A Nonparametric Bayesian Design for Drug Combination Cancer Trials

TL;DR

This paper introduces a nonparametric Bayesian adaptive design for early phase drug combination trials, aiming to accurately estimate the maximum tolerated dose (MTD) using a novel probabilistic model and dose allocation scheme.

Contribution

It presents a new nonparametric Bayesian model with a modified Continual Reassessment scheme for dose finding in cancer trials, improving upon existing methods.

Findings

Method performs comparably with existing approaches.

Application to a phase I trial demonstrates practical utility.

Uses Gibbs sampling with a weighting mechanism for probability updates.

Abstract

We propose an adaptive design for early phase drug combination cancer trials with the goal of estimating the maximum tolerated dose (MTD). A nonparametric Bayesian model, using beta priors truncated to the set of partially ordered dose combinations, is used to describe the probability of dose limiting toxicity (DLT). Dose allocation between successive cohorts of patients is estimated using a modified Continual Reassessment scheme. The updated probabilities of DLT are calculated with a Gibbs sampler that employs a weighting mechanism to calibrate the influence of data versus the prior. At the end of the trial, we recommend one or more dose combinations as the MTD based on our proposed algorithm. The design operating characteristics indicate that our method is comparable with existing methods. As an illustration, we apply our method to a phase I clinical trial of CB-839 and Gemcitabine.