by Gary L Rosner (Author), Purushottam W. Laud (Author), Wesley O. Johnson (Author)
Praise for Bayesian Thinking in Biostatistics:
"This
thoroughly modern Bayesian book …is a 'must have' as a textbook or a
reference volume. Rosner, Laud and Johnson make the case for Bayesian
approaches by melding clear exposition on methodology with serious
attention to a broad array of illuminating applications. These are
activated by excellent coverage of computing methods and provision of
code. Their content on model assessment, robustness, data-analytic
approaches and predictive assessments…are essential to valid practice.
The numerous exercises and professional advice make the book ideal as a
text for an intermediate-level course…"
-Thomas Louis, Johns Hopkins University
"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 it a
complete self- contained introduction to Bayesian inference for
biomedical problems….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."
- Peter Mueller, University of Texas
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 use, such
as rates and proportions, regression models, clinical trial design, and
methods for evaluating diagnostic tests.
Key Features
- Applies a Bayesian perspective to applications in biomedical science
- Highlights advances in clinical trial design
- Goes
beyond standard statistical models in the book by introducing Bayesian
nonparametric methods and illustrating their uses in data analysis
- Emphasizes estimation of biomedically relevant quantities and assessment of the uncertainty in this estimation
- Provides programs in the BUGS language, with variants for JAGS and Stan, that one can use or adapt for one's own research
