RobinCar: ROBust estimation and INference for Covariate Adjustment in Randomized clinical trials

RobinCar is a package that allows for robust estimation and inference for treatment effects in randomized clinical trials when covariates are used at the design and/or analysis stages of the trial. Supported covariate-adaptive randomization schemes at the design phase are simple randomization, stratified permuted block randomization, biased coin randomization, and Pocock and Simon's minimization. Statistical methods at the analysis stage are model-assisted and assumption-lean, in accordance with FDA guidance on covariate adjustment. Publications describing the methods are listed here.

See also RobinCar2, which is a lite version of RobinCar and is supported by the ASA Biopharmaceutical Section Covariate Adjustment Scientific Working Group Software Subteam.

Authors

Ting Ye, Yanyao Yi, Marlena Bannick (maintainer), Yuhan Qian, and Faith Bian

Documentation

To view documentation about the functions, see the RobinCar website here: https://marlenabannick.com/RobinCar/. You will also find vignettes about how to use the functions.

Installation

RobinCar is now available on CRAN!

1. Install via CRAN

install.packages("RobinCar")

2. Install with devtools

To get the most recent version in development, you can install the package with devtools:

devtools::install_github("mbannick/RobinCar")

3. Clone repository

Or to download the package, you may clone the repository:

git clone https://github.com/mbannick/RobinCar.git

Publications

Here are publications and preprints that explain the methods in RobinCar:

• Ting Ye, Jun Shao, Yanyao Yi (2023)
• Marlena Bannick, Jun Shao, Jingyi Liu, Yu Du, Yanyao Yi, Ting Ye (2025)
• Ting Ye, Marlena Bannick, Yanyao Yi, Jun Shao (2023)
• Ting Ye, Jun Shao, Yanyao Yi, Qinyuan Zhao (2023)
• Xiaoyu Qiu, Yuhan Qian, Jaehwan Yi, Jinqiu Wang, Yu Du, Yanyao Yi, Ting Ye (2025)