Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Binary, Ordinal, and Time-to-Event Outcomes

• Activity Number: 194 - Topics in Clinical Trials - I
• Type: Contributed
• Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Biopharmaceutical Section
• Author(s): David Benkeser and Ivan Diaz and Alex Luedtke and Jodi Segal and Daniel O Scharfstein and Michael Rosenblum*
• Companies: Emory University and Weill Cornell Medicine and University of Washington & Fred Hutchinson Cancer Research Center and Johns Hopkins University and Johns Hopkins University and Johns Hopkins Univ, Bloomberg School of Public Health
• Keywords: adaptive design

Abstract

There are currently over 400 clinical trials (phase 2 and 3) of treatments for COVID-19 registered on clinicaltrials.gov. Covariate adjustment is a statistical analysis method with potential to improve precision and reduce the required sample size for a substantial number of these trials. Though covariate adjustment is recommended by the FDA and the EMA, it is underutilized, especially for the types of outcomes (binary, ordinal and time-to-event) that are common in COVID-19 trials. To demonstrate the potential value added by covariate adjustment in this context, we simulated two-arm, randomized trials comparing a hypothetical COVID-19 treatment versus standard of care, where the primary outcome is binary, ordinal, or time-to-event. Our simulated distributions are derived from two sources: patients hospitalized at New York Presbyterian Hospital and a CDC description of 2449 cases. We found precision gains from using covariate adjustment equivalent to 9-21% reductions in the required sample size to achieve a desired power--for a variety of estimands when sample size was at least 200. We provide an R package and practical recommendations. https://doi.org/10.1101/2020.04.19.20069922