Bayesian Disease Mapping

Andrew B. Lawson is a professor of biostatistics and eminent scholar in the Division of Biostatistics and Epidemiology in the College of Medicine at the Medical University of South Carolina. He is an ASA fellow and an advisor in disease mapping and risk assessment for the World Health Organization. Dr. Lawson has published over 100 journal papers and eight books and is the founding editor of Spatial and Spatio-temporal Epidemiology. He received a PhD in spatial statistics from the University of St. Andrews. His research interests include the analysis of clustered disease maps, spatial and spatio-temporal disease surveillance, nutritional measurement error, and Bayesian latent variable and SEM modeling.

BACKGROUND
Introduction
Datasets





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
Perfect Sampling
Posterior and Likelihood Approximations
Exercises





Residuals and Goodness-of-Fit
Model GOF Measures
General Residuals
Bayesian Residuals
Predictive Residuals and the Bootstrap
Interpretation of Residuals in a Bayesian Setting
Pseudo Bayes Factors and Marginal Predictive Likelihood
Other Diagnostics
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





Regression and Ecological Analysis
Basic Regression Modeling
Missing Data
Non-Linear Predictors
Confounding and Multi-Colinearity
Geographically Dependent Regression
Variable Selection
Ecological Analysis: The General Case of Regression
Biases and Misclassification Error





Putative Hazard Modeling
Case Event Data
Aggregated Count Data
Spatiotemporal Effects





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





Disease Map Surveillance
Surveillance Concepts
Temporal Surveillance
Spatial and Spatiotemporal Surveillance





Appendices