Survival, recurrence and toxicity of HNSCC in comparison of a radiotherapy combination with cisplatin versus cetuximab: a meta-analysis

    International Nuclear Information System (INIS)

    Huang, Jingwen; Zhang, Jing; Shi, Changle; Liu, Lei; Wei, Yuquan

    2016-01-01

    Cisplatin-based treatment has been considered the standard treatment regimen of HNSCC. Cetuximab is an emerging target therapy that has potential therapeutic benefits over cisplatin. Nevertheless, curative effects of cisplatin-based chemoradiotherapy (CRT) versus cetuximab-based bioradiotherapy (BRT) are still controversial. Potentially eligible studies were retrieved using PubMed, Embase and Medline. Basic characteristics of patients and statistical data were collected. A meta-analysis model was established to compare CRT and BRT. Thirty-one eligible studies and 4212 patients were found. The pooled HRs with 95 % confidence intervals (CIs) for OS and PFS were 0.32 [0.09, 0.55] and 0.51 [0.22, 0.80], respectively, and both were in favor of cisplatin. However, 3-year survival and recurrence analysis of the subgroups showed no differences between the two groups (p > 0.05). In subgroup analysis, oropharyngeal primary tumors exhibited improved results by cetuximab with a pooled HR of 1.56 [1.14, 2.13] for PFS. Additionally, the HPV+ status was a significant factor in positive outcomes with cetuximab with a pooled HR of 1.12 [0.46, 2.17] for OS. Long-term use of BRT showed no significant difference compared with CRT, and both arms showed different aspects of toxicity. In subgroup analysis, taking the effects of treatment and adverse events into consideration, cetuximab plus radiation may show superior responses regarding OS and PFS in patients who have HPV+ or primary oropharyngeal HNSCC, respectively, but physicians should administer them with caution

  11. Histopathological analysis of pre-implantation donor kidney biopsies: association with graft survival and function in one year post-transplantation

    Directory of Open Access Journals (Sweden)

    Karla Lais Pêgas

    2014-04-01

    Full Text Available Introduction: Pre-implantation kidney biopsy is a decision-making tool when considering the use of grafts from deceased donors with expanded criteria, implanting one or two kidneys and comparing this to post-transplantation biopsies. The role of histopathological alterations in kidney compartments as a prognostic factor in graft survival and function has had conflicting results. Objective: This study evaluated the prevalence of chronic alterations in pre-implant biopsies of kidney grafts and the association of findings with graft function and survival in one year post-transplant. Methods: 110 biopsies were analyzed between 2006 and 2009 at Santa Casa de Porto Alegre, including live donors, ideal deceased donors and those with expanded criteria. The score was computed according to criteria suggested by Remuzzi. The glomerular filtration rate (GFR was calculated using the abbreviated MDRD formula. Results: No statistical difference was found in the survival of donors stratified according to Remuzzi criteria. The GFR was significantly associated with the total scores in the groups with mild and moderate alterations, and in the kidney compartments alone, by univariate analysis. The multivariate model found an association with the presence of arteriosclerosis, glomerulosclerosis, acute rejection and delayed graft function. Conclusion: Pre-transplant chronic kidney alterations did not influence the post-transplantation one-year graft survival, but arteriosclerosis and glomerulosclerosis is predictive of a worse GFR. Delayed graft function and acute rejection are independent prognostic factors.

  12. Survival analysis for predictive factors of delay vaccination in Iranian children

    Directory of Open Access Journals (Sweden)

    Abolfazl Mohammadbeigi

    2015-01-01

    Full Text Available Background: Today, beside immunization coverage the age appropriate vaccination is another helpful index in public health. Evidences have shown that high immunization coverage rates do not necessarily imply age-appropriate vaccination status. The current study aimed to show the predictive factors of delayed vaccination by survival models. Methods: A historical cohort study conducted on 3610 children aged between 24 and 47 months who was living in the suburbs of five big cities of Iran. Time of delay in vaccination of first dose of mumps-measles-rubella (MMR was calculated from date of vaccination minus age appropriate time according to vaccine card. Kaplan-Maier and Log rank tests were used for comparison the median of delay time. For controlling of confounding variables, multivariate cox model was used and hazard ratio with 95% confidence interval (95% was reported. Results: The mean ± standard deviation and median interquartile range of delay time was 38.34 ± 73.1 and 16 (11-31 days in delayed group. The Log rank test showed that city of living, nationality, parents′ education, and birth order are related with prolonged delay time in MMR vaccination (P 0.05. Cox regression showed that city of living, mother education, and nationality are the most predictive factors of delay time duration in MMR vaccination. Conclusions: Delay time duration of vaccination increased by faring from capital to the east south. Moreover, concentration of foreign immigrants in big cities and low level of mother education are the most predictors of delayed vaccination. Educational intervention should focus on immigrants and mothers with low education level.

  13. Positive end-expiratory pressure improves survival in a rodent model of cardiopulmonary resuscitation using high-dose epinephrine.

