Frailties in Bayesian Survival Analysis ? PDF Download

Author: John Michael Gay
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: Andreas Wienke
Publisher: CRC Press
ISBN: 9781420073911
Category : Mathematics
Languages : en
Pages : 324

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Book Description
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.

Author: Luc Duchateau
Publisher: Springer Science & Business Media
ISBN: 038772835X
Category : Mathematics
Languages : en
Pages : 329

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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.

Author: Joseph G. Ibrahim
Publisher: Springer Science & Business Media
ISBN: 1475734476
Category : Medical
Languages : en
Pages : 494

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Book Description
Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis. It presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible. The applications are all from the health sciences, including cancer, AIDS, and the environment.

Author: David D. Hanagal
Publisher: Springer Nature
ISBN: 9811511810
Category : Medical
Languages : en
Pages : 307

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Book Description
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.

Author: Prof Shaban
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
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.

Author: Lili Ding
Publisher:
ISBN:
Category :
Languages : en
Pages : 181

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Book Description
Interval-censored time to event data occur in survival analysis when the event time is only known to fall into an interval and these intervals often overlap with each other. Correlated survival data occur when individuals under study are clustered or experience multiple events of interest. For correlated interval-censored data, we study Bayesian parametric frailty models and Bayesian nonparametric frailty models with Dirichlet process mixtures. Statistical analysis and model selection methods based on Monte Carlo simulation are developed. Simulation studies and the analysis of bivariate interval-censored age at onset of puberty illustrate the performance and applications of the proposed methodologies.

Author: Il Do Ha
Publisher: Springer
ISBN: 9811065578
Category : Mathematics
Languages : en
Pages : 288

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Book Description
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.

Author: Takeshi Emura
Publisher: Springer
ISBN: 9811335168
Category : Medical
Languages : en
Pages : 118

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Book Description
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.

Author: Chinnaiyan Ponnuraja
Publisher: LAP Lambert Academic Publishing
ISBN: 9783838375267
Category :
Languages : en
Pages : 156

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Book Description
The main objective of this book is to study frailty models and the numerical techniques, needed to fit such models, in a unified and detailed way. Both proportional hazards models and accelerated failure time models are discussed; we consider parametric as well as semi-parametric modeling. We fit frailty models using both a frequentist (classical approach) and a Bayesian approach. The main drawback is the lack of software. We will mainly use the available public freeware package, to fit the different models, and all the programs used are also freely available from the public domain. Also software available in STATA and SAS is used to fit frailty models. WinBUGS, a freeware package, is used to perform most of the Bayesian analyzes. This book is proposed for students and for applied statisticians. It can be used as a post graduate course for students who have had a course on survival analysis and a course on applied statistical techniques. Applied statisticians will find a lot of examples in this book, which will help to get a better understanding of the theoretical ideas.