There has been a growing interest in covariate adjustment in the analysis of randomized controlled trials in past years. For instance, the U.S. Food and Drug Administration recently issued guidance that emphasizes the importance of distinguishing between conditional and marginal treatment effects. Although these effects coincide in linear models, this is not typically the case in other settings, and this distinction is often overlooked in clinical trial practice. Considering these developments, this paper provides a review of when and how to utilize covariate adjustment to enhance precision in randomized controlled trials. We describe the differences between conditional and marginal estimands and stress the necessity of aligning statistical analysis methods with the chosen estimand. Additionally, we highlight the potential misalignment of current practices in estimating marginal treatment effects. Instead, we advocate for the utilization of standardization, which can improve efficiency by leveraging the information contained in baseline covariates while remaining robust to model misspecification. Finally, we present practical considerations that have arisen in our respective consultations to further clarify the advantages and limitations of covariate adjustment.

翻译：近年来，在随机对照试验的分析中，协变量调整日益受到关注。例如，美国食品药品监督管理局近期发布的指南强调区分条件处理效应与边际处理效应的重要性。尽管在线性模型中这两种效应一致，但在其他情况下通常并非如此，而这一区别在临床试验实践中常被忽视。鉴于这些进展，本文对何时以及如何利用协变量调整以提高随机对照试验精度进行了综述。我们阐述了条件效应与边际效应的差异，并强调统计分析方法需与所选估计量保持一致的必要性。此外，我们指出现有实践在估计边际处理效应时可能存在偏差，并建议采用标准化方法——该方法能通过利用基线协变量信息提升效率，同时对模型误设保持稳健性。最后，我们结合各自咨询工作中的实际考量，进一步阐明协变量调整的优势与局限性。