    LENUS (Irish Health Repository)

    McCaul, Conán

    2009-10-01

    Multiple interventions have been tested in models of cardiopulmonary resuscitation (CPR) to optimize drug use, chest compressions, and ventilation. None has studied the effects of positive end-expiratory pressure (PEEP) on outcome. We hypothesized that because PEEP can reverse pulmonary atelectasis, lower pulmonary vascular resistance, and potentially improve cardiac output, its use during CPR would increase survival.

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

    Science.gov (United States)

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

    2013-01-01

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

  15. Opioid withdrawal, craving, and use during and after outpatient buprenorphine stabilization and taper: a discrete survival and growth mixture model.

    Science.gov (United States)

    Northrup, Thomas F; Stotts, Angela L; Green, Charles; Potter, Jennifer S; Marino, Elise N; Walker, Robrina; Weiss, Roger D; Trivedi, Madhukar

    2015-02-01

    Most patients relapse to opioids within one month of opioid agonist detoxification, making the antecedents and parallel processes of first use critical for investigation. Craving and withdrawal are often studied in relationship to opioid outcomes, and a novel analytic strategy applied to these two phenomena may indicate targeted intervention strategies. Specifically, this secondary data analysis of the Prescription Opioid Addiction Treatment Study used a discrete-time mixture analysis with time-to-first opioid use (survival) simultaneously predicted by craving and withdrawal growth trajectories. This analysis characterized heterogeneity among prescription opioid-dependent individuals (N=653) into latent classes (i.e., latent class analysis [LCA]) during and after buprenorphine/naloxone stabilization and taper. A 4-latent class solution was selected for overall model fit and clinical parsimony. In order of shortest to longest time-to-first use, the 4 classes were characterized as 1) high craving and withdrawal, 2) intermediate craving and withdrawal, 3) high initial craving with low craving and withdrawal trajectories and 4) a low initial craving with low craving and withdrawal trajectories. Odds ratio calculations showed statistically significant differences in time-to-first use across classes. Generally, participants with lower baseline levels and greater decreases in craving and withdrawal during stabilization combined with slower craving and withdrawal rebound during buprenorphine taper remained opioid-free longer. This exploratory work expanded on the importance of monitoring craving and withdrawal during buprenorphine induction, stabilization, and taper. Future research may allow individually tailored and timely interventions to be developed to extend time-to-first opioid use. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Mycobacterium tuberculosis PPE18 Protein Reduces Inflammation and Increases Survival in Animal Model of Sepsis.

    Science.gov (United States)

    Ahmed, Asma; Dolasia, Komal; Mukhopadhyay, Sangita

    2018-04-18

    Mycobacterium tuberculosis PPE18 is a member of the PPE family. Previous studies have shown that recombinant PPE18 (rPPE18) protein binds to TLR2 and triggers a signaling cascade which reduces levels of TNF-α and IL-12, and increases IL-10 in macrophages. Because TNF-α is a major mediator of the pathophysiology of sepsis and blocking inflammation is a possible line of therapy in such circumstances, we tested the efficacy of rPPE18 in reducing symptoms of sepsis in a mouse model of Escherichia coli- induced septic peritonitis. rPPE18 significantly decreased levels of serum TNF-α, IL-1β, IL-6, and IL-12 and reduced organ damage in mice injected i.p. with high doses of E. coli Peritoneal cells isolated from rPPE18-treated mice had characteristics of M2 macrophages which are protective in excessive inflammation. Additionally, rPPE18 inhibited disseminated intravascular coagulation, which can cause organ damage resulting in death. rPPE18 was able to reduce sepsis-induced mortality when given prophylactically or therapeutically. Additionally, in a mouse model of cecal ligation and puncture-induced sepsis, rPPE18 reduced TNF-α, alanine transaminase, and creatinine, attenuated organ damage, prevented depletion of monocytes and lymphocytes, and improved survival. Our studies show that rPPE18 has potent anti-inflammatory properties and can serve as a novel therapeutic to control sepsis. Copyright © 2018 by The American Association of Immunologists, Inc.

  17. ROCK PROPERTIES MODEL ANALYSIS MODEL REPORT

    International Nuclear Information System (INIS)

