A Bayesian decision-theoretic sequential response-adaptive randomization design.

We propose a class of phase II clinical trial designs with sequential stopping and adaptive treatment allocation to evaluate treatment efficacy. Our work is based on two-arm (control and experimental treatment) designs with binary endpoints. Our overall goal is to construct more efficient and ethical randomized phase II trials by reducing the average sample sizes and increasing the percentage of patients assigned to the better treatment arms of the trials. The designs combine the Bayesian decision-theoretic sequential approach with adaptive randomization procedures in order to achieve simultaneous goals of improved efficiency and ethics. The design parameters represent the costs of different decisions, for example, the decisions for stopping or continuing the trials. The parameters enable us to incorporate the actual costs of the decisions in practice. The proposed designs allow the clinical trials to stop early for either efficacy or futility. Furthermore, the designs assign more patients to better treatment arms by applying adaptive randomization procedures. We develop an algorithm based on the constrained backward induction and forward simulation to implement the designs. The algorithm overcomes the computational difficulty of the backward induction method, thereby making our approach practicable. The designs result in trials with desirable operating characteristics under the simulated settings. Moreover, the designs are robust with respect to the response rate of the control group.