A Bayesian adaptive design for dual‐agent phase I–II oncology trials integrating efficacy data across stages

Abstract

AbstractCombination of several anticancer treatments has typically been presumed to have enhanced drug activity. Motivated by a real clinical trial, this paper considers phase I–II dose finding designs for dual‐agent combinations, where one main objective is to characterize both the toxicity and efficacy profiles. We propose a two‐stage Bayesian adaptive design that accommodates a change of patient population in‐between. In stage I, we estimate a maximum tolerated dose combination using the escalation with overdose control (EWOC) principle. This is followed by a stage II, conducted in a new yet relevant patient population, to find the most efficacious dose combination. We implement a robust Bayesian hierarchical random‐effects model to allow sharing of information on the efficacy across stages, assuming that the related parameters are either exchangeable or nonexchangeable. Under the assumption of exchangeability, a random‐effects distribution is specified for the main effects parameters to capture uncertainty about the between‐stage differences. The inclusion of nonexchangeability assumption further enables that the stage‐specific efficacy parameters have their own priors. The proposed methodology is assessed with an extensive simulation study. Our results suggest a general improvement of the operating characteristics for the efficacy assessment, under a conservative assumption about the exchangeability of the parameters a priori.