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dc.contributor.authorSeaman, Shaunen
dc.contributor.authorHughes, Rachael Aen
dc.date.accessioned2018-06-14T14:42:45Z
dc.date.available2018-06-14T14:42:45Z
dc.date.issued2018-06-01en
dc.identifier.issn0962-2802
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/277051
dc.description.abstractEstimating the parameters of a regression model of interest is complicated by missing data on the variables in that model. Multiple imputation is commonly used to handle these missing data. Joint model multiple imputation and full-conditional specification multiple imputation are known to yield imputed data with the same asymptotic distribution when the conditional models of full-conditional specification are compatible with that joint model. We show that this asymptotic equivalence of imputation distributions does not imply that joint model multiple imputation and full-conditional specification multiple imputation will also yield asymptotically equally efficient inference about the parameters of the model of interest, nor that they will be equally robust to misspecification of the joint model. When the conditional models used by full-conditional specification multiple imputation are linear, logistic and multinomial regressions, these are compatible with a restricted general location joint model. We show that multiple imputation using the restricted general location joint model can be substantially more asymptotically efficient than full-conditional specification multiple imputation, but this typically requires very strong associations between variables. When associations are weaker, the efficiency gain is small. Moreover, full-conditional specification multiple imputation is shown to be potentially much more robust than joint model multiple imputation using the restricted general location model to mispecification of that model when there is substantial missingness in the outcome variable.
dc.languageengen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCompatibilityen
dc.subjectGibbs sampleren
dc.subjectchained equationsen
dc.subjectcongenialityen
dc.subjectinformative marginsen
dc.subjectlinear discriminant analysisen
dc.subjectlog linear modelen
dc.subjectmissing dataen
dc.subjectAlgorithmsen
dc.subjectBiasen
dc.subjectBiomedical Researchen
dc.subjectData Interpretation, Statisticalen
dc.subjectModels, Statisticalen
dc.subjectRandomized Controlled Trials as Topicen
dc.subjectRegression Analysisen
dc.titleRelative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: The general location model.en
dc.typeArticle
prism.endingPage1614
prism.issueIdentifier6en
prism.publicationDate2018en
prism.publicationNameStat Methods Med Resen
prism.startingPage1603
prism.volume27en
dc.identifier.doi10.17863/CAM.24351
dcterms.dateAccepted2016-08-02en
rioxxterms.versionofrecord10.1177/0962280216665872en
rioxxterms.versionVoR*
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2018-06-01en
dc.contributor.orcidSeaman, Shaun [0000-0003-3726-5937]
dc.identifier.eissn1477-0334
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idMRC (unknown)
cam.issuedOnline2016-09-05en


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International