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

• Published in: Statistics in biopharmaceutical research Vol. 15; no. 1; pp. 94 - 111
• Main Authors: Van Lancker, Kelly, Tarima, Sergey, Bartlett, Jonathan, Bauer, Madeline, Bharani-Dharan, Bharani, Bretz, Frank, Flournoy, Nancy, Michiels, Hege, Olarte Parra, Camila, Rosenberger, James L., Cro, Suzie
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 02.01.2023
• Subjects: Causal inference; Hypothetical estimands; Intercurrent events; Missing data; Treatment policy
• ISSN: 1946-6315; 1946-6315
• DOI: 10.1080/19466315.2022.2094459

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 revisited 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.