Detecting and Modeling Spatial Disease Clustering: A Bayesian Approach

Current statistical methods for disease clustering studies are based on a hypothesis testing paradigm. These methods typically do not produce useful estimates of disease rates or cluster risks. In this paper, we develop a Bayesian procedure for drawing inferences about speciic models for spatial clustering. The proposed methodology incorporates ideas from image analysis , from Bayesian model averaging and from model selection. With our approach, we obtain reasonable estimates for disease rates and allow for much greater exibility in both the type of clusters and the number of clusters that may be considered. We establish the asymptotic consistency of the resulting estimates and illustrate their behavior with several simulations.