Ebook: Bayesian Biostatistics

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.Content:
Chapter 1 Modes of Statistical Inference (pages 1–19):
Chapter 2 Bayes Theorem: Computing the Posterior Distribution (pages 20–45):
Chapter 3 Introduction to Bayesian Inference (pages 46–81):
Chapter 4 More than One Parameter (pages 82–103):
Chapter 5 Choosing the Prior Distribution (pages 104–138):
Chapter 6 Markov Chain Monte Carlo Sampling (pages 139–174):
Chapter 7 Assessing and Improving Convergence of the Markov Chain (pages 175–201):
Chapter 8 Software (pages 202–223):
Chapter 9 Hierarchical Models (pages 225–266):
Chapter 10 Model Building and Assessment (pages 267–318):
Chapter 11 Variable Selection (pages 319–361):
Chapter 12 Bioassay (pages 363–374):
Chapter 13 Measurement Error (pages 375–389):
Chapter 14 Survival Analysis (pages 390–406):
Chapter 15 Longitudinal Analysis (pages 407–429):
Chapter 16 Spatial Applications: Disease Mapping and Image Analysis (pages 430–455):
Chapter 17 Final Chapter (pages 456–459):