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Performance map of a cluster detection test using extended power.

Guttmann A, Ouchchane L, Li X, Perthus I, Gaudart J, Demongeot J, Boire JY - Int J Health Geogr (2013)

Bottom Line: 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.The performance map we propose enables epidemiologists to assess cluster detection tests across an entire study region.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics, Medical Informatics and Communication Technologies, Clermont University Hospital, Clermont-Ferrand F-63000, France. aline.guttmann@udamail.fr.

ABSTRACT

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 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.

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Extended power curves for two simulated clusters. Line 03160: cluster centered on the SU with zip code 03160 (northwest Auvergne); line 63112: cluster centered on the SU with zip code 63112 (central Auvergne). Both clusters were simulated with a relative risk of 6 and a baseline incidence of birth defects set to 2.26%.
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Figure 5: Extended power curves for two simulated clusters. Line 03160: cluster centered on the SU with zip code 03160 (northwest Auvergne); line 63112: cluster centered on the SU with zip code 63112 (central Auvergne). Both clusters were simulated with a relative risk of 6 and a baseline incidence of birth defects set to 2.26%.

Mentions: Some summary statistics of the AUCEP distributions are displayed in Table 1. Figure 5 shows two different extended power curves (and thus two different CDT behaviors) that have nearly equal AUCEP. One of these clusters was centered on SU “03160”, the other on SU “63112”.


Performance map of a cluster detection test using extended power.

Guttmann A, Ouchchane L, Li X, Perthus I, Gaudart J, Demongeot J, Boire JY - Int J Health Geogr (2013)

Extended power curves for two simulated clusters. Line 03160: cluster centered on the SU with zip code 03160 (northwest Auvergne); line 63112: cluster centered on the SU with zip code 63112 (central Auvergne). Both clusters were simulated with a relative risk of 6 and a baseline incidence of birth defects set to 2.26%.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4016504&req=5

Figure 5: Extended power curves for two simulated clusters. Line 03160: cluster centered on the SU with zip code 03160 (northwest Auvergne); line 63112: cluster centered on the SU with zip code 63112 (central Auvergne). Both clusters were simulated with a relative risk of 6 and a baseline incidence of birth defects set to 2.26%.
Mentions: Some summary statistics of the AUCEP distributions are displayed in Table 1. Figure 5 shows two different extended power curves (and thus two different CDT behaviors) that have nearly equal AUCEP. One of these clusters was centered on SU “03160”, the other on SU “63112”.

Bottom Line: 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.The performance map we propose enables epidemiologists to assess cluster detection tests across an entire study region.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biostatistics, Medical Informatics and Communication Technologies, Clermont University Hospital, Clermont-Ferrand F-63000, France. aline.guttmann@udamail.fr.

ABSTRACT

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 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.

Show MeSH
Related in: MedlinePlus