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:纵向研究往往缺少数据。 ICH E9(R1)增编谈到确定治疗效果估计值的重要性,并同时考虑各种时序事件。 跳到参考(J2R)是使用假设战略进行治疗效果评价的一种典型的基于控制的设想情景,即假定发生间流事件后治疗组的参与者的疾病进展与控制组中具有相同共变体的患者相同。我们建立了新的估计器,根据J2R下的拟议潜在成果框架评估平均治疗效果。各种识别公式是根据J2R处理的假设构建的,用以激励依赖所观察到的数据分布的不同部分的估测员。此外,我们获得了受有效影响功能启发的新的估计值,因为如果正确指定了多个骚扰功能的一对夫妇,或者如果在使用灵活模拟方法时,审查功能以不低于$n ⁇ -1/4美元的速度集中。在模拟试验中,拟议的定点和模拟试验是模拟反压试验的结果。