Journal
STATISTICS IN MEDICINE
Volume 21, Issue 20, Pages 3035-3054Publisher
WILEY-BLACKWELL
DOI: 10.1002/sim.1241
Keywords
correlated data; drop-outs; estimating equations; logistic models; repeated measures
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Funding
- NIA NIH HHS [AG14131] Funding Source: Medline
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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 (C) 2002 John Wiley Sons, Ltd.
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