Cluster detection is an important public health endeavor and in this paper we describe and apply a recently developed Bayesian method. Commonly-used approaches are based on so-called scan statistics and suffer from a number of difficulties including how to choose a level of significance and how to deal with the possibility of multiple clusters. The basis of our model is to partition the study region into a set of areas which are either “null” or “non-null”, the latter corresponding to clusters (excess risk) or anti-clusters (reduced risk). We demonstrate the Bayesian method and compare with a popular existing approach, using data on breast, brain, lung, prostate and colorectal cancer, in the Puget Sound region of Washington St ate. We address the important issues of sensitivity to the priors, and the incorporation of covariates. The approach is implemented within the freely-available R package SpatialEpi.
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Kim, Albert Y. and Wakefield, Jon, "A Bayesian Method for Cluster Detection with Application to Five Cancer Sites in Puget Sound" (2016). Mathematics and Statistics: Faculty Publications, Smith College, Northampton, MA.