Stabilizing Thompson Sampling with Null Hypothesis Bayesian Response-Adaptive Randomization

Abstract

Response-adaptive randomization (RAR) methods can be used to adapt randomization probabilities based on accumulating data, aiming to increase the probability of allocating patients to effective treatments. A popular RAR method is Thompson sampling, which randomizes patients proportionally to the Bayesian posterior probability that each treatment is the most effective. However, its high variability can also increase the risk of assigning patients to inferior treatments and lead to inferential problems such as confidence interval undercoverage. We propose a principled method based on Bayesian hypothesis testing to address these issues: We introduce a null hypothesis postulating equal effectiveness of treatments. Bayesian model averaging then induces shrinkage toward equal randomization probabilities, with the degree of shrinkage controlled by the prior probability of the null hypothesis. Equal randomization and Thompson sampling arise as special cases when the prior probability is one or zero, respectively. A simulation study demonstrates that the method can mitigate issues with Thompson sampling and has comparable statistical properties to Thompson sampling with common ad hoc modifications such as power transformation and probability capping. Under the null hypothesis and a normal model, the randomization probabilities are shown to converge asymptotically to equal randomization, unlike those of Thompson sampling. We implement the method in the free and open-source R package brar, enabling experimenters to easily perform null hypothesis Bayesian RAR and support more effective randomization of patients.