A Bayesian seamless phase I-II trial design with two stages for cancer clinical trials with drug combinations

TL;DR

This paper introduces a Bayesian two-stage design for cancer drug combination trials that adaptively identifies optimal dose combinations considering toxicity and efficacy, improving early phase trial decision-making.

Contribution

It proposes a novel Bayesian two-stage design integrating toxicity and efficacy data for better dose selection in cancer drug combination trials.

Findings

Effective dose combination identification through simulation

Improved patient allocation to promising doses

Validated with real trial example

Abstract

The use of drug combinations in clinical trials is increasingly common during the last years since a more favorable therapeutic response may be obtained by combining drugs. In phase I clinical trials, most of the existing methodology recommends a one unique dose combination as "optimal", which may result in a subsequent failed phase II clinical trial since other dose combinations may present higher treatment efficacy for the same level of toxicity. We are particularly interested in the setting where it is necessary to wait a few cycles of therapy to observe an efficacy outcome and the phase I and II population of patients are different with respect to treatment efficacy. Under these circumstances, it is common practice to implement two-stage designs where a set of maximum tolerated dose combinations is selected in a first stage, and then studied in a second stage for treatment efficacy. In this article we present a new two-stage design for early phase clinical trials with drug combinations. In the first stage, binary toxicity data is used to guide the dose escalation and set the maximum tolerated dose combinations. In the second stage, we take the set of maximum tolerated dose combinations recommended from the first stage, which remains fixed along the entire second stage, and through adaptive randomization, we allocate subsequent cohorts of patients in dose combinations that are likely to have high posterior median time to progression. The methodology is assessed with extensive simulations and exemplified with a real trial.