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Article: Analyzing binary outcome data with small clusters: A simulation study

TitleAnalyzing binary outcome data with small clusters: A simulation study
Authors
KeywordsWithin-cluster- resampling method
Binary outcome data
Generalized estimating equation
Random-effects logistic regression
Small clusters
Standard logistic regression
Issue Date2014
Citation
Communications in Statistics: Simulation and Computation, 2014, v. 43, n. 7, p. 1771-1782 How to Cite?
AbstractBinary outcome data with small clusters often arise in medical studies and the size of clusters might be informative of the outcome. The authors conducted a simulation study to examine the performance of a range of statistical methods. The simulation results showed that all methods performed mostly comparable in the estimation of covariate effects. However, the standard logistic regression approach that ignores the clustering encountered an undercoverage problem when the degree of clustering was nontrivial. The performance of random-effects logistic regression approach tended to be affected by low disease prevalence, relatively small cluster size, or informative cluster size. © 2014 Copyright Taylor and Francis Group, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/220886
ISSN
2015 Impact Factor: 0.397
2015 SCImago Journal Rankings: 0.525

 

DC FieldValueLanguage
dc.contributor.authorXu, Ying-
dc.contributor.authorLee, Chun Fan-
dc.contributor.authorCheung, Yin Bun-
dc.date.accessioned2015-10-22T09:04:42Z-
dc.date.available2015-10-22T09:04:42Z-
dc.date.issued2014-
dc.identifier.citationCommunications in Statistics: Simulation and Computation, 2014, v. 43, n. 7, p. 1771-1782-
dc.identifier.issn0361-0918-
dc.identifier.urihttp://hdl.handle.net/10722/220886-
dc.description.abstractBinary outcome data with small clusters often arise in medical studies and the size of clusters might be informative of the outcome. The authors conducted a simulation study to examine the performance of a range of statistical methods. The simulation results showed that all methods performed mostly comparable in the estimation of covariate effects. However, the standard logistic regression approach that ignores the clustering encountered an undercoverage problem when the degree of clustering was nontrivial. The performance of random-effects logistic regression approach tended to be affected by low disease prevalence, relatively small cluster size, or informative cluster size. © 2014 Copyright Taylor and Francis Group, LLC.-
dc.languageeng-
dc.relation.ispartofCommunications in Statistics: Simulation and Computation-
dc.subjectWithin-cluster- resampling method-
dc.subjectBinary outcome data-
dc.subjectGeneralized estimating equation-
dc.subjectRandom-effects logistic regression-
dc.subjectSmall clusters-
dc.subjectStandard logistic regression-
dc.titleAnalyzing binary outcome data with small clusters: A simulation study-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1080/03610918.2012.744044-
dc.identifier.scopuseid_2-s2.0-84893958244-
dc.identifier.volume43-
dc.identifier.issue7-
dc.identifier.spage1771-
dc.identifier.epage1782-
dc.identifier.eissn1532-4141-

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