Research
Scale and shape issues in focused cluster power for count data
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* Corresponding author: Robin C Puett rpuett@hsph.harvard.edu
1 Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
2 Department of Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
3 School of Medicine, Health Policy and Practice, University of East Anglia, UK
4 South Carolina Statewide Cancer Prevention & Control Program, Hollings Cancer Center, Medical University of South Carolina, Charleston, SC, USA
International Journal of Health Geographics 2005, 4:8 doi:10.1186/1476-072X-4-8
Published: 31 March 2005Abstract
Background
Interest in the development of statistical methods for disease cluster detection has experienced rapid growth in recent years. Evaluations of statistical power provide important information for the selection of an appropriate statistical method in environmentally-related disease cluster investigations. Published power evaluations have not yet addressed the use of models for focused cluster detection and have not fully investigated the issues of disease cluster scale and shape. As meteorological and other factors can impact the dispersion of environmental toxicants, it follows that environmental exposures and associated diseases can be dispersed in a variety of spatial patterns. This study simulates disease clusters in a variety of shapes and scales around a centrally located single pollution source. We evaluate the power of a range of focused cluster tests and generalized linear models to detect these various cluster shapes and scales for count data.
Results
In general, the power of hypothesis tests and models to detect focused clusters improved when the test or model included parameters specific to the shape of cluster being examined (i.e. inclusion of a function for direction improved power of models to detect clustering with an angular effect). However, power to detect clusters where the risk peaked and then declined was limited.
Conclusion
Findings from this investigation show sizeable changes in power according to the scale and shape of the cluster and the test or model applied. These findings demonstrate the importance of selecting a test or model with functions appropriate to detect the spatial pattern of the disease cluster.