Polak Tobias B, Labrecque Jeremy A, Uyl-de Groot Carin A, van Rosmalen Joost
Department of Biostatistics, Erasmus MC, Rotterdam, the Netherlands.
Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands.
Stat Med. 2025 Aug;44(18-19):e70168. doi: 10.1002/sim.70168.
The incorporation of real-world data to supplement the analysis of trials and improve decision-making has spurred the development of statistical techniques to account for introduced confounding. Recently, "hybrid" methods have been developed through which measured confounding is first attenuated via propensity scores and unmeasured confounding is addressed through (Bayesian) dynamic borrowing. Most efforts to date have focused on augmenting control arms with historical controls. Here we consider augmenting treatment arms through "expanded access", which is a pathway of nontrial access to investigational medicine for patients with seriously debilitating or life-threatening illnesses. Motivated by a case study on expanded access, we developed a novel method (the ProPP) that provides a conceptually simple and easy-to-use combination of propensity score weighting and the modified power prior. Our weighting scheme is based on the estimation of the average treatment effect of the patients in the trial, with the constraint that external patients cannot receive higher weights than trial patients. The causal implications of the weighting scheme and propensity-score integrated approaches in general are discussed. In a simulation study, our method compares favorably with existing (hybrid) borrowing methods in terms of precision and type I error rate. We illustrate our method by jointly analyzing individual patient data from the trial and expanded access program for vemurafenib to treat metastatic melanoma. Our method provides a double safeguard against prior-data conflict and forms a straightforward addition to evidence synthesis methods of trial and real-world (expanded access) data.
纳入真实世界数据以补充试验分析并改善决策，这推动了用于处理引入的混杂因素的统计技术的发展。最近，已经开发出了“混合”方法，通过这些方法，首先通过倾向得分减弱测量到的混杂因素，并通过（贝叶斯）动态借用解决未测量的混杂因素。迄今为止，大多数努力都集中在用历史对照扩充对照组。在这里，我们考虑通过“扩大准入”来扩充治疗组，这是一条为患有严重衰弱或危及生命疾病的患者提供非试验性获取研究性药物的途径。受一个关于扩大准入的案例研究的启发，我们开发了一种新颖的方法（ProPP），该方法提供了一种概念上简单且易于使用的倾向得分加权和修正的先验幂的组合。我们的加权方案基于对试验中患者平均治疗效果的估计，其约束条件是外部患者不能比试验患者获得更高的权重。一般来说，讨论了加权方案和倾向得分综合方法的因果含义。在一项模拟研究中，我们的方法在精度和I型错误率方面与现有的（混合）借用方法相比具有优势。我们通过联合分析来自维莫非尼治疗转移性黑色素瘤的试验和扩大准入计划的个体患者数据来说明我们的方法。我们的方法为防止先验数据冲突提供了双重保障，并构成了对试验和真实世界（扩大准入）数据证据合成方法的直接补充。
