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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/11846

Title: Doubly Robust and Multiple-Imputation-Based Generalized Estimating Equations
Authors: Teshome Ayele, Birhanu
Molenberghs, Geert
Sotto, Cristina
Kenward, Michael G.
Issue Date: 2011
Abstract: Generalized 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.
Notes: Molenberghs, 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. geert.molenbergs@uhasselt.be
URI: http://hdl.handle.net/1942/11846
DOI: 10.1080/10543406.2011.550096
ISI #: 000288281800004
ISSN: 1054-3406
Category: A1
Type: Journal Contribution
Validation: ecoom, 2012
Appears in Collections: Research publications

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