RobinCar2 is a package that provides robust covariate adjustment methods for estimating and inferring treatment effects under stratified randomization. The implemented methods follow the FDA’s final guidance on covariate adjustment and are supported by a well-established body of literature. For linear adjustment, see, for example, Tsiatis (2008), Bugni et al. (2018), Ye, Shao, Yi, and Zhao (2023), and Ye, Shao, and Yi (2022). For nonlinear adjustment, see, e.g., Rosenblum and van der Laan (2010), Wang et al. (2021), Ye, Bannick, Yi, and Shao (2023), and Bannick, Shao, Liu, Du, Yi, and Ye (2024).
All the following papers (added above) are cited in the FDA guidance.
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Bugni, F, IA Canay, and AM Shaikh, 2018, Inference Under Covariate-Adaptive Randomization, Journal of the American Statistical Association, 113(524):1784-1796.
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Tsiatis, AA, M Davidian, M Zhang, and X Lu, 2008, Covariate Adjustment for Two-Sample Treatment Comparisons in Randomized Trials: A Principled Yet Flexible Approach, Statistics in Medicine, 27(23):4658-4677.
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Wang, B, R Susukida, R Mojtabai, M Amin-Esmaeili, and M Rosemblum, 2021. Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Covariate Adjustment, Journal of the American Statistical Association, doi: 10.1080/01621459.2021.1981338.
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Rosenblum, M and MJ van der Laan, 2010, Simple, Efficient Estimators of Treatment Effects in Randomized Trials Using Generalized Linear Models to Leverage Baseline Variables, International Journal of Biostatistics, 6(1):13.
You can install the current development version from github with:
if (!require("remotes")) {
install.packages("remotes")
}
remotes::install_github(
"openpharma/RobinCar2"
)To cite RobinCar2 please see
here.