Frailty Models In Survival Analysis

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Frailty Models In Survival Analysis

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Author : Andreas Wienke
language : en
Publisher: CRC Press
Release Date : 2010-07-26

Frailty Models In Survival Analysis written by Andreas Wienke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-26 with Mathematics categories.

The concept of frailty offers a convenient way to introduce unobserved heterogeneity and associations into models for survival data. In its simplest form, frailty is an unobserved random proportionality factor that modifies the hazard function of an individual or a group of related individuals. Frailty Models in Survival Analysis presents a comprehensive overview of the fundamental approaches in the area of frailty models. The book extensively explores how univariate frailty models can represent unobserved heterogeneity. It also emphasizes correlated frailty models as extensions of univariate and shared frailty models. The author analyzes similarities and differences between frailty and copula models; discusses problems related to frailty models, such as tests for homogeneity; and describes parametric and semiparametric models using both frequentist and Bayesian approaches. He also shows how to apply the models to real data using the statistical packages of R, SAS, and Stata. The appendix provides the technical mathematical results used throughout. Written in nontechnical terms accessible to nonspecialists, this book explains the basic ideas in frailty modeling and statistical techniques, with a focus on real-world data application and interpretation of the results. By applying several models to the same data, it allows for the comparison of their advantages and limitations under varying model assumptions. The book also employs simulations to analyze the finite sample size performance of the models.

Modeling Survival Data Using Frailty Models

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Author : David D. Hanagal
language : en
Publisher: Springer Nature
Release Date : 2019-11-16

Modeling Survival Data Using Frailty Models written by David D. Hanagal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-16 with Medical categories.

This book presents the basic concepts of survival analysis and frailty models, covering both fundamental and advanced topics. It focuses on applications of statistical tools in biology and medicine, highlighting the latest frailty-model methodologies and applications in these areas. After explaining the basic concepts of survival analysis, the book goes on to discuss shared, bivariate, and correlated frailty models and their applications. It also features nine datasets that have been analyzed using the R statistical package. Covering recent topics, not addressed elsewhere in the literature, this book is of immense use to scientists, researchers, students and teachers.

The Frailty Model

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Author : Luc Duchateau
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-23

The Frailty Model written by Luc Duchateau and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-10-23 with Mathematics categories.

Readers will find in the pages of this book a treatment of the statistical analysis of clustered survival data. Such data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. A typical example is the time to death in cancer patients, with patients clustered in hospitals. Frailty models provide a powerful tool to analyze clustered survival data. In this book different methods based on the frailty model are described and it is demonstrated how they can be used to analyze clustered survival data. All programs used for these examples are available on the Springer website.

New Applications Of Frailty Models In Survival Analysis

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Author : Jong Hyeon Jeong
language : en
Publisher:
Release Date : 1996

New Applications Of Frailty Models In Survival Analysis written by Jong Hyeon Jeong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with categories.

Analysis Of Multivariate Survival Data

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Author : Philip Hougaard
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Analysis Of Multivariate Survival Data written by Philip Hougaard and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.

Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent. This book extends the field by allowing for multivariate times. As the field is rather new, the concepts and the possible types of data are described in detail. Four different approaches to the analysis of such data are presented from an applied point of view.

Statistical Modelling Of Survival Data With Random Effects

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Author : Il Do Ha
language : en
Publisher: Springer
Release Date : 2018-01-02

Statistical Modelling Of Survival Data With Random Effects written by Il Do Ha and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-02 with Mathematics categories.

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 researchers in medical and genetics fields, graduate students, and PhD (bio) statisticians.

Survival Analysis With Correlated Endpoints

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Author : Takeshi Emura
language : en
Publisher: Springer
Release Date : 2019-03-25

Survival Analysis With Correlated Endpoints written by Takeshi Emura and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-25 with Medical categories.

