Document Type
Article
Publication Date
5-2016
Publication Title
Epidemiology
Abstract
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.
Volume
27
Issue
3
First Page
347
Last Page
355
DOI
doi:10.1097/EDE.0000000000000450
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Rights
© the authors
Recommended Citation
Kim, Albert Y. and Wakefield, Jon, "A Bayesian Method for Cluster Detection with Application to Five Cancer Sites in Puget Sound" (2016). Mathematics Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/mth_facpubs/46
Comments
Peer reviewed accepted manuscript.