Performance map of a cluster detection test using extended power

International Journal of Health Geographics

Jean-Yves Boire³  Jacques Demongeot⁴  Jean Gaudart²  Isabelle Perthus¹  Xinran Li³  Lemlih Ouchchane³  Aline Guttmann³ 

[1] PEPRADE, EA 4681, Clermont-Ferrand F-63000, France;Biostatistics Unit, Assistance Publique Hôpitaux de Marseille, Marseille F-13005, France;ISIT, UMR CNRS UDA 6284, Auvergne University, Clermont-Ferrand F-63001, France;AGIM, FRE CNRS 3405, J. Fourier University, La Tronche University School of Medicine, Grenoble F-38700, France

关键词: Simulation study;    Extended power;    Performance map;    Cluster detection test;   

Others  :  809957
DOI  :  10.1186/1476-072X-12-47

 received in 2013-07-31, accepted in 2013-10-15,  发布年份 2013

【 摘 要 】

Background

Conventional 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.

Methods

To 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.

Results

Consistent 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.

Conclusions

The 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.

【 授权许可】

   
2013 Guttmann et al.; licensee BioMed Central Ltd.

【 图 表 】

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