    Clinton Lum

    2002-01-01

    The purpose of this Analysis and Model Report (AMR) is to document Rock Properties Model (RPM) 3.1 with regard to input data, model methods, assumptions, uncertainties and limitations of model results, and qualification status of the model. The report also documents the differences between the current and previous versions and validation of the model. The rock properties models are intended principally for use as input to numerical physical-process modeling, such as of ground-water flow and/or radionuclide transport. The constraints, caveats, and limitations associated with this model are discussed in the appropriate text sections that follow. This work was conducted in accordance with the following planning documents: WA-0344, ''3-D Rock Properties Modeling for FY 1998'' (SNL 1997, WA-0358), ''3-D Rock Properties Modeling for FY 1999'' (SNL 1999), and the technical development plan, Rock Properties Model Version 3.1, (CRWMS MandO 1999c). The Interim Change Notice (ICNs), ICN 02 and ICN 03, of this AMR were prepared as part of activities being conducted under the Technical Work Plan, TWP-NBS-GS-000003, ''Technical Work Plan for the Integrated Site Model, Process Model Report, Revision 01'' (CRWMS MandO 2000b). The purpose of ICN 03 is to record changes in data input status due to data qualification and verification activities. These work plans describe the scope, objectives, tasks, methodology, and implementing procedures for model construction. The constraints, caveats, and limitations associated with this model are discussed in the appropriate text sections that follow. The work scope for this activity consists of the following: (1) Conversion of the input data (laboratory measured porosity data, x-ray diffraction mineralogy, petrophysical calculations of bound water, and petrophysical calculations of porosity) for each borehole into stratigraphic coordinates; (2) Re-sampling and merging of data sets; (3) Development of geostatistical simulations of porosity; (4

  18. Intercity Travel Demand Analysis Model

    OpenAIRE

    Ming Lu; Hai Zhu; Xia Luo; Lei Lei

    2014-01-01

    It is well known that intercity travel is an important component of travel demand which belongs to short distance corridor travel. The conventional four-step method is no longer suitable for short distance corridor travel demand analysis for the time spent on urban traffic has a great impact on traveler's main mode choice. To solve this problem, the author studied the existing intercity travel demand analysis model, then improved it based on the study, and finally established a combined model...

  19. Uncertainty analysis of environmental models

    International Nuclear Information System (INIS)

    Monte, L.

    1990-01-01

    In the present paper an evaluation of the output uncertainty of an environmental model for assessing the transfer of 137 Cs and 131 I in the human food chain are carried out on the basis of a statistical analysis of data reported by the literature. The uncertainty analysis offers the oppotunity of obtaining some remarkable information about the uncertainty of models predicting the migration of non radioactive substances in the environment mainly in relation to the dry and wet deposition

  20. Multiscale Signal Analysis and Modeling

    CERN Document Server

    Zayed, Ahmed

    2013-01-01

    Multiscale Signal Analysis and Modeling presents recent advances in multiscale analysis and modeling using wavelets and other systems. This book also presents applications in digital signal processing using sampling theory and techniques from various function spaces, filter design, feature extraction and classification, signal and image representation/transmission, coding, nonparametric statistical signal processing, and statistical learning theory. This book also: Discusses recently developed signal modeling techniques, such as the multiscale method for complex time series modeling, multiscale positive density estimations, Bayesian Shrinkage Strategies, and algorithms for data adaptive statistics Introduces new sampling algorithms for multidimensional signal processing Provides comprehensive coverage of wavelets with presentations on waveform design and modeling, wavelet analysis of ECG signals and wavelet filters Reviews features extraction and classification algorithms for multiscale signal and image proce...

  1. A survival analysis of GBM patients in the West of Scotland pre- and post-introduction of the Stupp regime.

    Science.gov (United States)

    Teo, Mario; Martin, Sean; Owusu-Agyemang, Kevin; Nowicki, Stefan; Clark, Brian; Mackinnon, Mairi; Stewart, Willie; Paul, James; St George, Jerome

    2014-06-01

    It is now accepted that the concomitant administration of temozolomide with radiotherapy (Stupp regime), in the treatment of patients with newly diagnosed glioblastoma multiforme (GBM), significantly improves survival and this practice has been adopted locally since 2004. However, survival outcomes in cancer can vary in different population groups, and outcomes can be affected by a number of local factors including socioeconomic status. In the West of Scotland, we have one of the worse socioeconomic status and overall health record for a western European country. With the ongoing reorganisation and rationalisation in the National Health Service, the addition of prolonged courses of chemotherapy to patients' management significantly adds to the financial burden of a cash stripped NHS. A survival analysis in patients with GBM was therefore performed, comparing outcomes of pre- and post-introduction of the Stupp regime, to justify the current practice. Prospectively collected clinical data were analysed in 105 consecutive patients receiving concurrent chemoradiotherapy (Stupp regime) following surgical treatment of GBM between December 2004 and February 2009. This was compared to those of 106 consecutive GBM patients who had radical radiotherapy (pre-Stupp regime) post-surgery between January 2001 and February 2006. The median overall survival for the post-Stupp cohort was 15.3 months (range, 2.83-50.5 months), with 1-year and 2-year overall survival rates of 65.7% and 19%, respectively. This was in comparison with the median overall pre-Stupp survival of 10.7 months, with 1-year and 2-year survival rates of 42.6% and 12%, respectively (log-rank test, p GBM patients in the West of Scotland.

  2. Design and analysis methods for fish survival experiments based on release-recapture

    National Research Council Canada - National Science Library

    Burnham, Kenneth P

    1987-01-01

    .... The application of the methods developed here is more general, however, as it includes experiments to estimate survival of fish as they pass over spillways or through bypass systems and several dams...

