论文标题
ICH E9（R1）的实施：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
论文作者
论文摘要
当前的COVID-19大流行对正在进行的临床试验构成了许多挑战，并为临床试验中的现有原理和估算实践提供了压力测试环境。大流行可能会提高事件发生率（ICE）和缺失价值的速度，从而激发了有关修改协议和统计分析计划的大量讨论，以解决这些问题。在本文中，我们对临床试验中的估计值和敏感性分析进行了有关估计和处理估计值的最新研究，尤其是ICH E9（R1）。基于对使用因果推理框架处理ICE的策略的深入讨论，我们建议在应用ICH E9（R1）中应用估算和估计框架方面进行一些改进。具体而言，我们讨论了策略的混合，使我们可以根据ICE的原因来差异化冰。我们还建议应该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灵敏度分析的路线图。我们得出的结论是，所提出的框架有助于简化将临床目标转化为统计推断目标，并自动解决许多问题，并通过定义估计和选择估计程序，例如大流行等事件。
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.
