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

John S Preisser; Kurt K Lohman; Paul J Rathouz

doi:10.1002/sim.1241

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

John S Preisser·Kurt K Lohman·Paul J Rathouz

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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.

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Computer Simulation Female Humans Longitudinal Studies Male Models, Statistical Patient Dropouts Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.

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John S Preisser, Kurt K Lohman, Paul J Rathouz. 2002-10-30. Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random.. https://doi.org/10.1002/sim.1241

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