Overview

Bayesian analyses have made important inroads in modern clinical research due, in part, to the incorporation of the traditional tools of noninformative priors as well as the modern innovations of adaptive randomization and predictive power. Presenting an introductory perspective to modern Bayesian procedures, Elementary Bayesian Biostatistics explores Bayesian principles and illustrates their application to healthcare research. Building on the basics of classic biostatistics and algebra, this easy-to-read book provides a clear overview of the subject. It focuses on the history and mathematical foundation of Bayesian procedures, before discussing their implementation in healthcare research from first principles. The author also elaborates on the current controversies between Bayesian and frequentist biostatisticians. The book concludes with recommendations for Bayesians to improve their standing in the clinical trials community. Calculus derivations are relegated to the appendices so as not to overly complicate the main text. As Bayesian methods gain more acceptance in healthcare, it is necessary for clinical scientists to understand Bayesian principles. Applying Bayesian analyses to modern healthcare research issues, this lucid introduction helps readers make the correct choices in the development of clinical research programs.

Full Product Details

9780367388799

Table of Contents

Reviews

""This is a fun book for teaching oneself (or others) both some fundamental principles of epidemiology and clinical trials and fundamental principles of probability and statistical inference from the point of view of a practising clinical scientist who is also a very knowledgeable, no-nonsense Bayesian. What makes it very different from common textbooks is its blending of history, controversy (about probability, statistics, and clinical studies), real-life examples, and wise practical advice. … a very readable introduction to basic probability models, inference questions, and Bayesian answers without calculus and Markov chain Monte Carlo. …"" —International Statistical Review, 2008 "". . . provides a very clear exposition of Bayesian thinking for applications in biostatistics. The book’s strengths lie in its careful discussions of Bayesian thinking or problems in health care research, including the constructions of priors and loss functions . . . a welcome addition to the growing number of books that describe Bayesian modeling from an applied perspective."" –Jim Albert, Bowling Green State University, in JASA, December 2008

This is a fun book for teaching oneself (or others) both some fundamental principles of epidemiology and clinical trials and fundamental principles of probability and statistical inference from the point of view of a practising clinical scientist who is also a very knowledgeable, no-nonsense Bayesian. What makes it very different from common textbooks is its blending of history, controversy (about probability, statistics, and clinical studies), real-life examples, and wise practical advice. ... a very readable introduction to basic probability models, inference questions, and Bayesian answers without calculus and Markov chain Monte Carlo. ... -International Statistical Review, 2008 . . . provides a very clear exposition of Bayesian thinking for applications in biostatistics. The book's strengths lie in its careful discussions of Bayesian thinking or problems in health care research, including the constructions of priors and loss functions . . . a welcome addition to the growing number of books that describe Bayesian modeling from an applied perspective. -Jim Albert, Bowling Green State University, in JASA, December 2008

Author Information

Moyé, Lemuel A.

Tab Content 6

Customer Reviews

Recent Reviews

Countries Available