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

TL;DR

This paper introduces a Bayesian two-stage adaptive design for dual-agent oncology trials that effectively integrates efficacy data across stages, improving dose finding and treatment assessment.

Contribution

It develops a novel Bayesian hierarchical model allowing information sharing across stages with exchangeability assumptions, enhancing dual-agent dose-finding strategies.

Findings

Improved efficacy assessment in simulations

Effective dose combination identification

Enhanced operating characteristics

Abstract

Combination of several anti-cancer 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 \textit{a priori}