  3. A two-zone cosmic ray propagation model and its implication of the surviving fraction of radioactive cosmic ray isotopes

    International Nuclear Information System (INIS)

    Simon, M.; Scherzer, R.; Enge, W.

    1977-01-01

    In cosmic ray propagation calculations one can usually assume a homogeneous distribution of interstellar matter. The crucial astrophysical parameters in these models are: The path length distribution, the age of the cosmic ray particles and the interstellar matter density. These values are interrelated. The surviving fraction of radioactive cosmic ray isotopes is often used to determine a mean matter density of that region, where the cosmic ray particles may mainly reside. Using a Monte Carlo Propagation Program we calculated the change in the surviving fraction quantitatively assuming a region around the sources with higher matter density. (author)

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

    Science.gov (United States)

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

    2018-01-01

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

  5. Multivariate analysis: models and method

    International Nuclear Information System (INIS)

    Sanz Perucha, J.

    1990-01-01

    Data treatment techniques are increasingly used since computer methods result of wider access. Multivariate analysis consists of a group of statistic methods that are applied to study objects or samples characterized by multiple values. A final goal is decision making. The paper describes the models and methods of multivariate analysis

  6. Domain specific modeling and analysis

    NARCIS (Netherlands)

    Jacob, Joost Ferdinand

    2008-01-01

    It is desirable to model software systems in such a way that analysis of the systems, and tool development for such analysis, is readily possible and feasible in the context of large scientific research projects. This thesis emphasizes the methodology that serves as a basis for such developments.

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

    NARCIS (Netherlands)

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

    2014-01-01

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

  8. System-level analysis of genes and functions affecting survival during nutrient starvation in Saccharomyces cerevisiae.

    Science.gov (United States)

    Gresham, David; Boer, Viktor M; Caudy, Amy; Ziv, Naomi; Brandt, Nathan J; Storey, John D; Botstein, David

    2011-01-01

    An essential property of all cells is the ability to exit from active cell division and persist in a quiescent state. For single-celled microbes this primarily occurs in response to nutrient deprivation. We studied the genetic requirements for survival of Saccharomyces cerevisiae when starved for either of two nutrients: phosphate or leucine. We measured the survival of nearly all nonessential haploid null yeast mutants in mixed populations using a quantitative sequencing method that estimates the abundance of each mutant on the basis of frequency of unique molecular barcodes. Starvation for phosphate results in a population half-life of 337 hr whereas starvation for leucine results in a half-life of 27.7 hr. To measure survival of individual mutants in each population we developed a statistical framework that accounts for the multiple sources of experimental variation. From the identities of the genes in which mutations strongly affect survival, we identify genetic evidence for several cellular processes affecting survival during nutrient starvation, including autophagy, chromatin remodeling, mRNA processing, and cytoskeleton function. In addition, we found evidence that mitochondrial and peroxisome function is required for survival. Our experimental and analytical methods represent an efficient and quantitative approach to characterizing genetic functions and networks with unprecedented resolution and identified genotype-by-environment interactions that have important implications for interpretation of studies of aging and quiescence in yeast.

  9. Spinal bone metastases in gynecologic malignancies: a retrospective analysis of stability, prognostic factors and survival

    International Nuclear Information System (INIS)

    Foerster, Robert; Habermehl, Daniel; Bruckner, Thomas; Bostel, Tilman; Schlampp, Ingmar; Welzel, Thomas; Debus, Juergen; Rief, Harald

    2014-01-01

    The aim of this retrospective study was to evaluate the stability of spinal metastases in gynecologic cancer patients (pts) on the basis of a validated scoring system after radiotherapy (RT), to define prognostic factors for stability and to calculate survival. Fourty-four women with gynecologic malignancies and spinal bone metastases were treated at our department between January 2000 and January 2012. Out of those 34 were assessed regarding stability using the Taneichi score before, 3 and 6 months after RT. Additionally prognostic factors for stability, overall survival, and bone survival (time between first day of RT of bone metastases and death from any cause) were calculated. Before RT 47% of pts were unstable and 6 months after RT 85% of pts were stable. Karnofsky performance status (KPS) >70% (p = 0.037) and no chemotherapy (ChT) (p = 0.046) prior to RT were significantly predictive for response. 5-year overall survival was 69% and 1-year bone survival was 73%. RT is capable of improving stability of osteolytic spinal metastases from gynecologic cancer by facilitating re-ossification in survivors. KPS may be a predictor for response. Pts who received ChT prior to RT may require additional bone supportive treatment to overcome bone remodeling imbalance. Survival in women with bone metastases from gynecologic cancer remains poor

  10. Nitrosourea efficacy in high-grade glioma: a survival gain analysis summarizing 504 cohorts with 24193 patients.

    Science.gov (United States)

