The R Journal: article published in 2017, volume 9:2

CRTgeeDR: an R Package for Doubly Robust Generalized Estimating Equations Estimations in Cluster Randomized Trials with Missing Data
Melanie Prague, Rui Wang and Victor De Gruttola , The R Journal (2017) 9:2, pages 105-115.

Abstract Semi-parametric approaches based on generalized estimating equations (GEE) are widely used to analyze correlated outcomes in longitudinal settings. In this paper, we present a package CRTgeeDR developed for cluster randomized trials with missing data (CRTs). For use of inverse probability weighting to adjust for missing data in cluster randomized trials, we show that other software lead to biased estimation for non-independence working correlation structure. CRTgeeDR solves this problem. We also extend the ability of existing packages to allow augmented Doubly Robust GEE estimation (DR). Simulation studies demonstrate the consistency of estimators implemented in CRTgeeDR compared to packages such as geepack and the gains associated with the use of the DR for analyzing a binary outcome using a logistic regression. Finally, we illustrate the method on data from a sanitation CRT in developing countries.

Received: 2016-11-16; online 2017-08-25
CRAN packages: CRTgeeDR, gee, geepack, geeM, ipw, drgee, CausalGAM, tmle, tmlenet, numDeriv, geesmv
CRAN Task Views implied by cited CRAN packages: SocialSciences, Econometrics, NumericalMathematics, Robust

This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2017-041,
  author = {Melanie Prague and Rui Wang and Victor De Gruttola},
  title = {{CRTgeeDR: an R Package for Doubly Robust Generalized
          Estimating Equations Estimations in Cluster Randomized
          Trials with Missing Data}},
  year = {2017},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2017-041},
  url = {https://doi.org/10.32614/RJ-2017-041},
  pages = {105--115},
  volume = {9},
  number = {2}
}