Cluster detection using Bayes factors from overparameterized cluster models

Abstract

In this paper, we consider the use of a partition model to estimate regionaldisease rates and to detect spatial clusters. Formal inference regarding the number ofpartitions (or clusters) can be obtained with a reversible jump Markov chain MonteCarlo algorithm. As an alternative, we consider the ability of models with a fixed,but overly large, number of partitions to estimate regional disease rates and to pro-vide informal inferences about the number and locations of clusters using local Bayesfactors. We illustrate and compare these two approaches using data on leukemiaincidence in upstate New York and data on breast cancer incidence in Wisconsin..