Int J Health Geogr - Performance map of a cluster detection test using extended power.

Tópicos

{ spatial(1525) area(1432) region(1030) }
{ detect(2391) sensit(1101) algorithm(908) }
{ assess(1506) score(1403) qualiti(1306) }
{ model(2341) predict(2261) use(1141) }
{ featur(3375) classif(2383) classifi(1994) }
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{ error(1145) method(1030) estim(1020) }
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{ compound(1573) activ(1297) structur(1058) }
{ age(1611) year(1155) adult(843) }
{ health(1844) social(1437) communiti(874) }
{ can(774) often(719) complex(702) }
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{ decis(3086) make(1611) patient(1517) }
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{ method(2212) result(1239) propos(1039) }

Resumo

CKGROUND: 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.

Resumo Limpo

ckground convent power studi possess limit abil assess perform cluster detect test particular evalu accuraci cluster locat essenti assess furthermor usual estim power one particular altern hypothes thus assess perform entir region takahashi tango develop concept extend power indic rate null hypothesi reject accuraci cluster locat propos systemat assess method use extend power produc map show perform cluster detect test entir regionmethod explor behavior cluster detect test ident cluster type possibl locat success appli four differ spatial epidemiolog paramet paramet determin four cluster collect cover entir studi region simul dataset cluster analyz kulldorff spatial scan statist area extend power curv construct map paramet set show perform test across entir regionresult consist previous studi perform spatial scan statist increas baselin incid diseas size atrisk popul strength cluster ie relat risk perform heterogen howev even similar cluster ie similar respect aforement factor suggest influenc factorsconclus area extend power curv singl measur perform although need explor suitabl conduct systemat spatial evalu perform perform map propos enabl epidemiologist assess cluster detect test across entir studi region

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