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.
From the reviews:
"The analysis of time-event data arises naturally in many fields of study. This book focuses exclusively on medicine and public health but the methods presented can be applied in a number of other areas, including biology, economics and engineering. Although several previously published texts address survival analysis from a frequentist perspective, this book examines solely Bayesian approaches to survival analysis. Recent advances in computing and practical methods for prior elicitation have now made Bayesian survival analysis of complex models feasible. This book provides a comprehensive and modern treatment of the subject. In addition, the authors demonstrate the use of the statistical package BUGS for several of the models and methodologies discussed in the book. The authors provide a collection of theoretical and applied problems in the exercises at the end of each chapter."
ISI Short Book Reviews, April 2002
"This is definitely a worthwhile read for any statistician specializing in survival analysis. It is pitched so that part of it is readily usable by the medical statisitciann, but it will also provide stimulation for statisticians involved in methodological development or the writing of new software for survival analysis." International Journal of Epidemiology
"Many books have been published concerning survival analysis or Bayesian methods; Bayesian Survival Analysis is the first comprehensive treatment that combines these two important areas of statistics. Ibrahim, Chen, and Sinha have made an admirable accomplishment on the subject in a well-organized and easily accessible fashion." Journal of the American Statistical Association
"This is one of the best combinations of advanced methodology and practical applications that I have ever encountered." Technometrics, May 2002
"This is a book by three authors who are well-known for their contribution to Bayesian survival analysis. ... It is a good book with many areas of strength. ... There are several new methods, ideas, results, some of which are due to the authors. There is a good discussion of historical priors ... . Other things that strike me as new are a good technical discussion of frailty and cure models ... . I have learnt a lot and enjoyed reading the book." (Jayanta K. Ghosh, Sankhya: The Indian Journal of Statistics, Vol. 65 (3), 2003)
"This book illustrates several Bayesian techniques to analyze survival data in biology, medicine, public health, epidemiology, clinical trials, and economics. ... It could be used as a textbook in a graduate level course. ... In particular, I enjoyed the presentations of cure models and cancer vaccine trials. Biostatisticians will like reading this book from the Bayesian points of view." (Ramalingam Shanmugam, Journal of Statistical Computation and Simulation, Vol. 74 (10), 2004)
"This book offers an excellent and thorough summary of an exciting methodological development since the seventies of the last century. ... The authors offer a gentle journey through the archipelago of Bayesian Survival analysis. They combine in a pleasant way theory, examples, and exercises. ... I hope that this stimulating book may tempt many readers to enter the field of Bayesian survival analysis ... ." (Ulrich Mansmann, Metrika, September, 2004)
"It offers a presentation of Bayesian methods in Survival Analysis that is, at a time, comprehensive and suitably balanced between theory and applications; many relevant models and methods are illustrated and most of them are provided with detailed examples and case studies drawn from the medical research. ... The book offers a quite up-to-date view of Bayesian Statistics and accounts extensively for Monte Carlo-based sampling methods and for the various methods of prior elicitation, suitable to cope with non-parametric as well as with semi-parametric models." (Fabio Spizzichino, Statistics in Medicine, Vol. 23, 2004)
"This is not an elementary book. ... The book develops methodology and does this at a high level, because the reader is presumed to have a mathematical statistics background in both classical and Bayesian methods. Happily, the book is replete with examples. This is one of the best combinations of advanced methodology and practical applications that I have encountered. ... Computing support for the book comes from the package called BUGS ... ." (Technometrics, Vol. 44 (2), 2002)
"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 ... . 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." (L’Enseignement Mathématique, Vol. 48 (1-2), 2002)
"The book is about Bayesian survival analysis which is illustrated with examples that mostly use the BUGS software package. ... this is definitively a worthwhile read for any statistician specializing in survival analysis. It is pitched so that part of it is readily usable by the medical statistician, but it will also provide stimulation for statisticians involved in methodological development or the writing of new software for survival analysis." (Margaret May, International Journal of Epidemiology, Vol. 31 (2), 2002)
"This book focuses exclusively on medicine and public health but the methods presented can be applied in a number of other areas, including biology, economics and engineering. ... This book provides a comprehensive and modern treatment of the subject. In addition, the authors demonstrate the use of the statistical package BUGS for several of the models and methodologies discussed in the book. The authors provide a collection of theoretical and applied problems in the exercises at the end of each chapter." (C. M. O’Brien, Short Book Reviews, Vol. 22 (1), 2002)
"Ibrahim, Chen and Sinha command over a rich experience in both Bayesian and survival analysis. Drawing from this experience they have put together a comprehensive description of Bayesian methodology in survival analysis. The book is written for researchers and graduate students. ... The book is a useful tool for practitioners who analyze survival data using Bayesian methods." (Mathias Schaller, Statistical Papers, Vol. 47, 2005)
