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Revisiting ICH E9 (R1) During the COVID-19 Pandemic
Friday, January 22, 2021
10 a.m. to 12 p.m. ET
Yongming Qu, Eli Lilly
Yongming Qu is currently a Sr. Research Fellow at Eli Lilly and Company. He received his PhD in Statistics from Iowa State University. He has provided key leadership in various stages drug clinical development at Lilly. He has been passionate in developing new statistical methods for better clinical trial design and data analysis that impact drug development. He published more than 70 articles in statistical, medical and mathematical journals, and is an ASA Fellow.
Ilya Lipkovich, Eli Lilly
Ilya Lipkovich is a Sr. Research Advisor at Eli Lilly and Company. Ilya received his PhD in Statistics from Virginia Tech in 2002 and has more than 15 years of statistical consulting experience in pharmaceutical industry. He is an ASA Fellow and published on subgroup identification in clinical data, analysis with missing data, and causal inference. He is a frequent presenter at conferences, a co-developer of subgroup identification methods, and a co-author of the books "Analyzing Longitudinal Clinical Trial Data. A Practical Guide" and "Estimands, Estimators and Sensitivity Analysis in Clinical Trials."
Abstract 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 the causes of 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 resolves many issues with defining estimands and choosing estimation procedures arising from unanticipated events such as the current pandemic.