    Wolff, Johannes E A; Berrak, Su; Koontz Webb, Susannah E; Zhang, Ming

    2008-05-01

    Even though past studies have suggested efficacy of nitrosourea drugs in patients with high-grade glioma and temozolomide has recently been shown significantly to be beneficial, no conclusive comparisons between these agents have been published. We performed a survival gain analysis of 364 studies describing 24,193 patients with high-grade glioma treated in 504 cohorts, and compared the effects of drugs. The most frequent diagnoses were glioblastoma multiforme (GBM) (72%) and anaplastic astrocytoma (22%). The mean overall survival (mOS) was 14.1 months. The outcome was influenced by several of the known prognostic factors including the histological grade, if the tumors were newly diagnosed or recurrent, the completeness of resection, patients' age, and gender. This information allowed the calculation of a predicted mOS for each cohort based on their prognostic factors independent of treatment. Survival gain to characterize the influence of treatment was subsequently defined and validated as the difference between the observed and the predicted mOS. In 62 CCNU-treated cohorts and 15 ACNU-treated cohorts the survival gain was 5.3 months and 8.9 months (P < 0.0005), respectively. No detectable survival gain for patients treated with various BCNU-containing regimens was found. Conclusion CCNU- and ACNU-containing regimens were superior to BCNU containing regiments.

  11. Sensitivity Analysis of Simulation Models

    NARCIS (Netherlands)

    Kleijnen, J.P.C.

    2009-01-01

    This contribution presents an overview of sensitivity analysis of simulation models, including the estimation of gradients. It covers classic designs and their corresponding (meta)models; namely, resolution-III designs including fractional-factorial two-level designs for first-order polynomial

  12. Dose-rate models for human survival after exposure to ionizing radiation

    International Nuclear Information System (INIS)

    Jones, T.D.; Morris, M.D.; Young, R.W.

    1987-01-01

    This paper reviews new estimates of the LD 50 in man by Mole and by Rotblat, the biological processes contributing to hematologic death, the collection of animal experiments dealing with hematologic death, and the use of regression analysis to make new estimates of human mortality based on all relevant animal studies. Regression analysis of animal mortality data has shown that mortality is dependent strongly on dose rate, species, body weight, and time interval over which the exposure is delivered. The model has predicted human LD 50 s of 194, 250, 310, and 360 rad to marrow when the exposure time is a minute, an hour, a day, and a week, respectively

  13. Dose-rate models for human survival after exposure to ionizing radiation

    International Nuclear Information System (INIS)

    Jones, T.D.; Morris, M.D.; Young, R.W.

    1986-01-01

    This paper reviews new estimates of the L 50 in man by Mole and by Rotblat, the biological processes contributing to hematologic death, the collection of animal experiments dealing with hematologic death, and the use of regression analysis to make new estimates of human mortality based on all relevant animal studies. Regression analysis of animal mortality data has shown that mortality is dependent strongly on dose rate, species, body weight, and time interval over which the exposure is delivered. The model has predicted human LD 50 s of 194, 250, 310, and 360 rad to marrow when the exposure time is a minute, an hour, a day, and a week, respectively

  14. Association between pretreatment Glasgow prognostic score and gastric cancer survival and clinicopathological features: a meta-analysis

    Directory of Open Access Journals (Sweden)

    Zhang CX

    2016-06-01

    Full Text Available Chun-Xiao Zhang,* Shu-Yi Wang,* Shuang-Qian Chen, Shuai-Long Yang, Lu Wan, Bin Xiong Department of Oncology, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological Behaviors and Hubei Cancer Clinical Study Center, Wuhan, Hubei, People’s Republic of China *These authors contributed equally to this work Background: Glasgow prognostic score (GPS is widely known as a systemic inflammatory-based marker. The relationship between pretreatment GPS and gastric cancer (GC survival and clinicopathological features remains controversial. The aim of the study was to conduct a meta-analysis of published studies to evaluate the association between pretreatment GPS and survival and clinicopathological features in GC patients. Methods: We searched PubMed, Embase, MEDLINE, and BioMed databases for relevant studies. Combined analyses were used to assess the association between pretreatment GPS and overall survival, disease-free survival, and clinicopathological parameters by Stata Version 12.0. Results: A total of 14 studies were included in this meta-analysis, including 5,579 GC patients. The results indicated that pretreatment high GPS (HGPS predicted poor overall survival (hazard ratio =1.51, 95% CI: 1.37–1.66, P<0.01 and disease-free survival (hazard ratio =1.45, 95% CI: 1.26–1.68, P<0.01 in GC patients. Pretreatment HGPS was also significantly associated with advanced tumor–node–metastasis stage (odds ratio [OR] =3.09, 95% CI: 2.11–4.53, P<0.01, lymph node metastasis (OR =4.60, 95% CI: 3.23–6.56, P<0.01, lymphatic invasion (OR =3.04, 95% CI: 2.00–4.62, P<0.01, and venous invasion (OR =3.56, 95% CI: 1.81–6.99, P<0.01. Conclusion: Our meta-analysis indicated that pretreatment HGPS could be a predicative factor of poor survival outcome and clinicopathological features for GC patients. Keywords: Glasgow prognostic score, gastric cancer, survival, clinicopathological feature

