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dc.contributor.authorSeaman, Shaunen
dc.contributor.authorPavlou, Menelaosen
dc.contributor.authorCopas, Andrew Jen
dc.date.accessioned2018-06-14T10:45:11Z
dc.date.available2018-06-14T10:45:11Z
dc.date.issued2014-06en
dc.identifier.issn0006-341X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/277039
dc.description.abstractClustered data commonly arise in epidemiology. We assume each cluster member has an outcome Y and covariates X. When there are missing data in Y, the distribution of Y given X in all cluster members ("complete clusters") may be different from the distribution just in members with observed Y ("observed clusters"). Often the former is of interest, but when data are missing because in a fundamental sense Y does not exist (e.g., quality of life for a person who has died), the latter may be more meaningful (quality of life conditional on being alive). Weighted and doubly weighted generalized estimating equations and shared random-effects models have been proposed for observed-cluster inference when cluster size is informative, that is, the distribution of Y given X in observed clusters depends on observed cluster size. We show these methods can be seen as actually giving inference for complete clusters and may not also give observed-cluster inference. This is true even if observed clusters are complete in themselves rather than being the observed part of larger complete clusters: here methods may describe imaginary complete clusters rather than the observed clusters. We show under which conditions shared random-effects models proposed for observed-cluster inference do actually describe members with observed Y. A psoriatic arthritis dataset is used to illustrate the danger of misinterpreting estimates from shared random-effects models.
dc.description.sponsorshipSRS is funded by MRC grants U1052 60558 and MC_US_A030_0015, AJC and MP by MRC grant G0600657.
dc.languageengen
dc.publisherWiley
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectBridge distributionen
dc.subjectImmortal cohort inferenceen
dc.subjectInformative missingnessen
dc.subjectMissing not at randomen
dc.subjectMortal cohort inferenceen
dc.subjectSemi-continuous dataen
dc.subjectArthritis, Psoriaticen
dc.subjectBiometryen
dc.subjectCluster Analysisen
dc.subjectEpidemiologic Methodsen
dc.subjectFemaleen
dc.subjectHumansen
dc.subjectMaleen
dc.subjectModels, Statisticalen
dc.titleMethods for observed-cluster inference when cluster size is informative: a review and clarifications.en
dc.typeArticle
prism.endingPage456
prism.issueIdentifier2en
prism.publicationDate2014en
prism.publicationNameBiometricsen
prism.startingPage449
prism.volume70en
dc.identifier.doi10.17863/CAM.24339
dcterms.dateAccepted2014-01-01en
rioxxterms.versionofrecord10.1111/biom.12151en
rioxxterms.versionVoR*
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2014-06en
dc.contributor.orcidSeaman, Shaun [0000-0003-3726-5937]
dc.identifier.eissn1541-0420
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idMRC (unknown)
cam.issuedOnline2014-01-30en
cam.orpheus.successThu Jan 30 12:58:17 GMT 2020 - Embargo updated*
rioxxterms.freetoread.startdate2014-06-30


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