In randomized clinical trials with baseline variables that are prognostic for the primary outcome, there is potential to improve precision and reduce sample size by appropriately adjusting for these variables. A major challenge is that there are multiple statistical methods to adjust for baseline variables but little guidance on which is best to use in a given context.
The choice of method can have important consequences. For example, one commonly used method leads to uninterpretable estimates if there is any treatment effect heterogeneity, which would jeopardize the validity of trial conclusions.
In this module, we give practical guidance on how to avoid this problem while retaining the advantages of covariate adjustment. We discuss relevant statistical methods (which apply to continuous, binary, and time-to-event outcomes) and give software implementing them. Data examples from stroke and Alzheimer's disease trials are used to illustrate these methods.