The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) E9 (R1) Addendum provides a framework for defining estimands in clinical trials. Treatment policy strategy is the mostly used approach to handle intercurrent events in defining estimands. Imputing missing values for potential outcomes under the treatment policy strategy has been discussed in the literature. Missing values as a result of administrative study withdrawals (such as site closures due to business reasons, COVID-19 control measures, and geopolitical conflicts, etc.) are often imputed in the same way as other missing values occurring after intercurrent events related to safety or efficacy. Some research suggests using a hypothetical strategy to handle the treatment discontinuations due to administrative study withdrawal in defining the estimands and imputing the missing values based on completer data assuming missing at random, but this approach ignores the fact that subjects might experience other intercurrent events had they not had the administrative study withdrawal. In this article, we consider the administrative study withdrawal censors the normal real-world like intercurrent events and propose two methods for handling the corresponding missing values under the retrieved dropout imputation framework. Simulation shows the two methods perform well. We also applied the methods to actual clinical trial data evaluating an anti-diabetes treatment.

翻译：国际人用药品注册技术协调会（ICH）E9（R1）增补文件为临床试验中估计量的定义提供了框架。处理策略是定义估计量时处理并发事件最常用的方法。文献中已讨论了在治疗策略下对潜在结局缺失值进行填补的方法。因管理性研究退出（如商业原因导致的中心关闭、COVID-19防控措施及地缘政治冲突等）而产生的缺失值，通常采用与安全性或有效性相关并发事件发生后其他缺失值相同的方式进行填补。有研究建议采用假设性策略来处理因管理性研究退出导致的治疗中止，在定义估计量时基于完成者数据并假设数据随机缺失来填补缺失值，但这种方法忽略了受试者若非因管理性研究退出可能经历其他并发事件的事实。本文认为管理性研究退出截断了现实世界中正常的并发事件，并在检索性脱落填补框架下提出两种处理相应缺失值的方法。模拟研究表明两种方法均表现良好。我们还将这些方法应用于评估一种抗糖尿病治疗的实际临床试验数据。