The RobinCar Family: R Tools for Robust Covariate Adjustment in Randomized Clinical Trials

2026-01-20

AI Summary

This study addresses the lack of user-friendly, regulatory-compliant software tools for covariate adjustment in clinical trials aligned with the latest FDA guidance. To bridge this gap, the authors developed the R packages RobinCar and RobinCar2, which unify both conventional and state-of-the-art methods—including ANCOVA, G-computation, and PROCOVA™—within a single analytical framework. These tools support covariate-adjusted analyses for continuous, discrete, and time-to-event outcomes, implementing regulatory-endorsed approaches such as generalized linear models, machine learning algorithms, augmented covariate adjustment, covariate-adjusted log-rank tests, and marginal hazard ratio estimation. Validation on the ACTG 175 dataset demonstrates their computational efficiency and ease of use, substantially enhancing accessibility to analyses that meet current regulatory standards.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Causality
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Purpose: Covariate adjustment is a powerful statistical technique that can increase efficiency in clinical trials. Recent guidance from the U.S. FDA provided recommendations and best practices for using covariate adjustment. However, there has existed a gap between the extensive statistical literature on covariate adjustment and software that is easy to use and abides by these best practices. Methods: We have developed the RobinCar Family, which is comprised of RobinCar and RobinCar2. These two R packages enable covariate-adjusted analyses for continuous, discrete, and time-to-event outcomes that follow best practices. For continuous and discrete outcomes, the functions in the RobinCar Family facilitate traditional forms of covariate adjustment such as ANCOVA as well as more recent approaches like ANHECOVA, G-computation with generalized linear models and machine learning models, and adjustment for a super-covariate (as in PROCOVA(TM)). Functions for time-to-event outcomes implement the covariate-adjusted log-rank test, the stratified covariate-adjusted log-rank test, and the marginal covariate-adjusted hazard ratio. The RobinCar Family is supported by the ASA Biopharmaceutical Section Covariate Adjustment Scientific Working Group. Results: We provide an accessible overview of the covariate-adjusted statistical methods, and describe how they are implemented in RobinCar and RobinCar2. We highlight important usage notes for clinical trial practitioners. Conclusion: We apply RobinCar and RobinCar2 functions by analyzing data from the AIDS Clinical Trials Group Study 175, demonstrating that they are straightforward and user-friendly.

Problem

• covariate adjustment
• randomized clinical trials
• statistical software
• FDA guidance
• best practices

Innovation

• covariate adjustment
• R package
• clinical trials
• machine learning
• time-to-event analysis