Fast detection of arbitrarily shaped disease clusters.

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dc.contributor.authorAssunção, Renato Martins
dc.contributor.authorCosta, Marcelo Azevedo
dc.contributor.authorTavares, Andréa Iabrudi
dc.contributor.authorFerreira, Sabino José
dc.date.accessioned2012-11-12T22:19:49Z
dc.date.available2012-11-12T22:19:49Z
dc.date.issued2006
dc.description.abstractDisease cluster detection and evaluation have commonly used spatial statistics methods that scan the map with a fixed circular window to locate candidate clusters. Recently, there has been interest in searching for clusters with arbitrary shape. The circular scan test retains high power of detecting a cluster, but does not necessarily identify the exact regions contained in a non-circular cluster particularly well. We propose, implement and evaluate a new procedure that is fast and produces clusters estimates of arbitrary shape in a rich class of possible cluster candidates. We showed that our methods contain the so-called upper level set method as a particular case. We present a power study of our method and, among other results, the main conclusion is that the likelihood-based arbitrarily shaped scan method is not appropriate to _nd a cluster estimate. When the parameter space includes the set of all possible spatial clusters in a map, a large and discrete parameter space, maximum likely cluster estimates tend to overestimate the true cluster by a large extent. This calls for a new approach different from the maximum likelihood method for this important public health problem.pt_BR
dc.identifier.citationASSUNÇÃO, R. M. et al. Fast detection of arbitrarily shaped disease clusters. Statistics in Medicine, v. 25, n. 1, p. 723-742, 2006. Disponível em: <http://onlinelibrary.wiley.com/doi/10.1002/sim.2411/pdf>. Acesso em: 12 nov. 2012pt_BR
dc.identifier.issn10970258
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/1766
dc.language.isoen_USpt_BR
dc.subjectDisease clusterspt_BR
dc.subjectScan statisticspt_BR
dc.subjectSpatial clusterpt_BR
dc.subjectSpatial statisticspt_BR
dc.titleFast detection of arbitrarily shaped disease clusters.pt_BR
dc.typeArtigo publicado em periodicopt_BR
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