Bayesian Thinking in Biostatistics
Edition: First edition
Series: Chapman & Hall/CRC Texts in Statistical Science
Publisher: Taylor & Francis Group
Published: 16 March, 2021
Place of publication: Boca Raton, Flrida, USA; London, UK
Language: eng
Format: Hardcover
Number of pages: 624
ISBN-13: 9781439800089
Copyright date: ©2021
Description: The book introduces all the important topics that one would usually cover in a beginning graduate level class on Bayesian biostatistics. The careful introduction of the Bayesian viewpoint and the mechanics of implementing Bayesian inference in the early chapters makes the it a complete self contained introduction to Bayesian inference for biomedical problems. As a natural consequence of the biostatistics target audience all methods and discussions are well motivated by specific inference problems as they arise in biomedical research. Even without this target audience in mind, the same motivating problems would be a great pedagogical choice to keep discussion focused and to make many modeling and inference choices intuitively appealing. Overall the authors have made well informed choices about including material and topics, and about the level of details of some of the formal presentation, fittingly leaving some details to references. Another great feature for using this book as a textbook is the inclusion of extensive problem sets, going well beyond construed and simple problems. Many exercises consider real data and studies, providing very useful examples in addition to serving as problems. In summary, the book is a great introduction to Bayesian inference for readers with an interest in biomedical applications, but who do not necessarily have a formal biostatistics background.
Notes: Includes bibliographical references and index.
Print version: Rosner, Gary L.. Bayesian thinking in biostatistics First edition. Boca Raton : CRC Press, 2021
Table of contents: title: -- Scientific data analysis; title: -- Fundamentals I : Bayes theorem, knowledge distributions, prediction; title: -- Fundamentals II : models for exchangeable observations; title: -- Computational methods for Bayesian analysis; title: -- Comparing populations; title: -- Specifying prior distributions; title: -- Linear regression; title: -- Binary response regression; title: -- Poisson and non-linear regression; title: -- Model assessment; title: -- Survival modeling I : models for exchangeable observations; title: -- Survival modeling 2 : time-to-event regression models; title: -- Clinical trial designs; title: -- Hierarchical models and longitudinal data; title: -- Diagnostic tests.