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.
- 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.
Keywords: Biostatistics, biomedical statistics, Bayesian methodology, healthcare statistics, Bayesian healthcare statistics, statistics clinical trials, survival data statistics, longitudinal analysis statistics, disease mapping statistics, bioassay statistics, time series statistics, bioinformatics