Randomized controlled trials are considered the gold standard to evaluate the treatment effect (estimand) for efficacy and safety. According to the recent International Council on Harmonisation (ICH)-E9 addendum (R1), intercurrent events (ICEs) need to be considered when defining an estimand, and principal stratum is one of the five strategies to handle ICEs. Qu et al. (2020, Statistics in Biopharmaceutical Research 12:1-18) proposed estimators for the adherer average causal effect (AdACE) for estimating the treatment difference for those who adhere to one or both treatments based on the causal-inference framework, and demonstrated the consistency of those estimators; however, this method requires complex custom programming related to high-dimensional numeric integrations. In this article, we implemented the AdACE estimators using multiple imputation (MI) and constructs CI through bootstrapping. A simulation study showed that the MI-based estimators provided consistent estimators with the nominal coverage probabilities of CIs for the treatment difference for the adherent populations of interest. As an illustrative example, the new method was applied to data from a real clinical trial comparing 2 types of basal insulin for patients with type 1 diabetes.

翻译：根据最近国际协调理事会(ICH)-E9增编(R1),在确定估计值时,需要考虑潮间事件(ICES),而主要直流是处理ICE的五种战略之一。 Qu等人(2020年,生物药品研究统计12:1-18)提议为遵守者平均因果关系(AdACE)估计标准,以估计根据因果推断框架坚持一种或两种治疗的人的治疗差异,并显示这些估计者的一致性;然而,这一方法要求与高维数整合有关的复杂定制程序。在文章中,我们采用AACE估计器的多发性(MI),通过示波纹仪构建CI。模拟研究表明,以MI为基础的估计器提供了与根据因果推断值显示的概率比值一致的衡量器,以显示CICs对某种或两种治疗的概率的差别,从BA型实验型数据到BA型试验型数据样本的对比方法。