title: A Bayesian model for cluster detection.
abstract: The detection of areas in which the risk of a particular disease is significantly elevated, leading to an excess of cases, is an important enterprise in spatial epidemiology. Various frequentist approaches have been suggested for the detection of "clusters" within a hypothesis testing framework. Unfortunately, these suffer from a number of drawbacks including the difficulty in specifying a p-value threshold at which to call significance, the inherent multiplicity problem, and the possibility of multiple clusters. In this paper, we suggest a Bayesian approach to detecting "areas of clustering" in which the study region is partitioned into, possibly multiple, "zones" within which the risk is either at a null, or non-null, level. Computation is carried out using Markov chain Monte Carlo, tuned to the model that we develop. The method is applied to leukemia data in upstate New York.
authors.affiliation: Departments of Statistics and Biostatistics, University of Washington, Seattle, WA 98195, USA.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Wakefield
authors.forename: Albert
authors.initials: A
authors.lastname: Kim
mesh_terms: D001499:Bayes Theorem; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D007938:Leukemia / Q000453:epidemiology; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D009518:New York / Q000453:epidemiology; D012306:Risk
keywords: Bayes factors; Markov chain Monte Carlo; Scan statistic; Spatial epidemiology