Bbect: Bandit Based Ethical Clinical Trials

The aim of an Ethico-Optimal clinical trial is to randomly allocate the new drug (ND) and the standard of care (SOC) to patients in the sample, but with a greater fraction being administered ND if doing so is statistically justified. Such an adaptation is not possible in static trials, in which approximately half the patients would receive ND and the remaining patients SOC, despite evidence within the trial that ND is efficacious. We adapt a canonical stochastic multi-armed bandit algorithm named UCB1 to a clinical trials setting and analyse the resulting type-2 error (β), as also the minimum sample size required by such a trial for a certain β level. We also present simulations to establish that the ethical properties of such a trial are higher, both to verify our analysis and demonstrate an empirical advantage when compared to an existing method.