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＜電子ブック＞
Bayesian Survival Analysis / by Joseph G. Ibrahim, Ming-Hui Chen, Debajyoti Sinha
(Springer Series in Statistics. ISSN:2197568X)

版	1st ed. 2001.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2001
本文言語	英語
大きさ	XIV, 480 p : online resource
著者標目	*Ibrahim, Joseph G author Chen, Ming-Hui author Sinha, Debajyoti author SpringerLink (Online service)
件　名	LCSH:Biometry LCSH:Statistics  FREE:Biostatistics FREE:Statistical Theory and Methods
一般注記	1 Introduction -- 2 Parametric Models -- 3 Semiparametric Models -- 4 Frailty Models -- 5 Cure Rate Models -- 6 Model Comparison -- 7 Joint Models for Longitudinal and Survival Data -- 8 Missing Covariate Data -- 9 Design and Monitoring of Randomized Clinical Trials -- 10 Other Topics -- List of Distributions -- References -- Author Index 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. Several topics are addressed, including parametric models, semiparametric models based on prior processes, proportional and non-proportional hazards models, frailty models, cure rate models, model selection and comparison, joint models for longitudinal and survival data, models with time varying covariates, missing covariate data, design and monitoring of clinical trials, accelerated failure time models, models for mulitivariate survival data, and special types of hierarchial survival models. Also various censoring schemes are examined including right and interval censored data. Several additional topics are discussed, including noninformative and informative prior specificiations, computing posterior qualities of interest, Bayesian hypothesis testing, variable selection, model selection with nonnested models, model checking techniques using Bayesian diagnostic methods, and Markov chain Monte Carlo (MCMC) algorithms for sampling from the posteiror and predictive distributions. The book 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 essentially from the health sciences, including cancer, AIDS, and the environment. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. This book would be most suitable for second or third year graduate students in statistics or biostatistics. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners HTTP:URL=https://doi.org/10.1007/978-1-4757-3447-8

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