Sample Sizes for RCTs using Bayesian Response Adaptive Randomization

Music City Center 

We describe a Bayesian approach for sample size estimation for multi-arm randomized controlled trials with response adaptive randomization (RAR). Assuming normally distributed treatment effects and unknown but common variance, this design utilizes outcome data to estimate posterior distributions of parameters, modifies allocation to favor effective treatments, and re-estimates the number of participants. The sample size should be sufficient to show that at least one group difference is greater than 0 (success), or that all effect sizes are smaller than a desired threshold (futility) at prespecified thresholds. Using simulations, sample sizes are calculated using a Bayesian approach: [1] without interim analysis; [2] with interim analyses and with and without RAR; [3] based on hypothesis testing. We show that two interim analyses, conducted when outcomes are available among 25% and 50% of participants, could result in fewer participants with slightly higher number needed when incorporating RAR. The ethical benefits of allocating more patients to favorable arms with larger sample size requirements should be considered against the efficiency of equal group allocation in RAR trials.

Adaptive Designs

Power and Sample Size

Trial Design





Response adaptive randomization 

Biopharmaceutical Section