This book introduces readers to advanced statistical methods for analyzing survival data involving correlated endpoints. In particular, it describes statistical methods for applying Cox regression to two correlated endpoints by accounting for dependence between the endpoints with the aid of copulas. The practical advantages of employing copula-based models in medical research are explained on the basis of case studies. In addition, the book focuses on clustered survival data, especially data arising from meta-analysis and multicenter analysis. Consequently, the statistical approaches presented here employ a frailty term for heterogeneity modeling. This brings the joint frailty-copula model, which incorporates a frailty term and a copula, into a statistical model. The book also discusses advanced techniques for dealing with high-dimensional gene expressions and developing personalized dynamic prediction tools under the joint frailty-copula model. To help readers apply the statistical methods to real-world data, the book provides case studies using the authors’ original R software package (freely available in CRAN). The emphasis is on clinical survival data, involving time-to-tumor progression and overall survival, collected on cancer patients. Hence, the book offers an essential reference guide for medical statisticians and provides researchers with advanced, innovative statistical tools. The book also provides a concise introduction to basic multivariate survival models.

Survival Analysis State Of The Art

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Author : John P. Klein
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09

Survival Analysis State Of The Art written by John P. Klein and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Mathematics categories.

Survival analysis is a highly active area of research with applications spanning the physical, engineering, biological, and social sciences. In addition to statisticians and biostatisticians, researchers in this area include epidemiologists, reliability engineers, demographers and economists. The economists survival analysis by the name of duration analysis and the analysis of transition data. We attempted to bring together leading researchers, with a common interest in developing methodology in survival analysis, at the NATO Advanced Research Workshop. The research works collected in this volume are based on the presentations at the Workshop. Analysis of survival experiments is complicated by issues of censoring, where only partial observation of an individual's life length is available and left truncation, where individuals enter the study group if their life lengths exceed a given threshold time. Application of the theory of counting processes to survival analysis, as developed by the Scandinavian School, has allowed for substantial advances in the procedures for analyzing such experiments. The increased use of computer intensive solutions to inference problems in survival analysis~ in both the classical and Bayesian settings, is also evident throughout the volume. Several areas of research have received special attention in the volume.

Shared Frailty Survival Analysis Using Semiparametric Bayesian Method

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Author : Prof Shaban
language : en
Publisher:
Release Date : 2015

Shared Frailty Survival Analysis Using Semiparametric Bayesian Method written by Prof Shaban and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.

In survival data analysis, the proportional hazard model was introduced by Cox (1972) in order to estimate the effects of different covariates influencing the time-to-event data. The proportional hazard model has been used extensively in biomedicine, reliability engineering and, recently, interest in its application in different areas of knowledge has increased. However, proportional hazard model makes a number of assumptions, which may be violated. The object of this article is to present a Bayesian analysis for survival models with frailty under additive framework for the hazard function in contrast to proportional hazard model. Frailty models in survival analysis deal with the unobserved heterogeneity among subjects. Gibbs sampling technique is used to assess the posterior quantities of interest. An illustrative analysis within the context of survival time data is given.

Advanced Survival Models

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Author : Catherine Legrand
language : en
Publisher: CRC Press
Release Date : 2021-03-22

Advanced Survival Models written by Catherine Legrand and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-22 with Mathematics categories.

Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications, and in particular in medical research. During the last twenty years, several extensions of "classical" survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions, such as frailty models (in case of unobserved heterogeneity or clustered data), cure models (when a fraction of the population will not experience the event of interest), competing risk models (in case of different types of event), and joint survival models for a time-to-event endpoint and a longitudinal outcome. Features Presents state-of-the art approaches for different advanced survival models including frailty models, cure models, competing risk models and joint models for a longitudinal and a survival outcome Uses consistent notation throughout the book for the different techniques presented Explains in which situation each of these models should be used, and how they are linked to specific research questions Focuses on the understanding of the models, their implementation, and their interpretation, with an appropriate level of methodological development for masters students and applied statisticians Provides references to existing R packages and SAS procedure or macros, and illustrates the use of the main ones on real datasets This book is primarily aimed at applied statisticians and graduate students of statistics and biostatistics. It can also serve as an introductory reference for methodological researchers interested in the main extensions of classical survival analysis.