Statistical Challenges in COVID-19 Clinical Research

Andrea S. Foulkes, ScD
Chief of Biostatistics, Massachusetts General Hospital
Professor of Medicine (Biostatistics), Harvard Medical School
Professor in the Department of Biostatistics, Harvard T.H. Chan School of Public Health

Abstract The rapid collection of large-scale observational and clinical trials data on individuals with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has generated enormous opportunity for novel discovery and improved clinical decision-making tools that could lead to improved Coronavirus Disease (COVID-19) outcomes. At the same time, the statistical challenges inherent in leveraging these novel data resources are numerous. In this talk, I highlight several of these challenges and emphasize the importance of applying rigorous statistical methods that are specifically designed to account for the data generating process and to address the clinical hypothesis under investigation. Examples are based on data arising from the Massachusetts General Hospital (MGH) COVID-19 Patient Registry, a comprehensive resource including extensive manually extracted and electronic health record data on all patients hospitalized at MGH during the first surge.

Required Reading
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