BACKGROUND Randomized controlled trials are considered the gold standard for evaluating experimental treatments but often require large sample sizes. Single-arm trials require smaller sample sizes but are subject to bias when using historical control data for comparative inferences. This article presents a Bayesian adaptive synthetic-control design that exploits historical control data to create a hybrid of a single-arm trial and a randomized controlled trial. METHODS The Bayesian adaptive synthetic control design has two stages. In stage 1, a prespecified number of patients are enrolled in a single arm given the experimental treatment. Based on the stage 1 data, applying propensity score matching and Bayesian posterior prediction methods, the usefulness of the historical control data for identifying a pseudo sample of matched synthetic-control patients for making comparative inferences is evaluated. If a sufficient number of synthetic controls can be identified, the single-arm trial is continued. If not, the trial is switched to a randomized controlled trial. The performance of The Bayesian adaptive synthetic control design is evaluated by computer simulation. RESULTS The Bayesian adaptive synthetic control design achieves power and unbiasedness similar to a randomized controlled trial but on average requires a much smaller sample size, provided that the historical control data patients are sufficiently comparable to the trial patients so that a good number of matched controls can be identified in the historical control data. Compared to a single-arm trial, The Bayesian adaptive synthetic control design yields much higher power and much smaller bias. CONCLUSION The Bayesian adaptive synthetic-control design provides a useful tool for exploiting historical control data to improve the efficiency of single-arm phase II clinical trials, while addressing the problem of bias when comparing trial results to historical control data. The proposed design achieves power similar to a randomized controlled trial but may require a substantially smaller sample size.

中文翻译：

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BASIC：用于 II 期临床试验的贝叶斯自适应合成控制设计。

背景随机对照试验被认为是评估实验治疗的黄金标准，但通常需要大样本量。单臂试验需要较小的样本量，但在使用历史对照数据进行比较推论时会出现偏差。本文提出了一种贝叶斯自适应合成控制设计，该设计利用历史控制数据来创建单臂试验和随机对照试验的混合体。方法贝叶斯自适应综合控制设计有两个阶段。在第一阶段，预先指定数量的患者被纳入单臂接受实验治疗。基于第一阶段数据，应用倾向评分匹配和贝叶斯后验预测方法，评估历史对照数据用于识别匹配合成对照患者的伪样本以进行比较推断的有用性。如果可以识别出足够数量的合成对照，则继续进行单臂试验。如果没有，试验将转为随机对照试验。通过计算机仿真评估贝叶斯自适应综合控制设计的性能。结果 贝叶斯自适应综合控制设计实现了与随机对照试验相似的功效和无偏性，但平均而言需要小得多的样本量，前提是患者的历史控制数据与试验患者具有足够的可比性，以便大量匹配的对照可以可以在历史控制数据中识别。与单臂试验相比，贝叶斯自适应合成控制设计产生更高的功效和更小的偏差。结论 贝叶斯自适应合成控制设计为利用历史控制数据提高单臂 II 期临床试验的效率提供了有用的工具，同时解决了将试验结果与历史控制数据进行比较时的偏倚问题。所提出的设计实现了与随机对照试验相似的功效，但可能需要小得多的样本量。