Main description:

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.

Contents:

Preface. Introduction. Prologue. Basic Probability and Bayes Theorem. Compounding and the Law of Total Probability. Intermediate Compounding and Prior Distributions. Completing Your First Bayesian Computations. When Worlds Collide. Developing Prior Probability. Using Posterior Distributions: Loss and Risk. Putting It All Together. Bayesian Sample Size. Predictive Power and Adaptive Procedures. Is My Problem a Bayes Problem? Conclusions and Commentary. Appendices. Index.