In May 2021, the U.S. Food and Drug Administration (FDA) released a revised draft guidance for industry on “Adjustment for Covariates in Randomized Clinical Trials for Drugs and Biological Products”. Covariate adjustment is a statistical analysis method for improving precision in clinical trials by adjusting for pre-specified, prognostic baseline variables (e.g., age, BMI, comorbidities). The resulting sample size reductions can lead to substantial cost savings, and also more ethical trials since they avoid exposing more participants than necessary to experimental treatments. Though covariate adjustment is recommended by the FDA and the European Medicines Agency, many trials do not fully exploit the available information in baseline variables.

In Part 1, we explain what covariate adjustment is, how it works, when it may be useful, and how to implement it (in a preplanned way that is robust to model misspecification) for a variety of scenarios.

In Part 2, we present a new method that enables us to easily combine covariate adjustment with group sequential designs. This approach can lead to faster trials, without sacrificing validity or power, even when the experimental treatment is ineffective.

In Part 3, we show the impact of covariate adjustment using completed trial datasets in multiple disease areas. We provide step-by-step, clear documentation of how to apply the software in each setting. Participants will have the time to apply software tools on the different datasets.