Paperback : HK$640.00
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.
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.
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.
Moyé, Lemuel A.
"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
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