Department of Population Health Sciences Seminar
Model-Based Detection of Spatial Clustering of Disease
Ronald Gangnon, PhD, Departments of Biostatistics and
Medical Informatics, University of Wisconsin-Madison
Monday, February 9, 2004, 12-1 p.m.
132 WARF Building, 610 Walnut St.
We consider a partition model for estimation of regional disease rates and for detection of spatial clusters. Formal inference regarding the number of partitions (or clusters) can be obtained using a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. As an alternative, we consider models with a fixed, but overly large, number of partitions. We explore the ability of these models to provide informal inferences about the number and locations of clusters using localized Bayes factors. We illustrate these two approaches using data on leukemia incidence in upstate New York and data on breast cancer incidence in Wisconsin.
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