The growth of biostatistics has been phenomenal in recent years andhas been marked by considerable technical innovation in bothmethodology and computational practicality. One area that hasexperienced significant growth is Bayesian methods. The growing useof Bayesian methodology has taken place partly due to an increasingnumber of practitioners valuing the Bayesian paradigm as matchingthat of scientific discovery. In addition, computational advanceshave allowed for more complex models to be fitted routinely torealistic data sets.Through examples, exercises and a combination of introductoryand more advanced chapters, this book provides an invaluableunderstanding of the complex world of biomedical statisticsillustrated via a diverse range of applications taken fromepidemiology, exploratory clinical studies, health promotionstudies, image analysis and clinical trials.Key Features:* Provides an authoritative account of Bayesian methodology, fromits most basic elements to its practical implementation, with anemphasis on healthcare techniques.* Contains introductory explanations of Bayesian principlescommon to all areas of application.* Presents clear and concise examples in biostatisticsapplications such as clinical trials, longitudinal studies,bioassay, survival, image analysis and bioinformatics.* Illustrated throughout with examples using software includingWinBUGS, OpenBUGS, SAS and various dedicated Rprograms.* Highlights the differences between the Bayesian and classicalapproaches.* Supported by an accompanying website hosting free softwareand case study guides.Bayesian Biostatistics introduces the reader smoothlyinto the Bayesian statistical methods with chapters that graduallyincrease in level of complexity. Master students in biostatistics,applied statisticians and all researchers with a good background inclassical statistics who have interest in Bayesian methods willfind this book useful.