Bayesian Response Adaptive Randomization for Randomized Clinical Trials With Continuous Outcomes: The Role of Covariate Adjustment.

Abstract

Study designs incorporate interim analyses to allow for modifications to the trial design. These analyses may aid decisions regarding sample size, futility, and safety. Furthermore, they may provide evidence about potential differences between treatment arms. Bayesian response adaptive randomization (RAR) skews allocation proportions such that fewer participants are assigned to the inferior treatments. However, these allocation changes may introduce covariate imbalances. We discuss two versions of Bayesian RAR (with and without covariate adjustment for a binary covariate) for continuous outcomes analyzed using change scores and repeated measures, while considering either regression or mixed models for interim analysis modeling. Through simulation studies, we show that RAR (both versions) allocates more participants to better treatments compared to equal randomization, while reducing potential covariate imbalances. We also show that dynamic allocation using mixed models for repeated measures yields a smaller allocation proportion variance while having a similar covariate imbalance as regression models. Additionally, covariate imbalance was smallest for methods using covariate-adjusted RAR (CARA) in scenarios with small sample sizes and covariate prevalence less than 0.3. Covariate imbalance did not differ between RAR and CARA in simulations with larger sample sizes and higher covariate prevalence. We thus recommend a CARA approach for small pilot/exploratory studies for the identification of candidate treatments for further confirmatory studies.