Book description
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.
Emmanuel Lesaffre, Professor of Statistics,
Biostatistical Centre, Catholic University of Leuven, Leuven, Belgium.
Dr Lesaffre has worked on and studied various areas of biostatistics
for 25 years. He has taught a variety of courses to students from many
disciplines, from medicine and pharmacy, to statistics and
engineering, teaching Bayesian statistics for the last 5 years. Having
published over 200 papers in major statistical and medical journals,
he has also Co-Edited the book Disease Mapping and Risk Assessment
for Public Health, and was the Associate Editor for
Biometrics. He is currently Co-Editor of the journal
“Statistical Modelling: An International Journal”, Special Editor of
two volumes on Statistics in Dentistry in Statistical Methods in
Medical Research, and a member of the Editorial Boards of numerous journals.
Andrew Lawson, Professor of Statistics, Dept of Epidemiology
& Biostatistics, University of South Carolina, USA. Dr Lawson has
considerable and wide ranging experience in the development of
statistical methods for spatial and environmental epidemiology. He has
solid experience in teaching Bayesian statistics to students studying
biostatistics and has also written two books and numerous journal
articles in the biostatistics area. Dr Lawson has also guest edited
two special issues of “Statistics in Medicine” focusing on Disease
Mapping. He is a member of the editorial boards of the journals:
Statistics in Medicine and .
