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      Survival Models on Unobserved Heterogeneity and their Applications in Analyzing Large-scale Survey Data

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          Abstract

          In survival analysis, researchers often encounter multivariate survival time data, in which failure times are correlated even in the presence of model covariates. It is argued that because observations are clustered by unobserved heterogeneity, the application of standard survival models can result in biased parameter estimates and erroneous model-based predictions. In this article, the author describes and compares four methods handling unobserved heterogeneity in survival analysis: the Andersen-Gill approach, the robust sandwich variance estimator, the hazard model with individual frailty, and the retransformation method. An empirical analysis provides strong evidence that in the presence of strong unobserved heterogeneity, the application of a standard survival model can yield equally robust parameter estimates and the likelihood ratio statistic as does a corresponding model adding an additional parameter for random effects. When predicting the survival function, however, a standard model on multivariate survival time data can result in serious prediction bias. The retransformation method is effective to derive an adjustment factor for correctly predicting the survival function.

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          Most cited references44

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          Modeling Survival Data: Extending the Cox Model

          This is a book for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyse multiple/correlated event data using marginal and random effects (frailty) models. It covers the use of residuals and diagnostic plots to identify influential or outlying observations, assess proportional hazards and examine other aspects of goodness of fit. Other topics include time-dependent covariates and strata, discontinuous intervals of risk, multiple time scales, smoothing and regression splines, and the computation of expected survival curves. A knowledge of counting processes and martingales is not assumed as the early chapters provide an introduction to this area. The focus of the book is on actual data examples, the analysis and interpretation of the results, and computation. The methods are now readily available in SAS and S-Plus and this book gives a hands-on introduction, showing how to implement them in both packages, with worked examples for many data sets. The authors call on their extensive experience and give practical advice, including pitfalls to be avoided. Terry Therneau is Head of the Section of Biostatistics, Mayo Clinic, Rochester, Minnesota. He is actively involved in medical consulting, with emphasis in the areas of chronic liver disease, physical medicine, hematology, and laboratory medicine, and is an author on numerous papers in medical and statistical journals. He wrote two of the original SAS procedures for survival analysis (coxregr and survtest), as well as the majority of the S-Plus survival functions. Patricia Grambsch is Associate Professor in the Division of Biostatistics, School of Public Health, University of Minnesota. She has collaborated extensively with physicians and public health researchers in chronic liver disease, cancer prevention, hypertension clinical trials and psychiatric research. She is a fellow the American Statistical Association and the author of many papers in medical and statistical journals.
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            The Robust Inference for the Cox Proportional Hazards Model

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              • Article: not found

              Approximate Inference in Generalized Linear Mixed Models

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                Author and article information

                Journal
                101558406
                39164
                J Biom Biostat
                J Biom Biostat
                Journal of biometrics & biostatistics
                2155-6180
                8 August 2014
                30 April 2014
                2014
                16 December 2014
                : 5
                : 1000191
                Affiliations
                [1 ]DoD Deployment Health Clinical Center, Walter Reed National Military Medical Center, Bethesda, MD 20889, USA
                [2 ]Department of Psychiatry, F. Edward Hebert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
                Author notes
                [* ] Corresponding author: Xian Liu, DoD Deployment Health Clinical Center, Walter Reed National Military Medical Center, Bethesda, MD 20889, USA, Xian.Liu@ 123456usuhs.edu
                Article
                NIHMS619095
                10.4172/2155-6180.1000191
                4267525
                25525559
                f693076f-9211-4dfd-9281-24676796d19a
                Copyright: © 2014 Liu X.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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                clusters,correlated data,hazard rate models,survival analysis,unobserved,heterogeneity

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