Longitudinal studies are often subject to missing data. The ICH E9(R1) addendum addresses the importance of defining a treatment effect estimand with the consideration of intercurrent events. Jump-to-reference (J2R) is one classically envisioned control-based scenario for the treatment effect evaluation using the hypothetical strategy, where the participants in the treatment group after intercurrent events are assumed to have the same disease progress as those with identical covariates in the control group. We establish new estimators to assess the average treatment effect based on a proposed potential outcomes framework under J2R. Various identification formulas are constructed under the assumptions addressed by J2R, motivating estimators that rely on different parts of the observed data distribution. Moreover, we obtain a novel estimator inspired by the efficient influence function, with multiple robustness in the sense that it achieves $n^{1/2}$-consistency if any pairs of multiple nuisance functions are correctly specified, or if the nuisance functions converge at a rate not slower than $n^{-1/4}$ when using flexible modeling approaches. The finite-sample performance of the proposed estimators is validated in simulation studies and an antidepressant clinical trial.

翻译：纵向研究常存在数据缺失问题。ICH E9(R1)增补文件强调了在考虑并发事件时定义治疗效应估计目标的重要性。跳跃至参考组（J2R）是一种经典的控制导向场景，采用假设策略评估治疗效果，即假设治疗组中发生并发事件的参与者与对照组中具有相同协变量的参与者具有相同的疾病进展情况。我们在J2R框架下基于提出的潜在结果框架建立了新的估计量，用于评估平均治疗效果。在J2R所涉及的假设条件下，我们构建了多种识别公式，从而催生了依赖于观测数据分布不同部分的估计量。此外，受有效影响函数的启发，我们获得了一种新颖的估计量，该估计量具有多重稳健性：即当任意多组扰动函数被正确设定时，或当采用灵活建模方法时扰动函数以不低于n^{-1/4}的速率收敛时，该估计量可实现n^{1/2}一致性。通过模拟研究和抗抑郁药物临床试验，验证了所提出估计量的有限样本性能。