Bayesian Biostatistics (eBook)

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 .

"In conclusion, we consider the book by Lesaffre and
Lawson a noteworthy contribution to the dissemination of Bayesian
methods, and a good manual of reference for many common and some
specialized applications in biomedical research. The great variety
of examples and topics covered offers both advantages and
disadvantages. Some parts might be too specialized for statistics
students, but lecturers and applied statisticians will benefit a
lot from the authors' wealth of experience."
(Biometrical Journal, 15 July 2013)

"The book Bayesian Biostatisticsby Lesaffre and
Lawson, is a welcoming addition to this important area of research
in biostatistical applications. For example, in the area of
clinical trials, Bayesian methods provide flexibility and benefits
for incorporating historical data with current data and then using
the resulting posterior to make probability statements for
different outcomes".(Journal of
Biopharmaceutical Statistics, 1 January 2013)