The current COVID-19 pandemic poses numerous challenges for ongoing clinical trials and provides a stress-testing environment for the existing principles and practice of estimands in clinical trials. The pandemic may increase the rate of intercurrent events (ICEs) and missing values, spurring a great deal of discussion on amending protocols and statistical analysis plans to address these issues. In this article we revisit recent research on estimands and handling of missing values, especially the ICH E9 (R1) on Estimands and Sensitivity Analysis in Clinical Trials. Based on an in-depth discussion of the strategies for handling ICEs using a causal inference framework, we suggest some improvements in applying the estimand and estimation framework in ICH E9 (R1). Specifically, we discuss a mix of strategies allowing us to handle ICEs differentially based on reasons for ICEs. We also suggest ICEs should be handled primarily by hypothetical strategies and provide examples of different hypothetical strategies for different types of ICEs as well as a road map for estimation and sensitivity analyses. We conclude that the proposed framework helps streamline translating clinical objectives into targets of statistical inference and automatically resolves many issues with defining estimands and choosing estimation procedures arising from events such as the pandemic.

翻译：当前的 COVID-19 大流行为正在进行的临床试验带来了诸多挑战，并为临床试验中 estimands 的现有原则和实践提供了压力测试环境。疫情可能增加伴随事件（ICEs）和缺失数据的发生率，从而引发大量关于修订方案和统计分析计划以解决这些问题的讨论。本文重新审视了近期关于 estimands 和缺失数据处理的研究，特别是《ICH E9 (R1)：临床试验中的 estimands 和敏感性分析》。基于利用因果推断框架处理 ICEs 策略的深入讨论，我们提出了在 ICH E9 (R1) 中应用 estimand 和估计框架的一些改进建议。具体而言，我们讨论了根据不同 ICEs 原因进行差异化处理的混合策略。此外，我们建议主要采用假设性策略处理 ICEs，并针对不同类型的 ICEs 提供了不同假设性策略的示例，以及估计和敏感性分析的路线图。我们得出结论，所提出的框架有助于将临床目标转化为统计推断目标，并自动解决许多由疫情等事件引发的定义 estimands 和选择估计程序的问题。