Augmenting treatment arms with external data through propensity-score weighted power-priors: an application in expanded access
通过倾向得分加权幂先验将外部资料纳入治疗组AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确讨论倾向得分、混杂控制及试验患者平均处理效应，属于因果推断与外部资料整合。
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通过倾向得分加权幂先验将外部资料纳入治疗组
- 研究问题
- 如何把扩大使用项目中的非试验患者资料纳入试验治疗组，同时处理可观测混杂及外部资料与试验资料之间的冲突？
- 方法与思路
- 作者提出 ProPP，将倾向得分加权与修正幂先验结合，围绕试验患者的目标效应构造权重，并限制外部患者权重不高于试验患者。
- 材料陈述的贡献
- 摘要称该方法在模拟中的精度和第一类错误率表现较好，并用维莫非尼试验与扩大使用资料展示联合分析和冲突防护。
- 阅读关注点
- 核对加权目标、外部患者可比性与动态借用规则，并检查未测量混杂下的因果解释，以及错误率控制的模拟范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未给出具体识别条件，动态借用不能仅凭摘要认定为消除未测量混杂。
原文摘要
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.