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 non-trial 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 analysing 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.

翻译：利用“真实世界数据”补充试验分析并改进决策，推动了统计方法的发展以应对引入的混杂因素。近年来，研究者开发了“混合”方法，首先通过倾向评分减弱可测量混杂因素，再通过（贝叶斯）动态借用处理未测量混杂因素。目前多数研究聚焦于利用历史对照增强对照组。本文则考虑通过“扩大准入”增强治疗组——扩大准入是严重致残或危及生命疾病患者通过非试验途径获取研究药物的通道。基于一项扩大准入的案例研究，我们提出了一种新方法（ProPP），该方法将倾向评分加权与修正幂先验概念上简洁且易于使用的结合。我们的加权方案基于对试验患者平均治疗效应的估计，约束条件为外部患者的权重不能超过试验患者。讨论了加权方案及倾向评分集成方法在因果推断中的一般性含义。模拟研究表明，与现有（混合）借用方法相比，我们的方法在精度和第一类错误率方面表现更优。我们通过联合分析威罗菲尼治疗转移性黑色素瘤的试验与扩大准入项目的个体患者数据，演示了该方法。该方法为应对先验-数据冲突提供了双重保障，并构成了试验与真实世界（扩大准入）数据证据合成的直接补充。