Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book covers Bayesian survival analysis.

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.

Introduction.- Parametric Models.- Semiparametric Models.- Fraility Models.- Cure Rate Models.- Model Comparison.- Joint Models for Longitudinal and Survival Data.- Missing Covariate Data.- Design and Monitoring of Randomized Clinical Trials.- Other Topics.