Likelihood‐based tests for localized spatial clustering of disease

Abstract

AbstractNumerous methods have been proposed for detecting spatial clustering of disease. Two methods for likelihood‐based inference using parametric models for clustering are the spatial scan statistic and the weighted average likelihood ratio (WALR) test. The spatial scan statistic provides a measure of evidence for clustering at a specific, data‐identified location; it can be biased towards finding clusters in areas with greater spatial resolution. The WALR test provides a more global assessment of the evidence for clustering and identifies cluster locations in a relatively unbiased fashion using a posterior distribution over potential clusters. We consider two new statistics which attempt to combine the specificity of the scan statistic with the lack of bias of the WALR test: a scan statistic based on a penalized likelihood ratio and a localized version of the WALR test. We evaluate the power of these tests and bias of the associated estimates through simulations and demonstrate their application using the well‐known New York leukemia data. Copyright © 2004 John Wiley & Sons, Ltd.