title: A Bayesian Method for Cluster Detection with Application to Brain and Breast Cancer in Puget Sound.
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.
authors: From the aMathematics Department, Middlebury College, Middlebury, VT; and bDepartments of Statistics and Biostatistics, University of Washington, Seattle, WA.
authors: Albert Y
authors: AY
authors: Kim
authors: Jon
authors: J
authors: Wakefield
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D001932:Brain Neoplasms / Q000453:epidemiology*; D001943:Breast Neoplasms / Q000453:epidemiology*; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D015179:Colorectal Neoplasms / Q000453:epidemiology; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008175:Lung Neoplasms / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D011471:Prostatic Neoplasms / Q000453:epidemiology; D014861:Washington / Q000453:epidemiology; D055815:Young Adult