Abstract
BackgroundConventional power studies possess limited ability to assess the performance of cluster detection tests. In particular, they cannot evaluate the accuracy of the cluster location, which is essential in such assessments. Furthermore, they usually estimate power for one or a few particular alternative hypotheses and thus cannot assess performance over an entire region. Takahashi and Tango developed the concept of extended power that indicates both the rate of null hypothesis rejection and the accuracy of the cluster location. We propose a systematic assessment method, using here extended power, to produce a map showing the performance of cluster detection tests over an entire region.MethodsTo explore the behavior of a cluster detection test on identical cluster types at any possible location, we successively applied four different spatial and epidemiological parameters. These parameters determined four cluster collections, each covering the entire study region. We simulated 1,000 datasets for each cluster and analyzed them with Kulldorff’s spatial scan statistic. From the area under the extended power curve, we constructed a map for each parameter set showing the performance of the test across the entire region.ResultsConsistent with previous studies, the performance of the spatial scan statistic increased with the baseline incidence of disease, the size of the at-risk population and the strength of the cluster (i.e., the relative risk). Performance was heterogeneous, however, even for very similar clusters (i.e., similar with respect to the aforementioned factors), suggesting the influence of other factors.ConclusionsThe area under the extended power curve is a single measure of performance and, although needing further exploration, it is suitable to conduct a systematic spatial evaluation of performance. The performance map we propose enables epidemiologists to assess cluster detection tests across an entire study region.
Highlights
Conventional power studies possess limited ability to assess the performance of cluster detection tests
Epidemiologists use local methods to detect clusters without a priori knowledge of their location, and to determine their significance. Because these cluster detection tests (CDTs) must reveal both the presence and location of clusters, performance studies have been constrained by the limitations of conventional estimation techniques
When evaluating the behavior of these CDTs in a particular region, limited knowledge can be gleaned by simulating one or a few clusters in that region, and even less knowledge can be accrued from studies on other region
Summary
Conventional power studies possess limited ability to assess the performance of cluster detection tests. They cannot evaluate the accuracy of the cluster location, which is essential in such assessments. They usually estimate power for one or a few particular alternative hypotheses and cannot assess performance over an entire region. Epidemiologists use local methods to detect clusters without a priori knowledge of their location, and to determine their significance. Because these cluster detection tests (CDTs) must reveal both the presence and location of clusters, performance studies have been constrained by the limitations of conventional estimation techniques. When evaluating the behavior of these CDTs in a particular region, limited knowledge can be gleaned by simulating one or a few clusters in that region, and even less knowledge can be accrued from studies on other region
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