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.
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