Detecting disease clusters: the importance of statistical power.
A variety of methods and models have been proposed for the statistical analysis of disease excesses, yet rarely are these methods compared with respect to their ability to detect possible clusters. Evaluation of statistical power is one approach for comparing different methods. In this paper, the authors study the probability that a test will reject the null hypothesis, given that the null hypothesis is indeed false. They present a discussion of some considerations involved in power studies of cluster methods and review two methods for detecting space-time clusters of disease, one based on cell occupancy models and the other based on interevent distance comparisons. The authors compare these approaches with respect to: 1) the sensitivity to detect disease excesses (false negatives); 2) the likelihood of detecting clusters that do not exist (false positives); and 3) the structure of a cluster in a given investigation (the alternative hypothesis). The methods chosen, which are two of the most commonly used, are specific to different hypotheses. They both show low power for the small number of cases which are typical of citizen reports to health departments.
Department of Environmental and Community Medicine, Robert Wood Johnson Medical School, Piscataway, NJ 08854.
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Wartenberg
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Greenberg
D002985:Clinical Protocols; D016000:Cluster Analysis; D003198:Computer Simulation; D004781:Environmental Exposure; D004812:Epidemiologic Methods; D005069:Evaluation Studies as Topic; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis; D012307:Risk Factors; D012680:Sensitivity and Specificity