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Activity Number:
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375
- Causal Estimand in Clinical Trials
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract #322201
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Title:
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Estimands and Their Estimators for Clinical Trials Impacted by the COVID-19 Pandemic
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Author(s):
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Kelly Van Lancker* and Sergey Tarima and Jonathan Bartlett and Madeline Bauer and Bharani Bharani-Dharan and Frank Bretz and Nancy Flournoy and Hege Michiels and Camila Olarte Parra and James Landis Rosenberger and Suzie Cro
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Companies:
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Johns Hopkins University, Bloomberg School of Public Health, US and Ghent University, Belgium and Division of Biostatistics, Medical College of Wisconsin and University of Bath and Division of Infectious Diseases, Keck School of Medicine, University of Southern Californi and Novartis Pharmaceuticals and Novartis and University of Missouri and Ghent University and University of Bath and Penn State and NISS and Imperial Clinical Trials Unit, Imperial College London
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Keywords:
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Estimands;
Covid-19;
Missing data;
Intercurrent events
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Abstract:
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The COVID-19 pandemic continues to affect the conduct of clinical trials of medical products globally. Complications may arise from pandemic-related operational challenges such as site closures, travel limitations and interruptions to the supply chain, or from health-related challenges such as COVID-19 infected trial participants. 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. 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 an unbiased and a possibly biased estimator for hypothesis testing and estimation. Finally, we outline different considerations for designing future trials in the context of any unforeseen disruptions (e.g., emergence of a new pandemic).
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Authors who are presenting talks have a * after their name.
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