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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/9691

Title: A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data
Authors: Beunckens, Caroline
Sotto, Cristina
Molenberghs, Geert
Issue Date: 2008
Abstract: Missingness frequently complicates the analysis of longitudinal data. A popular solution for dealing with incomplete longitudinal data is the use of likelihood-based methods, when, for example, linear, generalized linear, or non-linear mixed models are considered, due to their validity under the assumption of missing at random (MAR). Semi-parametric methods such as generalized estimating equations (GEEs) offer another attractive approach but require the assumption of missing completely at random (MCAR). Weighted GEE (WGEE) has been proposed as an elegant way to ensure validity under MAR. Alternatively, multiple imputation (MI) can be used to pre-process incomplete data, after which GEE is applied (MI-GEE). Focusing on incomplete binary repeated measures, both methods are compared using the so-called asymptotic, as well as small-sample, simulations, in a variety of correctly specified as well as incorrectly specified models. In spite of the asymptotic unbiasedness of WGEE, results provide striking evidence that MI-GEE is both less biased and more accurate in the small to moderate sample sizes which typically arise in clinical trials. (c) 2007 Elsevier B.V. All rights reserved.
Notes: [Beunckens, Caroline; Sotto, Cristina; Molenberghs, Geert] Hasselt Univ, Ctr Stat, B-3590 Diepenbeek, Belgium. [Sotto, Cristina] Univ Philippines, Sch Stat, Quezon City 1101, Philippines.
URI: http://hdl.handle.net/1942/9691
DOI: 10.1016/j.csda.2007.04.020
ISI #: 000253669700022
ISSN: 0167-9473
Category: A1
Type: Journal Contribution
Validation: ecoom, 2009
Appears in Collections: Research publications

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