论文标题
随机试验和现实世界数据的弹性整合分析，用于治疗异质性估计
Elastic Integrative Analysis of Randomized Trial and Real-World Data for Treatment Heterogeneity Estimation
论文作者
论文摘要
我们提出了对随机试验和现实世界数据的基于测试的弹性整合分析，以通过已知效应修饰符向量估算治疗效应异质性。当现实世界数据不受偏见的影响时，我们的方法将试验和现实世界数据结合在一起，以进行有效的估计。利用试验设计，我们构建了一个测试来决定是否使用现实世界数据。我们表征了局部替代方案下基于测试的估计量的渐近分布。我们提供一个数据自适应过程，以选择具有良好有限样本覆盖属性的最小均方根误差和弹性置信区间的测试阈值。
We propose a test-based elastic integrative analysis of the randomized trial and real-world data to estimate treatment effect heterogeneity with a vector of known effect modifiers. When the real-world data are not subject to bias, our approach combines the trial and real-world data for efficient estimation. Utilizing the trial design, we construct a test to decide whether or not to use real-world data. We characterize the asymptotic distribution of the test-based estimator under local alternatives. We provide a data-adaptive procedure to select the test threshold that promises the smallest mean square error and an elastic confidence interval with a good finite-sample coverage property.
