实验选择器交叉验证的目标最大似然估计器用于混合RCT-外部数据研究
Experiment-selector cross-validated targeted maximum likelihood estimator for hybrid RCT-external data studies
AI总结：
ES-CVTMLE通过交叉验证目标最大似然估计器优化混合RCT-外部数据研究的偏倚-方差权衡，提升统计效力并提供稳健推断。
摘要：
在随机对照试验（RCT）中加入外部数据可能会提高效力，但有引入偏倚的风险。为了选择和分析（单独的RCT或结合外部数据）具有最佳偏倚-方差权衡的实验，我们开发了一种新型的实验选择器交叉验证的目标最大似然估计器（ES-CVTMLE）用于随机-外部数据研究。该估计器利用两个偏倚估计值来确定是否整合外部数据：1）基于RCT与结合实验之间控制组的条件均值结果的差异函数，2）基于负对照结果的平均治疗效应估计值。我们定义了在不同偏倚程度下的ES-CVTMLE渐近分布，并通过蒙特卡洛模拟构造置信区间。我们在模拟中评估了ES-CVTMLE与其他三种数据融合估计器的性能，并在LEADER试验中展示了ES-CVTMLE区分偏倚和无偏外部对照的能力。ES-CVTMLE有潜力在提高效力的同时，为未来的混合RCT-外部数据研究提供相对稳健的推断。
英文摘要：
Augmenting a randomized controlled trial (RCT) with external data may increase power at the risk of introducing bias. To select and analyze the experiment (RCT alone or combined with external data) with the optimal bias-variance tradeoff, we develop a novel experiment-selector cross-validated targeted maximum likelihood estimator for randomized-external data studies (ES-CVTMLE). This estimator utilizes two estimates of bias to determine whether to integrate external data based on 1) a function of the difference in conditional mean outcome under control between the RCT and combined experiments and 2) an estimate of the average treatment effect on a negative control outcome (NCO). We define the asymptotic distribution of the ES-CVTMLE under varying magnitudes of bias and construct confidence intervals by Monte Carlo simulation. We evaluate ES-CVTMLE compared to three other data fusion estimators in simulations and demonstrate the ability of ES-CVTMLE to distinguish biased from unbiased external controls in a real data analysis of the effect of liraglutide on glycemic control from the LEADER trial. The ES-CVTMLE has the potential to improve power while providing relatively robust inference for future hybrid RCT-external data studies.
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