Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/354
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dc.contributor.authorMICHIELS, Bart-
dc.contributor.authorMOLENBERGHS, Geert-
dc.contributor.authorLipsitz, Stuart R.-
dc.date.accessioned2004-10-25T07:23:18Z-
dc.date.available2004-10-25T07:23:18Z-
dc.date.issued1999-
dc.identifier.citationBiometrics, 55(3). p. 978-983-
dc.identifier.issn0006-341X-
dc.identifier.urihttp://hdl.handle.net/1942/354-
dc.description.abstractMost models for incomplete data are formulated within the selection model framework. This paper studies similarities and differences of modeling incomplete data within both selection and pattern-mixture settings. The focus is on missing at random mechanisms and on categorical data. Point and interval estimation is discussed. A comparison of both approaches is done on side effects in a psychiatric study.-
dc.language.isoen-
dc.publisherINTERNATIONAL BIOMETRIC SOC-
dc.subjectCategorical data-
dc.subjectClustered data-
dc.subjectMissing data-
dc.subjectLongitudinal data-
dc.subject.othercategorical data; maximum likelihood estimation; missing data; multiple imputation; sensitivity analysis-
dc.titleSelection models and pattern-mixture models for incomplete categorical data with covariates-
dc.typeJournal Contribution-
dc.identifier.epage983-
dc.identifier.issue3-
dc.identifier.spage978-
dc.identifier.volume55-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.bibliographicCitation.oldjcatA1-
dc.identifier.doi10.1111/j.0006-341X.1999.00978.x-
dc.identifier.isi000082683000047-
item.fullcitationMICHIELS, Bart; MOLENBERGHS, Geert & Lipsitz, Stuart R. (1999) Selection models and pattern-mixture models for incomplete categorical data with covariates. In: Biometrics, 55(3). p. 978-983.-
item.fulltextWith Fulltext-
item.accessRightsRestricted Access-
item.validationecoom 2000-
item.contributorMICHIELS, Bart-
item.contributorMOLENBERGHS, Geert-
item.contributorLipsitz, Stuart R.-
crisitem.journal.issn0006-341X-
crisitem.journal.eissn1541-0420-
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