实施ICH（R1）：在COVID-19大流行期间学到的几点
Implementation of ICH E9 (R1): a few points learned during the COVID-19 pandemic
摘要：
当前的COVID-19大流行为正在进行的临床试验带来了众多挑战，并为临床试验中被估量的现有原则和实践提供了压力测试环境。大流行可能会增加并发事件（ICE）和缺失值的发生率，从而引发了大量关于修改方案和统计分析计划以解决这些问题的讨论。在本文中，我们回顾了最近的研究估计和处理的缺失值，特别是ICH E9（R1）的估计和敏感性分析在临床试验。基于对使用因果推断框架处理ICE的策略的深入讨论，我们建议在ICH E9（R1）中应用被估量和估计框架时进行一些改进。具体来说，我们讨论了一个混合的策略，使我们能够处理ICE差异的基础上ICE的原因。我们还建议应主要通过假设策略来处理ICE，并为不同类型的ICE提供不同假设策略的示例，以及估计和敏感性分析的路线图。我们的结论是，拟议的框架有助于简化转换临床目标的统计推断的目标，并自动解决了许多问题，定义被估量和选择估计程序所产生的事件，如流行病。
英文摘要：
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.
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