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Bias from the use of generalized estimating equations to analyze incomplete longitudinal binary data

Copas, AJ; Seaman, SR; (2010) Bias from the use of generalized estimating equations to analyze incomplete longitudinal binary data. J APPL STAT , 37 (6) 911 - 922. 10.1080/02664760902939604.

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Abstract

Patient dropout is a common problem in studies that collect repeated binary measurements. Generalized estimating equations (GEE) are often used to analyze such data. The dropout mechanism may be plausibly missing at random (MAR), i.e. unrelated to future measurements given covariates and past measurements. In this case, various authors have recommended weighted GEE with weights based on an assumed dropout model, or an imputation approach, or a doubly robust approach based on weighting and imputation. These approaches provide asymptotically unbiased inference, provided the dropout or imputation model (as appropriate) is correctly specified. Other authors have suggested that, provided the working correlation structure is correctly specified, GEE using an improved estimator of the correlation parameters ('modified GEE') show minimal bias. These modified GEE have not been thoroughly examined. In this paper, we study the asymptotic bias under MAR dropout of these modified GEE, the standard GEE, and also GEE using the true correlation. We demonstrate that all three methods are biased in general. The modified GEE may be preferred to the standard GEE and are subject to only minimal bias in many MAR scenarios but in others are substantially biased. Hence, we recommend the modified GEE be used with caution.

Type: Article
Title: Bias from the use of generalized estimating equations to analyze incomplete longitudinal binary data
DOI: 10.1080/02664760902939604
Keywords: binary data, generalized estimating equations, missing data, missing at random, repeated measures, MISSING DATA, REPEATED OUTCOMES, DROP-OUTS, SEMIPARAMETRIC REGRESSION, LINEAR-MODELS, ASSOCIATION, NONRESPONSE, RESPONSES, TRIAL, RATES
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Pop Health Sciences > Institute for Global Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Pop Health Sciences > Institute for Global Health > Infection and Population Health
URI: http://discovery.ucl.ac.uk/id/eprint/144979
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