TY - JOUR

T1 - Detecting disease clusters

T2 - The importance of statistical power

AU - Wartenberg, D.

AU - Greenberg, M.

PY - 1990

Y1 - 1990

N2 - 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 eview 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.

AB - 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 eview 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.

KW - space-time clustering

KW - statistics

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U2 - 10.1093/oxfordjournals.aje.a115778

DO - 10.1093/oxfordjournals.aje.a115778

M3 - Article

C2 - 2192551

AN - SCOPUS:0025363345

VL - 132

SP - S156-S166

JO - American Journal of Epidemiology

JF - American Journal of Epidemiology

SN - 0002-9262

IS - 1 SUPPL.

ER -