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

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  • Andrew Copas
  • Shaun Seaman

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.

Suggested Citation

  • Andrew Copas & Shaun Seaman, 2010. "Bias from the use of generalized estimating equations to analyze incomplete longitudinal binary data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 911-922.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:911-922
    DOI: 10.1080/02664760902939604
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    References listed on IDEAS

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    1. Stijn Vansteelandt & Andrea Rotnitzky & James Robins, 2007. "Estimation of Regression Models for the Mean of Repeated Outcomes Under Nonignorable Nonmonotone Nonresponse," Biometrika, Biometrika Trust, vol. 94(4), pages 841-860.
    2. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    3. Garrett M. Fitzmaurice & Stuart R. Lipsitz & Geert Molenberghs & Joseph G. Ibrahim, 2001. "Bias in Estimating Association Parameters for Longitudinal Binary Responses with Drop‐Outs," Biometrics, The International Biometric Society, vol. 57(1), pages 15-21, March.
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    Cited by:

    1. Jiachen Cai & Ning Zhang & Xin Zhou & Donna Spiegelman & Molin Wang, 2023. "Correcting for bias due to mismeasured exposure history in longitudinal studies with continuous outcomes," Biometrics, The International Biometric Society, vol. 79(4), pages 3739-3751, December.

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