A targeted learning (TL) framework is developed to estimate the difference in the restricted mean survival time (RMST) for a clinical trial with time-to-event outcomes. The approach starts by defining the target estimand as the RMST difference between investigational and control treatments. Next, an efficient estimation method is introduced: a targeted minimum loss estimator (TMLE) utilizing pseudo-observations. Moreover, a version of the copy reference (CR) approach is developed to perform a sensitivity analysis for right-censoring. The proposed TL framework is demonstrated using a real data application.

翻译：本文开发了一种目标学习（TL）框架，用于估计具有时间-事件结局的临床试验中受限平均生存时间（RMST）的差异。该方法首先将目标估计量定义为研究性治疗与对照治疗之间的RMST差异。接着，引入了一种高效的估计方法：利用伪观测的目标最小损失估计量（TMLE）。此外，还开发了一种复制参照（CR）方法的变体，以对右删失进行敏感性分析。所提出的TL框架通过一个真实数据应用进行了演示。