A frequentist approach to dynamic borrowing.
A frequentist approach to dynamic borrowing.
复制标题
动态借贷的频率论方法。
DOI：
10.1002/bimj.202100406
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Zhu,Jiawen
中科院分区：
文献类型：
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作者：
Li,Ruilin;Lin,Ray;Huang,Jiangeng;Tian,Lu;Zhu,Jiawen
人们越来越有兴趣利用外部控制数据来增强临床试验中的随机对照组数据并做出更信息丰富的决策。近年来，作为外部控制，现实世界数据的质量和可用性稳步提高。然而，通过直接将外部对照与随机对照合并来借用信息可能会导致对治疗效果的估计出现偏差。为了更好地控制误报错误，人们提出了贝叶斯框架下的动态借用方法。然而，这些贝叶斯动态借用方法的数值计算，特别是参数调整，在实践中仍然是一个挑战。在本文中，我们提出了贝叶斯相应先验借用方法的频率论解释，并从优化的角度描述了与该方法相关的内在挑战。受这一观察的启发，我们提出了一种使用自适应套索的新动态借用方法。从该方法得出的治疗效果估计遵循已知的渐近分布，可用于构建置信区间并进行假设检验。该方法的有限样本性能通过不同设置下的广泛蒙特卡罗模拟进行评估。与贝叶斯方法相比，我们观察到自适应套索具有高度竞争性的性能。还根据数值研究的结果和说明示例彻底讨论了选择调整参数的方法。
There has been growing interest in leveraging external control data to augment a randomized control group data in clinical trials and enable more informative decision making. In recent years, the quality and availability of real‐world data have improved steadily as external controls. However, information borrowing by directly pooling such external controls with randomized controls may lead to biased estimates of the treatment effect. Dynamic borrowing methods under the Bayesian framework have been proposed to better control the false positive error. However, the numerical computation and, especially, parameter tuning, of those Bayesian dynamic borrowing methods remain a challenge in practice. In this paper, we present a frequentist interpretation of a Bayesian commensurate prior borrowing approach and describe intrinsic challenges associated with this method from the perspective of optimization. Motivated by this observation, we propose a new dynamic borrowing approach using adaptive lasso. The treatment effect estimate derived from this method follows a known asymptotic distribution, which can be used to construct confidence intervals and conduct hypothesis tests. The finite sample performance of the method is evaluated through extensive Monte Carlo simulations under different settings. We observed highly competitive performance of adaptive lasso compared to Bayesian approaches. Methods for selecting tuning parameters are also thoroughly discussed based on results from numerical studies and an illustration example.