title: Performance map of a cluster detection test using extended power.
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 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.
authors.affiliation: Department of Biostatistics, Medical Informatics and Communication Technologies, Clermont University Hospital, Clermont-Ferrand F-63000, France. aline.guttmann@udamail.fr.
authors.forename: Aline
authors.initials: A
authors.lastname: Guttmann
authors.forename: Lemlih
authors.initials: L
authors.lastname: Ouchchane
authors.forename: Xinran
authors.initials: X
authors.lastname: Li
authors.forename: Isabelle
authors.initials: I
authors.lastname: Perthus
authors.forename: Jean
authors.initials: J
authors.lastname: Gaudart
authors.forename: Jacques
authors.initials: J
authors.lastname: Demongeot
authors.forename: Jean-Yves
authors.initials: JY
authors.lastname: Boire
mesh_terms: D016000:Cluster Analysis; D000013:Congenital Abnormalities / Q000175:diagnosis / Q000453:epidemiology*; D016208:Databases, Factual; D005602:France / Q000453:epidemiology; D062305:Geographic Mapping; D006801:Humans; D011159:Population Surveillance / Q000379:methods*; D012042:Registries