Linked Life Data

16453376

Source:http://linkedlifedata.com/resource/pubmed/id/16453376

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2006-2-9
pubmed:abstractText
Disease 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 find 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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
0277-6715
pubmed:author
pubmed:copyrightInfo
Copyright 2006 John Wiley & Sons, Ltd
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
25
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
723-42
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Fast detection of arbitrarily shaped disease clusters.
pubmed:affiliation
Departamento de Estatística, Universidade Federal de Minas Gerais, 31270-901, Belo Horizonte, MG Brazil. assuncao@est.ufmg.br
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't