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- W2439769715 abstract "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." @default.
- W2439769715 created "2016-06-24" @default.
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- W2439769715 date "2016-05-01" @default.
- W2439769715 modified "2023-09-24" @default.
- W2439769715 title "A Bayesian Method for Cluster Detection with Application to Brain and Breast Cancer in Puget Sound" @default.
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- W2439769715 doi "https://doi.org/10.1097/ede.0000000000000450" @default.
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