Registration for this module closes July 7. 

Live sessions will be held 8:30 a.m. - noon Pacific (11:30-3 p.m. Eastern).

In randomized clinical trials with baseline variables that are correlated with the outcome, there is potential to improve precision and reduce the required sample size by appropriately adjusting for these variables in the statistical analysis (called covariate adjustment). The resulting sample size reductions can lead to substantial cost savings, and also can lead to more ethical trials since they avoid exposing more participants than necessary to experimental treatments. Despite regulators such as the U.S. Food and Drug Administration and the European Medicines Agency recommending covariate adjustment, it remains underutilized leading to inefficient trials in many disease areas. This is especially true for trials with binary, ordinal, and time-to-event outcomes, which are quite common. In this module, we explain what covariate adjustment is, how it works, when it may be useful to apply, and how to implement it (in a preplanned way that is robust to model misspecification) for a variety of scenarios. We demonstrate the impact of covariate adjustment using completed trial data sets in multiple disease areas.