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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21812

Title: Different methods for handling incomplete longitudinal binary outcome due to missing at random dropout
Authors: Satty, A.
Mwambi, H.
Molenberghs, Geert
Issue Date: 2015
Citation: STATISTICAL METHODOLOGY, 24, p. 12-27
Abstract: This paper compares the performance of weighted generalized estimating equations (WGEEs), multiple imputation based on generalized estimating equations (Ml-GEES) and generalized linear mixed models (GLMMs) for analyzing incomplete longitudinal binary data when the underlying study is subject to dropout. The paper aims to explore the performance of the above methods in terms of handling dropouts that are missing at random (MAR). The methods are compared on simulated data. The longitudinal binary data are generated from a logistic regression model, under different sample sizes. The incomplete data are created for three different dropout rates. The methods are evaluated in terms of bias, precision and mean square error in case where data are subject to MAR dropout. In conclusion, across the simulations performed, the MI-GEE method performed better in both small and large sample sizes. Evidently, this should not be seen as formal and definitive proof, but adds to the body of knowledge about the methods' relative performance. In addition, the methods are compared using data from a randomized clinical trial. (C) 2014 Elsevier B.V. All rights reserved.
Notes: [Satty, A.; Mwambi, H.] Univ KwaZulu Natal, Sch Math Stat & Comp Sci, Private Bag X01, ZA-3209 Pietermaritzburg, South Africa. [Molenberghs, G.] Hasselt Univ, I BioStat, B-3500 Hasselt, Belgium. [Molenberghs, G.] Univ Leuven, KU Leuven, B-3000 Leuven, Belgium.
URI: http://hdl.handle.net/1942/21812
DOI: 10.1016/j.stamet.2014.10.002
ISI #: 000370216600002
ISSN: 1572-3127
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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