  15. Association of CDX2 Expression With Survival in Early Colorectal Cancer: A Systematic Review and Meta-analysis.

    Science.gov (United States)

    Tomasello, Gianluca; Barni, Sandro; Turati, Luca; Ghidini, Michele; Pezzica, Ezio; Passalacqua, Rodolfo; Petrelli, Fausto

    2018-02-15

    CDX2 is a homeobox gene encoding transcriptional factors for intestinal organogenesis and represents a specific marker of colorectal adenocarcinoma (CRC) differentiation. We have evaluated if CDX2 expression is associated with better overall and disease-free survival (OS and DFS) in patients with CRC. PubMed, SCOPUS, EMBASE, The Cochrane Library, and Web of Science (from inception to July 2017) were systematically reviewed for relevant studies on adult patients with CRC where OS and DFS were calculated according to CDX2 expression in uni- or multivariate analysis were included. Hazard ratio (HR) for mortality and/or disease progression was calculated. The search produced 16 studies suitable for inclusion (6291 individual patients). The meta-analysis showed a reduced risk of death for patients with CDX2-positive CRC in 14 studies (HR, 0.5; 95% confidence interval [CI], 0.38-0.66; P < .001 according to random effect model). In 6 studies where only DFS data was available, CDX2 expression led to a 52% lower risk of relapse or death (HR, 0.48; 95% CI, 0.39-0.59; P < .001 according to random effect model). The results did not change as a function of ethnicity, type of study, CDX2 detection modality, or stage. Interestingly, in stages II to III, CDX2 expression was associated with a 70% lower risk of death (HR, 0.3; 95% CI, 0.12-0.77; P = .01). CDX2 expression confirms to be a strong prognostic factor in stage II and III CRC. In this setting, along with other clinical and pathologic factors, the lack of expression of CDX2 may be considered an important variable when deciding for adjuvant chemotherapy. Copyright © 2018 Elsevier Inc. All rights reserved.

  16. Breast cancer detection and survival among women with cosmetic breast implants: systematic review and meta-analysis of observational studies.

    Science.gov (United States)

    Lavigne, Eric; Holowaty, Eric J; Pan, Sai Yi; Villeneuve, Paul J; Johnson, Kenneth C; Fergusson, Dean A; Morrison, Howard; Brisson, Jacques

    2013-04-29

    To evaluate whether the stage distribution among women diagnosed as having breast cancer differs between those who have received breast implants for cosmetic purposes and those with no implants and to evaluate whether cosmetic breast augmentation before the detection of breast cancer is a predictor of post-diagnosis survival. Systematic review of observational studies with two meta-analyses. Systematic search of the literature published before September 2012 conducted in Medline, Embase, Global health, CINAHL, IPAB, and PsycINFO. Eligible publications were those that included women diagnosed as having breast cancer and who had had augmentation mammaplasty for cosmetic purposes. The overall odds ratio of the first meta-analysis based on 12 studies was 1.26 (95% confidence interval 0.99 to 1.60; P=0.058; I(2)=35.6%) for a non-localized stage of breast cancer at diagnosis comparing women with implants who had breast cancer and women without implants who had breast cancer. The second meta-analysis, based on five studies, evaluated the relation between cosmetic breast implantation and survival. This meta-analysis showed reduced survival after breast cancer among women who had implants compared with those who did not (overall hazard ratio for breast cancer specific mortality 1.38, 95% confidence interval 1.08 to 1.75). The research published to date suggests that cosmetic breast augmentation adversely affects the survival of women who are subsequently diagnosed as having breast cancer. These findings should be interpreted with caution, as some studies included in the meta-analysis on survival did not adjust for potential confounders. Further investigations are warranted regarding diagnosis and prognosis of breast cancer among women with breast implants.

  17. Stochastic modeling analysis and simulation

    CERN Document Server

    Nelson, Barry L

    1995-01-01

    A coherent introduction to the techniques for modeling dynamic stochastic systems, this volume also offers a guide to the mathematical, numerical, and simulation tools of systems analysis. Suitable for advanced undergraduates and graduate-level industrial engineers and management science majors, it proposes modeling systems in terms of their simulation, regardless of whether simulation is employed for analysis. Beginning with a view of the conditions that permit a mathematical-numerical analysis, the text explores Poisson and renewal processes, Markov chains in discrete and continuous time, se

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

    OpenAIRE

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

    2003-01-01

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

  19. Bruxism and dental implant failures: a multilevel mixed effects parametric survival analysis approach.

    Science.gov (United States)

