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.

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 studiesbioassay, 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 biostatisticsapplied statisticians and all researchers with a good background inclassical statistics who have interest in Bayesian methods willfind this book useful.