Bayesian detection and modeling of spatial disease clustering

Many current statistical methods for disease clustering studies are based o n a hypothesis testing paradigm. These methods typically do not produce use ful estimates of disease rates or cluster risks. In this paper, we develop a Bayesian procedure for drawing inferences about specific models for spati al clustering. The proposed methodology incorporates ideas from image analy sis, from Bayesian model averaging, and from model selection. With our appr oach, we obtain estimates for disease rates and allow for greater flexibili ty in both the type of clusters and the number of clusters that may be cons idered. We illustrate the proposed procedure through simulation studies and an analysis of the well-known New York leukemia data.