论文标题

估计数及其估算受到COVID-19大流行影响的临床试验的估计:NISS Ingram Olkin论坛系列的报告未计划的临床试验中断

Estimands and their Estimators for Clinical Trials Impacted by the COVID-19 Pandemic: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

论文作者

Van Lancker, Kelly, Tarima, Sergey, Bartlett, Jonathan, Bauer, Madeline, Bharani-Dharan, Bharani, Bretz, Frank, Flournoy, Nancy, Michiels, Hege, Parra, Camila Olarte, Rosenberger, James L, Cro, Suzie

论文摘要

COVID-19大流行在全球范围内继续影响临床试验的行为。与大流行有关的操作挑战可能引起并发症,例如现场关闭,旅行限制和对研究产品的供应链中断或与健康相关的挑战(例如COVID-19的感染)。这些并发症中的某些并发症导致了不可预见的事件,因为它们会影响与临床问题有关的解释或存在的测量结果。在本文中,我们演示了ICH E9(R1)对估计和灵敏度分析的附录如何为讨论潜在的大流行有关的试验中断提供了严格的基础,并将这些干扰嵌入研究目标和设计元素的背景下。我们介绍了几种假设的估计和策略,并回顾了各种因果推理和缺少数据方法,以及一种统计方法,将无偏见的估计量结合在一起以进行估计。为了说明,我们描述了风格化试验的特征,以及它可能受到大流行的影响。然后,将通过讨论估算和估算值的变化来考虑大流行中断,从而重新访问该程式化的试验。最后,我们概述了在不可预见的中断的背景下设计未来试验的考虑。

The COVID-19 pandemic continues to affect the conduct of clinical trials globally. Complications may arise from pandemic-related operational challenges such as site closures, travel limitations and interruptions to the supply chain for the investigational product, or from health-related challenges such as COVID-19 infections. Some of these complications lead to unforeseen intercurrent events in the sense that they affect either the interpretation or the existence of the measurements associated with the clinical question of interest. In this article, we demonstrate how the ICH E9(R1) Addendum on estimands and sensitivity analyses provides a rigorous basis to discuss potential pandemic-related trial disruptions and to embed these disruptions in the context of study objectives and design elements. We introduce several hypothetical estimand strategies and review various causal inference and missing data methods, as well as a statistical method that combines unbiased and possibly biased estimators for estimation. To illustrate, we describe the features of a stylized trial, and how it may have been impacted by the pandemic. This stylized trial will then be re-visited by discussing the changes to the estimand and the estimator to account for pandemic disruptions. Finally, we outline considerations for designing future trials in the context of unforeseen disruptions.

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