Optimizing the Choice of Maximum Spatial Window Size in Spatial Scan Statistic

• Activity Number: 290
• Type: Topic Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Li Zhu*+ and Junhee Han
• Companies: National Cancer Institute and Unversity of Arkansas
• Keywords: scan stistics ; surveillance ; cancer mortality ; log likelihood ratio ; maximum spatial window size

Abstract

Spatial and space-time scan statistics are widely used in disease surveillance to identify geographical areas of elevated disease risk and for the early detection of disease outbreaks. With a scan statistic, a scanning window of variable location and size moves across the map to evaluate thousands of overlapping circles as potential clusters, adjusting for the multiple testing. Almost always, the method will find many very similar overlapping clusters, and it is not useful to report all of them. This paper proposes two optimization criteria by which to select which of the many overlapping clusters to report. The GINI coefficient provides a quick and intuitive way to evaluate the degree of the heterogeneity of the cluster models, which is useful to explain how well the cluster models reveal the underlying true cluster patterns. The Cluster Information Criterion (CLIC) is more systemic taking into account the likelihood, the number of clusters, and the population size. Simulation studies and real cancer mortality data show that the proposed criteria can identify a more refined collection of non-overlapping clusters to report.