A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data
| Autor: | Geert Molenberghs, Caroline Beunckens, Cristina Sotto |
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| Jazyk: | angličtina |
| Rok vydání: | 2008 |
| Předmět: |
Statistics and Probability
Mixed model Applied Mathematics Linear model Estimating equations Missing data Computational Mathematics Computational Theory and Mathematics Sample size determination Statistics Binary data Likelihood function Generalized estimating equation missing at random weighted GEE multiple imputation GEE asymptotic bias Mathematics |
| Popis: | 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. The authors gratefully acknowledge support from Fonds Wetenschappelijk Onderzoek-Vlaanderen Research Project G.0002.98 “Sensitivity Analysis for Incomplete and Coarse Data” and from Belgian IUAP/PAI network “Statistical Techniques and Modeling for Complex Substantive Questions with Complex Data”. |
| Databáze: | OpenAIRE |
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