Cancer phase I trial design using drug combinations when a fraction of dose limiting toxicities is attributable to one or more agents

TL;DR

This paper introduces a Bayesian adaptive design for phase I cancer trials involving drug combinations, accounting for toxicity attributions to improve dose escalation decisions.

Contribution

It develops a novel Bayesian method using Copula regression to model toxicity attributions and estimate the MTD curve in drug combination trials.

Findings

The design performs well under correct and misspecified models.

It effectively estimates the MTD curve considering toxicity attributions.

The method can be extended to discrete dose combinations.

Abstract

Drug combination trials are increasingly common nowadays in clinical research. However, very few methods have been developed to consider toxicity attributions in the dose escalation process. We are motivated by a trial in which the clinician is able to identify certain toxicities that can be attributed to one of the agents. We present a Bayesian adaptive design in which toxicity attributions are modeled via Copula regression and the maximum tolerated dose (MTD) curve is estimated as a function of model parameters. The dose escalation algorithm uses cohorts of two patients, following the continual reassessment method (CRM) scheme, where at each stage of the trial, we search for the dose of one agent given the current dose of the other agent. The performance of the design is studied by evaluating its operating characteristics when the underlying model is either correctly specified or misspecified. We show that this method can be extended to accommodate discrete dose combinations.