期刊
STATISTICS IN BIOPHARMACEUTICAL RESEARCH
卷 -, 期 -, 页码 -出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/19466315.2022.2094459
关键词
Causal inference; Hypothetical estimands; Intercurrent events; Missing data; Treatment policy
资金
- Fulbright Belgium, Belgian American Educational Foundation
- VLAIO (Flemish Innovation and Entrepreneurship) [HBC.2017.0219]
- Department of Health and Human Services of the National Institutes of Health [R40MC41748]
- UK Medical Research Council [MR/T023953/1]
- VLAIO under Baekeland grant [HBC.2019.2155]
- NIHR advanced research fellowship [300593]
This article discusses how to address trial disruptions caused by the COVID-19 pandemic and proposes strategies and methods to deal with them. By introducing the concepts of estimands and sensitivity analyses, the impact of pandemic-related interferences on trial results can be better understood, and considerations for future trial designs can be provided.
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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