Cluster detection is an important public health endeavor, and in this article, 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, which include 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 that are either “null” or “non-null,” the latter corresponding to clusters (excess risk) or anticlusters (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 State.
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From the aMathematics Department, Middlebury College, Middlebury, VT; and bDepartments of Statistics and Biostatistics, University of Washington, Seattle, WA.
Submitted 22 August 2014; accepted 21 January 2015.
The authors were supported by Grant R01 CA095994 from the National Institutes of Health.
The authors report no conflicts of interest.
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Correspondence: Albert Y. Kim, Mathematics Department, Middlebury College, 14 Old Chapel Rd, Middlebury, VT 05753. E-mail: email@example.com.