Covariate Adjustment in Bayesian Adaptive Clinical Trials

In conventional randomized controlled trials, adjustment for baseline values of covariates known to be associated with the outcome ("covariate adjustment") increases the power of the trial. Recent work has shown similar results hold for more flexible frequentist designs, such as information adaptive and adaptive multi-arm designs. However, covariate adjustment has not been characterized within the more flexible Bayesian adaptive designs, despite their growing popularity. We focus on a subclass of these which allow for early stopping at an interim analysis given evidence of treatment superiority. We consider both collapsible and non-collapsible estimands, and show how to marginalize posterior samples of conditional estimands. We describe several estimands for three common outcome types (continuous, binary, time-to-event). We perform a simulation study to assess the impact of covariate adjustment using a variety of adjustment models in several different scenarios. This is followed by a real world application of the compared approaches to a COVID-19 trial with a binary endpoint. For all scenarios, it is shown that covariate adjustment increases power and the probability of stopping the trials early, and decreases the expected sample sizes as compared to unadjusted analyses.