Empirical bayes versus fully bayesian analysis of geographical variation in disease risk

Two approaches to relative risk estimation are described and compared, including an empirical Bayes approach that uses a technique of penalized log-likelihood maximization and an innovative stochastic simulation technique called the Gibbs sampler.

Abstract

This paper reviews methods for mapping geographical variation in disease incidence and mortality. Recent results in Bayesian hierarchical modelling of relative risk are discussed. Two approaches to relative risk estimation, along with the related computational procedures, are described and compared. The first is an empirical Bayes approach that uses a technique of penalized log-likelihood maximization; the second approach is fully Bayesian, and uses an innovative stochastic simulation technique called the Gibbs sampler. We chose to map geographical variation in breast cancer and Hodgkin's disease mortality as observed in all the health care districts of Sardinia, to illustrate relevant problems, methods and techniques.