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A Bayesian Method for Cluster Detection with Application to Brain and Breast Cancer in Puget Sound

Kim, Albert Y.; Wakefield, Jon

doi: 10.1097/EDE.0000000000000450
Cancer
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SDC

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.

Supplemental Digital Content is available in the text.

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.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).

Correspondence: Albert Y. Kim, Mathematics Department, Middlebury College, 14 Old Chapel Rd, Middlebury, VT 05753. E-mail: aykim@middlebury.edu.

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