Li Jianghao, Du Yu, Liu Huayu, Yi Yanyao
Department of Biometrics, Eli Lilly and Company, Indianapolis, IN, USA.
Ther Innov Regul Sci. 2023 May;57(3):611-618. doi: 10.1007/s43441-023-00497-2. Epub 2023 Feb 21.
The use of information from real world to assess the effectiveness of medical products is becoming increasingly popular and more acceptable by regulatory agencies. According to a strategic real-world evidence framework published by U.S. Food and Drug Administration, a hybrid randomized controlled trial that augments internal control arm with real-world data is a pragmatic approach worth more attention. In this paper, we aim to improve on existing matching designs for such a hybrid randomized controlled trial. In particular, we propose to match the entire concurrent randomized clinical trial (RCT) such that (1) the matched external control subjects used to augment the internal control arm are as comparable as possible to the RCT population, (2) every active treatment arm in an RCT with multiple treatments is compared with the same control group, and (3) matching can be conducted and the matched set locked before treatment unblinding to better maintain the data integrity and increase the credibility of the analysis. Besides a weighted estimator, we also introduce a bootstrap method to obtain its variance estimation. The finite sample performance of the proposed method is evaluated by simulations based on data from a real clinical trial.
利用来自真实世界的信息来评估医疗产品的有效性正变得越来越普遍，并且越来越受到监管机构的认可。根据美国食品药品监督管理局发布的一个战略性真实世界证据框架，一种通过真实世界数据增强内部对照臂的混合随机对照试验是一种值得更多关注的务实方法。在本文中，我们旨在改进此类混合随机对照试验的现有匹配设计。具体而言，我们提议对整个同期随机临床试验（RCT）进行匹配，使得：（1）用于增强内部对照臂的匹配外部对照受试者与RCT人群尽可能可比；（2）具有多种治疗的RCT中的每个活性治疗臂都与同一个对照组进行比较；（3）可以在治疗揭盲之前进行匹配并锁定匹配集，以更好地维护数据完整性并提高分析的可信度。除了一个加权估计量之外，我们还引入一种自助法来获得其方差估计。基于一项真实临床试验的数据，通过模拟评估了所提方法的有限样本性能。
