CRTgeeDR: An R Package for Doubly Robust Generalized Estimating Equations Estimations in Cluster Randomized Trials with Missing Data
Résumé
Semi-parametric approaches based on generalized estimating equation (GEE) are widely
used to analyse correlated outcomes. Most available softwares had been developed for
longitudinal settings. In this paper, we present a R package CRTgeeDR for estimating
parameters in marginal regression in cluster randomized trials (CRTs). Theory for adjusting
for missing at random outcomes by inverse-probability weighting methods (IPW)
based on the use of a propensity score had been largely studied and implemented. We
exhibit that in CRTs most of the available softwares use an implementation of weights
that lead to a bias in estimation if a non-independence working correlation structure is
chosen. In CRTgeeDR, we solve this problem by using a different implementation while
keeping the consistency properties of the IPW. Moreover, in CRTs using an augmented
GEE (AUG) allow to improve efficiency by adjusting for treatment-covariate interactions
and imbalance in baseline covariates between treatment groups using an outcome model.
In CRTgeeDR, we extend the abilities of existing packages such as geepack and geeM
to allow such data augmentation. Finally, one may want to combine IPW and AUG in
a Doubly Robust (DR) estimator, which lead to consistent estimation when either the
propensity score or the outcome model corresponds to the true data generation process
(Prague, Wang, Stephens, Tchetgen Tchetgen, and De gruttola 2015). The DR approach
is implemented in CRTgeeDR. Simulations studies demonstrate the consistency of IPW
implemented in CRTgeeDR and the gains associated with the use of the DR for analyzing
a binary outcome using a logit regression. Finally, we reanalyzed data from a sanitation
CRT in developing countries (Guiteras, Levinsohn, and Mobarak 2015a) with the DR
approach compared to classical GEE and demonstrated a signiffcant intervention effect.
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