title: Bayesian detection and modeling of spatial disease clustering.
abstract: Many 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 specific 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 estimates for disease rates and allow for greater flexibility in both the type of clusters and the number of clusters that may be considered. We illustrate the proposed procedure through simulation studies and an analysis of the well-known New York leukemia data.
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison 53706, USA. ronald@biostat.wisc.edu
authors.forename: R E
authors.initials: RE
authors.lastname: Gangnon
authors.forename: M K
authors.initials: MK
authors.lastname: Clayton
mesh_terms: D001499:Bayes Theorem; D016000:Cluster Analysis; D004812:Epidemiologic Methods; D006801:Humans; D007938:Leukemia / Q000453:epidemiology; D015233:Models, Statistical; D009518:New York / Q000453:epidemiology; D015203:Reproducibility of Results; D018401:Sample Size