Covariate handling approaches in combination with dynamic borrowing for hybrid control studies.
Covariate handling approaches in combination with dynamic borrowing for hybrid control studies.
复制标题
协变量处理方法与混合控制研究的动态借用相结合。
DOI：
10.1002/pst.2297
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发表时间：
2023
影响因子：
1.5
通讯作者：
Zhu,Jiawen
中科院分区：
文献类型：
--
作者：
Fu,Chenqi;Pang,Herbert;Zhou,Shouhao;Zhu,Jiawen
在进行随机对照试验（rct）时，从外部对照中借用数据是一种有吸引力的证据合成策略。通常被称为混合对照试验，它们利用来自临床试验或潜在现实世界数据（RWD）的现有对照数据，使试验设计能够将更多患者分配到新的干预组，并提高效率或降低主要RCT的成本。目前已经建立和发展了几种外部控制数据的借用方法，其中倾向得分法和贝叶斯动态借用框架发挥了重要作用。注意到倾向评分方法和贝叶斯层次模型的独特优势，我们以互补的方式利用这两种方法来分析混合控制研究。在本文中，我们回顾了协变量调整、倾向得分匹配和加权与动态借用相结合的方法，并通过综合模拟比较了这些方法的性能。检验了不同程度的协变量不平衡和混杂。我们的研究结果表明，在所调查的设置下，传统的协变量调整与贝叶斯相称先验模型相结合提供了最高的功率，并具有良好的I型误差控制。它具有理想的性能，特别是在不同混杂程度的情况下。为了在探索性设置中估计疗效信号，建议使用协变量调整方法结合贝叶斯相称先验。
Borrowing data from external control has been an appealing strategy for evidence synthesis when conducting randomized controlled trials (RCTs). Often named hybrid control trials, they leverage existing control data from clinical trials or potentially real‐world data (RWD), enable trial designs to allocate more patients to the novel intervention arm, and improve the efficiency or lower the cost of the primary RCT. Several methods have been established and developed to borrow external control data, among which the propensity score methods and Bayesian dynamic borrowing framework play essential roles. Noticing the unique strengths of propensity score methods and Bayesian hierarchical models, we utilize both methods in a complementary manner to analyze hybrid control studies. In this article, we review methods including covariate adjustments, propensity score matching and weighting in combination with dynamic borrowing and compare the performance of these methods through comprehensive simulations. Different degrees of covariate imbalance and confounding are examined. Our findings suggested that the conventional covariate adjustment in combination with the Bayesian commensurate prior model provides the highest power with good type I error control under the investigated settings. It has desired performance especially under scenarios of different degrees of confounding. To estimate efficacy signals in the exploratory setting, the covariate adjustment method in combination with the Bayesian commensurate prior is recommended.