Shows how to apply Bayesian hierarchical modeling to geographical analyses of disease
Deals with both population and individual analyses resulting from cancer registry data
Discusses the survival and longitudinal analyses of data in health services and designed studies
Covers standard topics, such as relative risk estimation and clustering
Uses a range of actual data sets to illustrate concepts
Provides the data sets, WinBUGS ODC files, and R code on the author’s website
Focusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology provides an overview of the main areas of Bayesian hierarchical modeling and its application to the geographical analysis of disease.
The book explores a range of topics in Bayesian inference and modeling, including Markov chain Monte Carlo methods, Gibbs sampling, the Metropolis–Hastings algorithm, goodness-of-fit measures, and residual diagnostics. It also focuses on special topics, such as cluster detection; space-time modeling; and multivariate, survival, and longitudinal analyses. The author explains how to apply these methods to disease mapping using numerous real-world data sets pertaining to cancer, asthma, epilepsy, foot and mouth disease, influenza, and other diseases. In the appendices, he shows how R and WinBUGS can be useful tools in data manipulation and simulation.
Applying Bayesian methods to the modeling of georeferenced health data, Bayesian Disease Mapping proves that the application of these approaches to biostatistical problems can yield important insights into data.

Contents of the book :
BACKGROUND
Introduction
Data Sets
Bayesian Inference and Modeling
Likelihood Models
Prior Distributions
Posterior Distributions
Predictive Distributions
Bayesian Hierarchical Modeling
Hierarchical Models
Posterior Inference
Exercises
Computational Issues
Posterior Sampling
Markov Chain Monte Carlo Methods
Metropolis and Metropolis–Hastings Algorithms
Gibbs Sampling
Perfect Sampling
Posterior and Likelihood Approximations
Exercises
Residuals and Goodness-of-Fit
Model Goodness-of-Fit Measures
General Residuals
Bayesian Residuals
Predictive Residuals and the Bootstrap
Interpretation of Residuals in a Bayesian Setting
Exceedence Probabilities
Exercises
THEMES
Disease Map Reconstruction and Relative Risk Estimation
An Introduction to Case Event and Count Likelihoods
Specification of the Predictor in Case Event and Count Models
Simple Case and Count Data Models with Uncorrelated Random Effects
Correlated Heterogeneity Models
Convolution Models
Model Comparison and Goodness-of-Fit Diagnostics
Alternative Risk Models
Edge Effects
Exercises
Disease Cluster Detection
Cluster Definitions
Cluster Detection using Residuals
Cluster Detection using Posterior Measures
Cluster Models
Edge Detection and Wombling
Ecological Analysis
General Case of Regression
Biases and Misclassification Error
Putative Hazard Models
Multiple Scale Analysis
Modifiable Areal Unit Problem (MAUP)
Misaligned Data Problem (MIDP)
Multivariate Disease Analysis
Notation for Multivariate Analysis
Two Diseases
Multiple Diseases
Spatial Survival and Longitudinal Analyses
General Issues
Spatial Survival Analysis
Spatial Longitudinal Analysis
Extensions to Repeated Events
Spatiotemporal Disease Mapping
Case Event Data
Count Data
Alternative Models
Infectious Diseases
Appendix A: Basic R and WinBUGS
Appendix B: Selected WinBUGS Code
Appendix C: R Code for Thematic Mapping
References
Index