Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/65600
Citations
Scopus Web of Science® Altmetric
?
?
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYelland, L.en
dc.contributor.authorSalter, A.en
dc.contributor.authorRyan, P.en
dc.contributor.authorMakrides, M.en
dc.date.issued2011en
dc.identifier.citationPaediatric and Perinatal Epidemiology, 2011; 25(3 Sp Iss):283-297en
dc.identifier.issn0269-5022en
dc.identifier.issn1365-3016en
dc.identifier.urihttp://hdl.handle.net/2440/65600-
dc.description.abstractRandomised trials involving infants from both single and multiple births present unique statistical challenges. A range of methods have been used to analyse such data, including standard methods which treat all infants as independent, and more complex methods which account for the dependence between outcomes of infants from the same pregnancy. Conflicting recommendations have been made regarding if and when this dependence, or clustering, should be taken into account in the analysis. We studied the performance of ordinary logistic regression, which ignores the clustering, compared with logistic generalised estimating equations (GEEs) and mixed effects models (MEMs), which account for the clustering, using real and simulated datasets. Ordinary logistic regression produced appropriate type I error and coverage rates, provided the dependence between outcomes of infants from the same pregnancy was small and the multiple birth rate was low, but performed poorly otherwise. The type I error rate increased and the coverage rate decreased as either the strength of the dependence or the multiple birth rate increased. In contrast, logistic GEEs maintained appropriate type I error and coverage rates across a wide range of settings. The performance of logistic MEMs varied depending on the setting and the estimation procedure used but was often similar to or better than ordinary logistic regression. We recommend using a method which takes the clustering into account when analysing datasets including infants from multiple births.en
dc.description.statementofresponsibilityLisa N. Yelland, Amy B. Salter, Philip Ryan and Maria Makridesen
dc.language.isoenen
dc.publisherBlackwell Publishing Ltden
dc.rights© 2011 Blackwell Publishing Ltd.en
dc.subjectmultiple births; statistical methodology; logistic regression; mixed effects models; generalised estimating equations.en
dc.titleAnalysis of binary outcomes from randomised trials including multiple births: when should clustering be taken into account?en
dc.typeJournal articleen
dc.identifier.rmid0020105653en
dc.identifier.doi10.1111/j.1365-3016.2011.01196.xen
dc.identifier.pubid31090-
pubs.library.collectionPaediatrics publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidYelland, L. [0000-0003-3803-8728]en
dc.identifier.orcidMakrides, M. [0000-0003-3832-541X]en
Appears in Collections:Paediatrics publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.