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dc.contributor.authorTESHOME AYELE, Birhanu-
dc.contributor.authorMOLENBERGHS, Geert-
dc.contributor.authorSOTTO, Cristina-
dc.contributor.authorKenward, Michael G.-
dc.identifier.citationJOURNAL OF BIOPHARMACEUTICAL STATISTICS, 21(2). p. 202-225-
dc.description.abstractGeneralized estimating equations (GEE), proposed by Liang and Zeger (1986), provide a popular method to analyze correlated non-Gaussian data. When data are incomplete, the GEE method suffers from its frequentist nature and inferences under this method are valid only under the strong assumption that the missing data are missing completely at random. When response data are missing at random, two modifications of GEE can be considered, based on inverse-probability weighting or on multiple imputation. The weighted GEE (WGEE) method involves weighting observations by the inverse of their probability of being observed. Imputation methods involve filling in missing observations with values predicted by an assumed imputation model, multiple times. The so-called doubly robust (DR) methods involve both a model for the weights and a predictive model for the missing observations given the observed ones. To yield consistent estimates, WGEE needs correct specification of the dropout model while imputation-based methodology needs a correctly specified imputation model. DR methods need correct specification of either the weight or the predictive model, but not necessarily both. Focusing on incomplete binary repeated measures, we study the relative performance of the singly robust and doubly robust versions of GEE in a variety of correctly and incorrectly specified models using simulation studies. Data from a clinical trial in onychomycosis further illustrate the method.-
dc.publisherTAYLOR & FRANCIS INC-
dc.rightsCopyright of Journal of Biopharmaceutical Statistics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.-
dc.subject.otherData augmentation; Doubly robust GEE; Missing at random; Multiple imputation; Weighted GEE-
dc.subject.otherdata augmentation; doubly robust GEE; missing at random; multiple imputation; weighted GEE-
dc.titleDoubly Robust and Multiple-Imputation-Based Generalized Estimating Equations-
dc.typeJournal Contribution-
dc.description.notesMolenberghs, G (reprint author),[Birhanu, Teshome; Molenberghs, Geert; Sotto, Cristina] Hasselt Univ, I BioStat, B-3590 Diepenbeek, Belgium. [Molenberghs, Geert] Katholieke Univ Leuven, I BioStat, Louvain, Belgium. [Sotto, Cristina] Univ Philippines, Sch Stat, Quezon City 1101, Philippines. [Kenward, Michael G.] London Sch Hyg & Trop Med, Dept Med Stat, London WC1, England.
item.validationecoom 2012-
item.fulltextWith Fulltext-
item.contributorMOLENBERGHS, Geert-
item.contributorTESHOME AYELE, Birhanu-
item.contributorKenward, Michael G.-
item.contributorSOTTO, Cristina-
item.fullcitationTESHOME AYELE, Birhanu; MOLENBERGHS, Geert; SOTTO, Cristina & Kenward, Michael G. (2011) Doubly Robust and Multiple-Imputation-Based Generalized Estimating Equations. In: JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 21(2). p. 202-225.-
item.accessRightsRestricted Access-
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