In conventional randomized controlled trials, adjustment for baseline values of covariates known to be at least moderately associated with the outcome increases the power of the trial. Recent work has shown particular benefit for more flexible frequentist designs, such as information adaptive and adaptive multi-arm designs. However, covariate adjustment has not been characterized within the more flexible Bayesian adaptive designs, despite their growing popularity. We focus on a subclass of these which allow for early stopping at an interim analysis given evidence of treatment superiority. We consider both collapsible and non-collapsible estimands, and show how to obtain posterior samples of marginal estimands from adjusted analyses. We describe several estimands for three common outcome types. We perform a simulation study to assess the impact of covariate adjustment using a variety of adjustment models in several different scenarios. This is followed by a real world application of the compared approaches to a COVID-19 trial with a binary endpoint. For all scenarios, it is shown that covariate adjustment increases power and the probability of stopping the trials early, and decreases the expected sample sizes as compared to unadjusted analyses.

翻译：在传统随机对照试验中，调整与结局至少存在中等关联的基线协变量可提高试验效能。近期研究表明，这种调整在更灵活的频率学派设计（如信息自适应设计和自适应多臂设计）中具有特殊优势。然而，尽管贝叶斯自适应设计日益普及，协变量调整方法尚未在此类更灵活的设计中得到系统阐述。我们聚焦于允许根据治疗优效性证据在中期分析时提前终止的贝叶斯自适应设计子类，分别考虑可压缩与不可压缩估计量，并阐述如何从调整分析中获取边缘估计量的后验样本。针对三种常见结局类型，我们描述了多种估计量。通过模拟研究评估了在不同场景下使用多种调整模型进行协变量调整的影响，随后将所比较的方法应用于一项具有二分类终点的COVID-19试验真实案例。结果表明，在所有场景中，与未调整分析相比，协变量调整可提高试验效能和提前终止试验的概率，并降低期望样本量。