Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random

Authors

  • John S. Preisser,

    Corresponding author
    1. Department of Biostatistics, CB #7420, School of Public Health, University of North Carolina, Chapel Hill, NC 27599, U.S.A.
    • Department of Biostatistics, CB #7420, School of Public Health, University of North Carolina, Chapel Hill, NC 27599, U.S.A.
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  • Kurt K. Lohman,

    1. Section on Biostatistics, Department of Public Health Sciences, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, U.S.A.
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  • Paul J. Rathouz

    1. Department of Health Studies, University of Chicago, 5841 S. Maryland Ave., MC 2007, Chicago,IL 60637, U.S.A.
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Abstract

The generalized estimating equations (GEE) approach is commonly used to model incomplete longitudinal binary data. When drop-outs are missing at random through dependence on observed responses (MAR), GEE may give biased parameter estimates in the model for the marginal means. A weighted estimating equations approach gives consistent estimation under MAR when the drop-out mechanism is correctly specified. In this approach, observations or person-visits are weighted inversely proportional to their probability of being observed. Using a simulation study, we compare the performance of unweighted and weighted GEE in models for time-specific means of a repeated binary response with MAR drop-outs. Weighted GEE resulted in smaller finite sample bias than GEE. However, when the drop-out model was misspecified, weighted GEE sometimes performed worse than GEE. Weighted GEE with observation-level weights gave more efficient estimates than a weighted GEE procedure with cluster-level weights. Copyright © 2002 John Wiley & Sons, Ltd.

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