Combining Real-World and Randomized Control Trial Data Using Data-Adaptive Weighting via the On-Trial Score
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通过试验参与评分自适应加权整合真实世界与随机试验数据
- 研究问题
- 如何将真实世界常规治疗患者纳入随机试验的混合对照组，在扩大信息来源的同时控制患者差异导致的偏差和一类错误？
- 方法与思路
- 作者按真实世界患者与试验患者的相似程度构造试验参与评分权重，再用 Cox 比例风险模型估计治疗效应。
- 材料陈述的贡献
- 摘要称在几乎所有考察情景中，该方法控制一类错误、减小偏差，并较仅用试验数据降低方差；另有电子健康记录应用。
- 阅读关注点
- 应核对评分与权重的构造、数据可比性和效应识别条件、比例风险假设，以及一类错误控制所覆盖的模拟情景。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未说明未测混杂的处理，模拟结果不能保证任意场景无偏。
原文摘要
Clinical trials with a hybrid control arm (a control arm constructed from a combination of randomized patients and real-world data on patients receiving usual care in standard clinical practice) have the potential to decrease the cost of randomized trials while increasing the proportion of trial patients given access to novel therapeutics. However, due to stringent trial inclusion criteria and differences in care and data quality between trials and community practice, trial patients may have systematically different outcomes compared to their real-world counterparts. We propose a new method for analyses of trials with a hybrid control arm that efficiently controls bias and type I error. Under our proposed approach, selected real-world patients are weighted by a function of the "on-trial score," which reflects their similarity to trial patients. In contrast to previously developed hybrid control designs that assign the same weight to all real-world patients, our approach upweights of real-world patients who more closely resemble randomized control patients while dissimilar patients are discounted. Estimates of the treatment effect are obtained via Cox proportional hazards models. We compare our approach to existing approaches via simulations and apply these methods to a study using electronic health record data. Our proposed method is able to control type I error, minimize bias, and decrease variance when compared to using only trial data in nearly all scenarios examined. Therefore, our new approach can be used when conducting clinical trials by augmenting the standard-of-care arm with weighted patients from the EHR to increase power without inducing bias.