Augmenting randomized controlled trials (RCTs) with external real-world data (RWD) has the potential to improve the finite sample efficiency of treatment effect estimators. We describe using adaptive targeted maximum likelihood estimation (A-TMLE) for estimating the average treatment effect (ATE) by decomposing the ATE estimand into two components: a pooled-ATE estimand that combines data from both the RCT and external sources, and a bias estimand that captures the conditional effect of RCT enrollment on the outcome. This approach views the RCT data as the reference and corrects for inconsistencies of any kind between the RCT and the external data source. Given the growing abundance of external RWD from modern electronic health records, determining the optimal strategy to select candidate external patients for data integration remains an open yet critical problem. In this work, we begin by analyzing the robustness property of the A-TMLE estimator and then propose a matching-based sampling strategy that improves the robustness of the estimator with respect to the target estimand. Our proposed strategy is outcome-blind and involves matching based on two one-dimensional scores: the trial enrollment score and the propensity score in the external data. We demonstrate in simulations that our sampling strategy improves the coverage and shortens the widths of confidence intervals produced by A-TMLE. We illustrate our method with a case study of augmenting the DEVOTE cardiovascular safety trial by using the Optum Clinformatics claims database.

翻译：通过整合外部真实世界数据（RWD）来增强随机对照试验（RCT），有望提高治疗效果估计器的有限样本效率。本文描述了使用自适应目标最大似然估计（A-TMLE）来估计平均处理效应（ATE）的方法，该方法通过将ATE估计量分解为两个部分实现：一部分是结合了RCT与外部数据源的合并ATE估计量，另一部分是捕获RCT入组对结果条件效应的偏倚估计量。此方法将RCT数据视为参考基准，并校正RCT与外部数据源之间任何类型的不一致性。鉴于现代电子健康记录所产生的外部RWD日益丰富，如何确定选择候选外部患者进行数据整合的最优策略，仍然是一个悬而未决但至关重要的问题。在本研究中，我们首先分析了A-TMLE估计器的鲁棒性特性，随后提出了一种基于匹配的抽样策略，该策略提升了估计器相对于目标估计量的鲁棒性。我们提出的策略是结果盲法的，其匹配基于两个一维评分：试验入组评分和外部数据中的倾向评分。我们在模拟中证明，我们的抽样策略提高了A-TMLE所产生置信区间的覆盖率并缩短了其宽度。我们通过一个案例研究来阐述我们的方法：利用Optum Clinformatics理赔数据库增强DEVOTE心血管安全性试验。