Adaptive Seamless Dose-Finding Trials

Problem definition: We study early-stage dose-finding clinical trials with simultaneous consideration of efficacy and toxicity without parametric assumptions on the forms of the unknown dose-efficacy and dose-toxicity curves. We propose algorithms that adaptively allocate doses based on patient responses, in order to maximize the efficacy for the patients during the trial while minimizing the toxicity. Methodology/results: We leverage online learning to design the clinical trial and propose two algorithms. The first one follows dose-escalation principles and analyzes the efficacy and toxicity simultaneously. The second one uses bisection search to identify a safe dose range and then applies upper confidence bound algorithms within the safe range to identify efficacious doses. We show the matching upper and lower bounds for the regret of both algorithms. We find that observing the dose-escalation principle is costly, as the optimal regret of the first algorithm is in the order of N3=4, worse than the optimal regret of the second algorithm, which is in the order of N2=3. We test our proposed algorithms with three benchmarks commonly used in practice on synthetic and real data sets, and the results show that they are competitive with or significantly outperform the benchmarks. Managerial implications: We provide a novel insight that following the dose-escalation principle inevitably leads to higher regret. The first proposed algorithm is suitable to use when little information about the dose-toxicity profile is available, whereas the second one is appealing when more information is available about the toxicity profile.