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，对已知与结局至少中等程度相关的基线协变量进行调整，可提高试验的统计功效。近期研究表明，更灵活的频率学派设计（如信息自适应设计和自适应多组设计）尤其受益于此。然而，尽管贝叶斯自适应设计日益普及，协变量调整在该类设计中的特性尚未得到系统刻画。本文聚焦于允许基于治疗优效性证据进行中期分析提前终止的贝叶斯自适应设计子类。我们同时考虑可压缩与不可压缩的目标估计量，并展示如何从调整分析中获得边际估计量的后验样本。针对三种常见结局类型描述多种估计量。通过模拟研究，评估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结局的新冠肺炎试验实际案例。结果表明：在所有场景中，与未调整分析相比，协变量调整均可提高统计功效与试验早期终止概率，并降低预期样本量。