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dc.contributor.authorYelland, Lisa Nen
dc.contributor.authorSullivan, Thomas Ren
dc.contributor.authorPavlou, Menelaosen
dc.contributor.authorSeaman, Shaunen
dc.date.accessioned2019-06-13T09:20:19Z
dc.date.available2019-06-13T09:20:19Z
dc.date.issued2015-10-07en
dc.identifier.issn1365-3016
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/293587
dc.description.abstractBACKGROUND: Informative birth size occurs when the average outcome depends on the number of infants per birth. Although analysis methods have been proposed for handling informative birth size, their performance is not well understood. Our aim was to evaluate the performance of these methods and to provide recommendations for their application in randomised trials including infants from single and multiple births. METHODS: Three generalised estimating equation (GEE) approaches were considered for estimating the effect of treatment on a continuous or binary outcome: cluster weighted GEEs, which produce treatment effects with a mother-level interpretation when birth size is informative; standard GEEs with an independence working correlation structure, which produce treatment effects with an infant-level interpretation when birth size is informative; and standard GEEs with an exchangeable working correlation structure, which do not account for informative birth size. The methods were compared through simulation and analysis of an example dataset. RESULTS: Treatment effect estimates were affected by informative birth size in the simulation study when the effect of treatment in singletons differed from that in multiples (i.e. in the presence of a treatment group by multiple birth interaction). The strength of evidence supporting the effectiveness of treatment varied between methods in the example dataset. CONCLUSIONS: Informative birth size is always a possibility in randomised trials including infants from both single and multiple births, and analysis methods should be pre-specified with this in mind. We recommend estimating treatment effects using standard GEEs with an independence working correlation structure to give an infant-level interpretation.
dc.description.sponsorshipAustralian National Health and Medical Research Council. Grant Number: #ID 1052388 United Kingdom Medical Research Council. Grant Number: ID U1052 60558
dc.languageengen
dc.publisherBlackwell Publishing Inc.
dc.subjectclusteringen
dc.subjectgeneralised estimating equationsen
dc.subjectinformative cluster sizeen
dc.subjectmultiple birthsen
dc.subjectstatistical methodologyen
dc.subjectAdulten
dc.subjectFemaleen
dc.subjectFetal Growth Retardationen
dc.subjectHumansen
dc.subjectInfant, Low Birth Weighten
dc.subjectInfant, Newbornen
dc.subjectInfant, Prematureen
dc.subjectMaleen
dc.subjectPopulation Surveillanceen
dc.subjectPregnancyen
dc.subjectPregnancy, Multipleen
dc.subjectPremature Birthen
dc.subjectRandomized Controlled Trials as Topicen
dc.subjectReference Standardsen
dc.titleAnalysis of Randomised Trials Including Multiple Births When Birth Size Is Informative.en
dc.typeArticle
prism.endingPage575
prism.issueIdentifier6en
prism.publicationDate2015en
prism.publicationNamePaediatric and Perinatal Epidemiologyen
prism.startingPage567
prism.volume29en
dc.identifier.doi10.17863/CAM.24329
rioxxterms.versionofrecord10.1111/ppe.12228en
rioxxterms.versionAM*
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2015-10-07en
dc.contributor.orcidSeaman, Shaun [0000-0003-3726-5937]
dc.identifier.eissn1365-3016
rioxxterms.typeJournal Article/Reviewen
cam.issuedOnline2005-09-01en
dc.identifier.urlhttps://onlinelibrary.wiley.com/doi/full/10.1111/ppe.12228en


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