Includes bibliographical references and index.

Scientific data analysis -- Fundamentals I : Bayes theorem, knowledge distributions, prediction -- Fundamentals II : models for exchangeable observations -- Computational methods for Bayesian analysis -- Comparing populations -- Specifying prior distributions -- Linear regression -- Binary response regression -- Poisson and non-linear regression -- Model assessment -- Survival modeling I : models for exchangeable observations -- Survival modeling 2 : time-to-event regression models -- Clinical trial designs -- Hierarchical models and longitudinal data -- Diagnostic tests.

"With a focus on incorporating sensible prior distributions and discussions on many recent developments in Bayesian methodologies, Bayesian Thinking in Biostatistics considers statistical issues in biomedical research. The book emphasizes greater collaboration between biostatisticians and biomedical researchers. The text includes an overview of Bayesian statistics, a discussion of many of the methods biostatisticians frequently, such as rates and proportions, regression models, clinical trial design, and methods for evaluating diagnostic tests"--