Optimal Sequential Predictive Probability Designs for Early-Phase Oncology Expansion Cohorts

PURPOSE The customary approach to early-phase clinical trial design, where the focus is on identification of the maximum tolerated dose, is not always suitable for noncytotoxic or other targeted therapies. Many trials have continued to follow the 3 + 3 dose-escalation design, but with the addition of phase I dose-expansion cohorts to further characterize safety and assess efficacy. Dose-expansion cohorts are not always planned in advance nor rigorously designed. We introduce an approach to the design of phase I expansion cohorts on the basis of sequential predictive probability monitoring. METHODS Two optimization criteria are proposed that allow investigators to stop for futility to preserve limited resources while maintaining traditional control of type I and type II errors. We demonstrate the use of these designs through simulation, and we elucidate their implementation with a redesign of the phase I expansion cohort for atezolizumab in metastatic urothelial carcinoma. RESULTS A sequential predictive probability design outperforms Simon's two-stage designs and posterior probability monitoring with respect to both proposed optimization criteria. The Bayesian sequential predictive