geeCRT: Bias-Corrected GEE for Cluster Randomized Trials

Population-averaged models have been increasingly used in the design and analysis of cluster randomized trials (CRTs). To facilitate the applications of population-averaged models in CRTs, the package implements the generalized estimating equations (GEE) and matrix-adjusted estimating equations (MAEE) approaches to jointly estimate the marginal mean models correlation models both for general CRTs and stepped wedge CRTs. Despite the general GEE/MAEE approach, the package also implements a fast cluster-period GEE method specifically for stepped wedge CRTs with large and variable cluster-period sizes and gives a simple and efficient estimating equations approach based on the cluster-period means to estimate the intervention effects as well as correlation parameters. In addition, the package also provides functions for generating correlated binary data with specific mean vector and correlation matrix based on the multivariate probit method in Emrich and Piedmonte (1991) <doi:10.1080/00031305.1991.10475828> or the conditional linear family method in Qaqish (2003) <doi:10.1093/biomet/90.2.455>.

Version: 0.0.1
Depends: R (≥ 3.6.0)
Imports: MASS, rootSolve, mvtnorm
Suggests: knitr, rmarkdown
Published: 2020-11-11
Author: Hengshi Yu [aut, cre], Fan Li [aut], Paul Rathouz [aut], Elizabeth L. Turner [aut], John Preisser [aut]
Maintainer: Hengshi Yu <hengshi at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: geeCRT results


Reference manual: geeCRT.pdf
Package source: geeCRT_0.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: geeCRT_0.0.1.tgz, r-oldrel: geeCRT_0.0.1.tgz


Please use the canonical form to link to this page.