A Bayesian design for dual-agent dose optimization with targeted therapies

TL;DR

This paper introduces a Bayesian two-stage design for optimizing dose combinations of targeted therapies, balancing efficacy and safety, and demonstrating improved performance over existing methods.

Contribution

It presents a novel flexible cubic spline model within a Bayesian framework for dual-agent dose optimization, with adaptive randomization and overdose control.

Findings

Safer and more efficient dose identification compared to existing algorithms

Effective in various simulated scenarios, robust to sample size and model misspecification

Demonstrated utility in a case study with targeted cancer therapies

Abstract

In this article, we propose a phase I-II design in two stages for the combination of molecularly targeted therapies. The design is motivated by a published case study that combines a MEK and a PIK3CA inhibitors; a setting in which higher dose levels do not necessarily translate into higher efficacy responses. The goal is therefore to identify dose combination(s) with a prespecified desirable risk-benefit trade-off. We propose a flexible cubic spline to model the marginal distribution of the efficacy response. In stage I, patients are allocated following the escalation with overdose control (EWOC) principle whereas, in stage II, we adaptively randomize patients to the available experimental dose combinations based on the continuously updated model parameters. A simulation study is presented to assess the design's performance under different scenarios, as well as to evaluate its sensitivity to the sample size and to model misspecification. Compared to a recently published dose finding algorithm for biologic drugs, our design is safer and more efficient at identifying optimal dose combinations.