• John S Preisser, Kurt K Lohman, and Paul J Rathouz.
• Department of Biostatistics, CB #7420, School of Public Health, University of North Carolina, Chapel Hill 27599, USA. jpreisse@bios.unc.edu
• Stat Med. 2002 Oct 30;21(20):3035-54.

AbstractThe 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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