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, Bayesian Biostatistics 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 Bayesian Biostatistics useful.

Preface xiii
Notation, terminology and some guidance for reading the book xvii

Part I BASIC CONCEPTS IN BAYESIAN METHODS
1 Modes of statistical inference 3
2 Bayes theorem: Computing the posterior distribution 20
3 Introduction to Bayesian inference 46
4 More than one parameter 82
5 Choosing the prior distribution 104
6 Markov chain Monte Carlo sampling 139
7 Assessing and improving convergence of the Markov chain 175
8 Software 202

Part II BAYESIAN TOOLS FOR STATISTICAL MODELING
9 Hierarchical models 227
10 Model building and assessment 267
11 Variable selection 319

Part III BAYESIAN METHODS IN PRACTICAL APPLICATIONS
12 Bioassay 365
13 Measurement error 375
14 Survival analysis 390
15 Longitudinal analysis 407
16 Spatial applications: Disease mapping and image analysis 430
17 Final chapter 456

Appendix: Distributions 460

References 484
Index 509