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.

翻译：目的：协变量调整是一种能够提高临床试验效率的强大统计技术。美国FDA近期发布的指南为协变量调整的使用提供了建议与最佳实践。然而，关于协变量调整的广泛统计学文献与易于使用且遵循这些最佳实践的软件之间始终存在差距。方法：我们开发了由RobinCar与RobinCar2组成的RobinCar系列。这两个R软件包支持对连续型、离散型及时间-事件结局进行符合最佳实践的协变量调整分析。针对连续型与离散型结局，RobinCar系列中的函数支持传统协变量调整方法（如ANCOVA）以及最新方法，包括ANHECOVA、基于广义线性模型与机器学习模型的G-计算，以及超协变量调整（如PROCOVA(TM)）。针对时间-事件结局的函数实现了协变量调整对数秩检验、分层协变量调整对数秩检验以及边际协变量调整风险比。RobinCar系列得到美国统计学会生物制药分会协变量调整科学工作组的支持。结果：我们对协变量调整统计方法进行了简明概述，并阐述了其在RobinCar与RobinCar2中的实现方式。我们重点说明了临床试验实践者需注意的关键使用事项。结论：通过分析艾滋病临床试验组175研究的数据，我们应用了RobinCar与RobinCar2的函数，证明其操作直观且用户友好。