Bayesian Biostatistics (PDF)

The growth of biostatistics has been phenomenal in recent years and
has been marked by considerable technical innovation in both
methodology and computational practicality. One area that has
experienced significant growth is Bayesian methods. The growing use
of Bayesian methodology has taken place partly due to an increasing
number of practitioners valuing the Bayesian paradigm as matching
that of scientific discovery. In addition, computational advances
have allowed for more complex models to be fitted routinely to
realistic data sets.

Through examples, exercises and a combination of introductory
and more advanced chapters, this book provides an invaluable
understanding of the complex world of biomedical statistics
illustrated via a diverse range of applications taken from
epidemiology, exploratory clinical studies, health promotion
studies, image analysis and clinical trials.

Key Features:

* Provides an authoritative account of Bayesian methodology, from
its most basic elements to its practical implementation, with an
emphasis on healthcare techniques.

* Contains introductory explanations of Bayesian principles
common to all areas of application.

* Presents clear and concise examples in biostatistics
applications such as clinical trials, longitudinal studies,
bioassay, survival, image analysis and bioinformatics.

* Illustrated throughout with examples using software including
WinBUGS, OpenBUGS, SAS and various dedicated R
programs.

* Highlights the differences between the Bayesian and classical
approaches.

* Supported by an accompanying website hosting free software
and case study guides.

Bayesian Biostatistics introduces the reader smoothly
into the Bayesian statistical methods with chapters that gradually
increase in level of complexity. Master students in biostatistics,
applied statisticians and all researchers with a good background in
classical statistics who have interest in Bayesian methods will
find this book useful.