Benkeser et al. present a very informative paper evaluating the efficiency gains of covariate adjustment in settings with binary, ordinal, and time-to-event outcomes. The adjustment method focuses on estimating the marginal treatment effect averaged over the covariate distribution in both arms combined. The authors show that covariate adjustment can achieve power gains that could find answers more quickly. The suggested approach is an important weapon in the armamentarium against epidemics like COVID-19. I recommend evaluating the procedure against more traditional approaches for conditional analyses (e.g., logistic regression) and against blinded methods of building prediction models followed by randomization-based inference.

中文翻译：

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关于“使用协变量调整二元、有序和事件发生时间结果来提高 COVID-19 治疗随机试验的精度和功效”的讨论

本克斯等人。发表了一篇内容丰富的论文，评估了在具有二元、有序和事件发生时间结果的设置中协变量调整的效率增益。调整方法侧重于估计在两个组合的协变量分布上平均的边际治疗效果。作者表明，协变量调整可以实现功率增益，从而更快地找到答案。建议的方法是武器库中对抗 COVID-19 等流行病的重要武器。我建议对照更传统的条件分析方法（例如逻辑回归）和构建预测模型的盲法方法评估该过程，然后再进行基于随机化的推理。