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
multivariate survival data, and special types of hierarchical
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. Joseph G. Ibrahim
is Associate Professor of Biostatistics at the Harvard School of
Public Health and Dana-Farber Cancer Institute; Ming-Hui Chen is
Associate Professor of Mathematical Science at Worcester
Polytechnic Institute; Debajyoti Sinha is Associate Professor of
Biostatistics at the Medical University of South Carolina.
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