title: Power Evaluation of Focused Cluster Tests.
abstract: Many statistical tests have been developed to assess the significance of clusters of disease located around known sources of environmental contaminants, also known as focused disease clusters. The majority of focused-cluster tests were designed to detect a particular spatial pattern of clustering, one in which the disease cluster centers around the pollution source and declines in a radial fashion with distance. However, other spatial patterns of environmentally related disease clusters are likely given that the spatial dispersion patterns of environmental contaminants, and thus human exposure, depend on a number of factors (i.e., meteorology and topography). For this study, data were simulated with five different spatial patterns of disease clusters, reflecting potential pollutant dispersion scenarios: 1) a radial effect decreasing with increasing distance, 2) a radial effect with a defined peak and decreasing with distance, 3) a simple angular effect, 4) an angular effect decreasing with increasing distance and 5) an angular effect with a defined peak and decreasing with distance. The power to detect each type of spatially distributed disease cluster was evaluated using Stone's Maximum Likelihood Ratio Test, Tango's Focused Test, Bithell's Linear Risk Score Test, and variations of the Lawson-Waller Score Test. Study findings underscore the importance of considering environmental contaminant dispersion patterns, particularly directional effects, with respect to focused-cluster test selection in cluster investigations. The effect of extra variation in risk also is considered, although its effect is not substantial in terms of the power of tests.
authors.affiliation: South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina, USA ; Departments of Epidemiology and Biostatistics and Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, USA.
authors.forename: Rc
authors.initials: R
authors.lastname: Puett
authors.affiliation: Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, USA.
authors.forename: Ab
authors.initials: A
authors.lastname: Lawson
authors.affiliation: School of Medicine, Health Policy and Practice, University of East Anglia, United Kingdom.
authors.forename: Ab
authors.initials: A
authors.lastname: Clark
authors.affiliation: South Carolina Statewide Cancer Prevention and Control Program, University of South Carolina, USA.
authors.forename: Jr
authors.initials: J
authors.lastname: Hebert
authors.affiliation: Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, USA.
authors.forename: M
authors.initials: M
authors.lastname: Kulldorff
keywords: clusters; power; small area analysis; spatial statistics