    Chrcanovic, B R; Kisch, J; Albrektsson, T; Wennerberg, A

    2016-11-01

    Recent studies have suggested that the insertion of dental implants in patients being diagnosed with bruxism negatively affected the implant failure rates. The aim of the present study was to investigate the association between the bruxism and the risk of dental implant failure. This retrospective study is based on 2670 patients who received 10 096 implants at one specialist clinic. Implant- and patient-related data were collected. Descriptive statistics were used to describe the patients and implants. Multilevel mixed effects parametric survival analysis was used to test the association between bruxism and risk of implant failure adjusting for several potential confounders. Criteria from a recent international consensus (Lobbezoo et al., J Oral Rehabil, 40, 2013, 2) and from the International Classification of Sleep Disorders (International classification of sleep disorders, revised: diagnostic and coding manual, American Academy of Sleep Medicine, Chicago, 2014) were used to define and diagnose the condition. The number of implants with information available for all variables totalled 3549, placed in 994 patients, with 179 implants reported as failures. The implant failure rates were 13·0% (24/185) for bruxers and 4·6% (155/3364) for non-bruxers (P bruxism was a statistically significantly risk factor to implant failure (HR 3·396; 95% CI 1·314, 8·777; P = 0·012), as well as implant length, implant diameter, implant surface, bone quantity D in relation to quantity A, bone quality 4 in relation to quality 1 (Lekholm and Zarb classification), smoking and the intake of proton pump inhibitors. It is suggested that the bruxism may be associated with an increased risk of dental implant failure. © 2016 John Wiley & Sons Ltd.

  20. Prehospital critical care for out-of-hospital cardiac arrest: An observational study examining survival and a stakeholder-focused cost analysis.

    Science.gov (United States)

    von Vopelius-Feldt, Johannes; Powell, Jane; Morris, Richard; Benger, Jonathan

    2016-12-07

    Survival rates from out-of-hospital cardiac arrest (OHCA) remain low, despite remarkable efforts to improve care. A number of ambulance services in the United Kingdom (UK) have developed prehospital critical care teams (CCTs) which attend critically ill patients, including OHCA. However, current scientific evidence describing CCTs attending OHCA is sparse and research to date has not demonstrated clear benefits from this model of care. This prospective, observational study will describe the effect of CCTs on survival from OHCA, when compared to advanced-life-support (ALS), the current standard of prehospital care in the UK. In addition, we will describe the association between individual critical care interventions and survival, and also the costs of CCTs for OHCA. To examine the effect of CCTs on survival from OHCA, we will use routine Utstein variables data already collected in a number of UK ambulance trusts. We will use propensity score matching to adjust for imbalances between the CCT and ALS groups. The primary outcome will be survival to hospital discharge, with the secondary outcome of survival to hospital admission. We will record the critical care interventions delivered during CCT attendance at OHCA. We will describe frequencies and aim to use multiple logistic regression to examine possible associations with survival. Finally, we will undertake a stakeholder-focused cost analysis of CCTs for OHCA. This will utilise a previously published Emergency Medical Services (EMS) cost analysis toolkit and will take into account the costs incurred from use of a helicopter and the proportion of these costs currently covered by charities in the UK. Prehospital critical care for OHCA is not universally available in many EMS. In the UK, it is variable and largely funded through public donations to charities. If this study demonstrates benefit from CCTs at an acceptable cost to the public or EMS commissioners, it will provide a rationale to increase funding and service

  1. Lung Shunt Fraction prior to Yttrium-90 Radioembolization Predicts Survival in Patients with Neuroendocrine Liver Metastases: Single-Center Prospective Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Ludwig, Johannes M. [Yale University, Division of Interventional Radiology, Department of Radiology and Biomedical Imaging (United States); Ambinder, Emily McIntosh [John Hopkins University School of Medicine, Department of Diagnostic Radiology (United States); Ghodadra, Anish [University of Pittsburgh School of Medicine, Interventional Radiology, Department of Radiology (United States); Xing, Minzhi [Yale University, Division of Interventional Radiology, Department of Radiology and Biomedical Imaging (United States); Prajapati, Hasmukh J. [The University of Tennessee Health Science Center, Division of Interventional Radiology, Department of Radiology (United States); Kim, Hyun S., E-mail: kevin.kim@yale.edu [Yale University, Division of Interventional Radiology, Department of Radiology and Biomedical Imaging (United States)

    2016-07-15

    ObjectiveTo investigate survival outcomes following radioembolization with Yttrium-90 (Y90) for neuroendocrine tumor liver metastases (NETLMs). This study was designed to assess the efficacy of Y90 radioembolization and to evaluate lung shunt fraction (LSF) as a predictor for survival.MethodsA single-center, prospective study of 44 consecutive patients (median age: 58.5 years, 29.5 % male) diagnosed with pancreatic (52.3 %) or carcinoid (47.7 %) NETLMs from 2006 to 2012 who underwent Y90 radioembolization was performed. Patients’ baseline characteristics, including LSF and median overall survival (OS) from first Y90 radioembolization, were recorded and compared between patients with high (≥10 %) and low (<10 %) LSF. Baseline comparisons were performed using Fisher’s exact tests for categorical and Mann–Whitney U test for continuous variables. Survival was calculated using the Kaplan–Meier method. Univariate (Wilcoxon rank-sum test) and multivariate analyses (Cox Proportional Hazard Model) for risk factor analysis were performed.ResultsThere was no statistically significant difference in age, gender, race, tumor properties, or previous treatments between patients with high (n = 15) and low (n = 29) LSF. The median OS was 27.4 months (95 %CI 12.73–55.23), with 4.77 months (95 %CI 2.87–26.73) for high and 42.77 months (95 %CI 18.47–59.73) for low LSF (p = 0.003). Multivariate analysis identified high LSF (p = 0.001), total serum bilirubin >1.2 mg (p = 0.016), and lack of pretreatment with octreotide (p = 0.01) as independent prognostic factors for poorer survival. Tumor type and total radiation dose did not predict survival.ConclusionsLSF ≥10 %, elevated bilirubin levels, and lack of pretreatment with octreotide were found to be independent prognostic factors for poorer survival in patients with NETLMs.

