Doubly robust generalized estimating equations for longitudinal data.

Abstract

A popular method for analysing repeated-measures data is generalized estimating equations (GEE). When response data are missing at random (MAR), two modifications of GEE use inverse-probability weighting and imputation. The weighted GEE (WGEE) method involves weighting observations by their inverse probability of being observed, according to some assumed missingness model. Imputation methods involve filling in missing observations with values predicted by an assumed imputation model. WGEE are consistent when the data are MAR and the dropout model is correctly specified. Imputation methods are consistent when the data are MAR and the imputation model is correctly specified.Recently, doubly robust (DR) methods have been developed. These involve both a model for probability of missingness and an imputation model for the expectation of each missing observation, and are consistent when either is correct. We describe DR GEE, and illustrate their use on simulated data. We also analyse the INITIO randomized clinical trial of HIV therapy allowing for MAR dropout.

DOI: 10.1002/sim.3520

Cite this paper

@article{Seaman2009DoublyRG, title={Doubly robust generalized estimating equations for longitudinal data.}, author={Shaun R. Seaman and Andrew J Copas}, journal={Statistics in medicine}, year={2009}, volume={28 6}, pages={937-55} }