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

    Science.gov (United States)

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

    2017-01-01

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

  3. Brachytherapy Is Associated With Improved Survival in Inoperable Stage I Endometrial Adenocarcinoma: A Population-Based Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Acharya, Sahaja; Perkins, Stephanie M.; DeWees, Todd; Fischer-Valuck, Benjamin W. [Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri (United States); Mutch, David G.; Powell, Matthew A. [Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, Missouri (United States); Schwarz, Julie K. [Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri (United States); Grigsby, Perry W., E-mail: pgrigsby@radonc.wustl.edu [Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri (United States)

    2015-11-01

    Purpose: To assess the use of brachytherapy (BT) with or without external beam radiation (EBRT) in inoperable stage I endometrial adenocarcinoma in the United States and to determine the effect of BT on overall survival (OS) and cause-specific survival (CSS). Methods and Materials: Data between 1998 and 2011 from the National Cancer Institute's Surveillance, Epidemiology and End Results database were analyzed. Coarsened exact matching was used to adjust for differences in age and grade between patients who received BT and those who did not. Prognostic factors affecting OS and CSS were evaluated using the Kaplan-Meier product-limit method and a Cox proportional hazards regression model. Results: A total of 460 patients with inoperable stage I endometrial adenocarcinoma treated with radiation therapy were identified. Radiation consisted of either EBRT (n=260) or BT with or without EBRT (n=200). The only factor associated with BT use was younger patient age (median age, 72 vs 76 years, P=.001). Patients who received BT had a higher 3-year OS (60% vs 47%, P<.001) and CSS (82% vs 74%, P=.032) compared with those who did not. On multivariate analysis, BT use was independently associated with an improved OS (hazard ratio [HR] 0.67, 95% confidence interval [CI] 0.52-0.87) and CSS (HR 0.61, 95% CI 0.39-0.93). When patients were matched on age, BT use remained significant on multivariate analysis for OS (HR 0.65, 95% CI 0.48-0.87) and CSS (HR 0.52, 95% CI 0.31-0.84). When matched on age and grade, BT remained independently associated with improved OS and CSS (OS HR 0.62, 95% CI 0.46-0.83; CSS HR 0.57, 95% CI 0.34-0.92). Conclusion: Brachytherapy is independently associated with improved OS and CSS. It should be considered as part of the treatment regimen for stage I inoperable endometrial cancer patients undergoing radiation.

  4. Brachytherapy Is Associated With Improved Survival in Inoperable Stage I Endometrial Adenocarcinoma: A Population-Based Analysis

    International Nuclear Information System (INIS)

    Acharya, Sahaja; Perkins, Stephanie M.; DeWees, Todd; Fischer-Valuck, Benjamin W.; Mutch, David G.; Powell, Matthew A.; Schwarz, Julie K.; Grigsby, Perry W.

    2015-01-01

    Purpose: To assess the use of brachytherapy (BT) with or without external beam radiation (EBRT) in inoperable stage I endometrial adenocarcinoma in the United States and to determine the effect of BT on overall survival (OS) and cause-specific survival (CSS). Methods and Materials: Data between 1998 and 2011 from the National Cancer Institute's Surveillance, Epidemiology and End Results database were analyzed. Coarsened exact matching was used to adjust for differences in age and grade between patients who received BT and those who did not. Prognostic factors affecting OS and CSS were evaluated using the Kaplan-Meier product-limit method and a Cox proportional hazards regression model. Results: A total of 460 patients with inoperable stage I endometrial adenocarcinoma treated with radiation therapy were identified. Radiation consisted of either EBRT (n=260) or BT with or without EBRT (n=200). The only factor associated with BT use was younger patient age (median age, 72 vs 76 years, P=.001). Patients who received BT had a higher 3-year OS (60% vs 47%, P<.001) and CSS (82% vs 74%, P=.032) compared with those who did not. On multivariate analysis, BT use was independently associated with an improved OS (hazard ratio [HR] 0.67, 95% confidence interval [CI] 0.52-0.87) and CSS (HR 0.61, 95% CI 0.39-0.93). When patients were matched on age, BT use remained significant on multivariate analysis for OS (HR 0.65, 95% CI 0.48-0.87) and CSS (HR 0.52, 95% CI 0.31-0.84). When matched on age and grade, BT remained independently associated with improved OS and CSS (OS