Modeling synergism in early phase cancer trials with drug combination with continuous dose levels: is there an added value?

TL;DR

This study evaluates whether including an interaction term for synergism in continuous dose models improves safety and accuracy in early phase cancer trials, challenging prior claims that it adds no value.

Contribution

The paper demonstrates that incorporating an interaction term enhances trial safety and the reliability of the estimated MTD curve in continuous dose settings.

Findings

Including an interaction term improves trial safety.

Models with interaction better estimate the MTD curve.

Ignoring interaction can compromise trial outcomes.

Abstract

In parametric Bayesian designs of early phase cancer clinical trials with drug combinations exploring a discrete set of partially ordered doses, several authors claimed that there is no added value in including an interaction term to model synergism between the two drugs. In this paper, we investigate these claims in the setting of continuous dose levels of the two agents. Parametric models will be used to describe the relationship between the doses of the two agents and the probability of dose limiting toxicity and efficacy. Trial design proceeds by treating cohorts of two patients simultaneously receiving different dose combinations and response adaptive randomization. We compare trial safety and efficiency of the estimated maximum tolerated dose (MTD) curve between models that include an interaction term with models without the synergism parameter with extensive simulations. Under a selected class of dose-toxicity models and dose escalation algorithm, we found that not including an interaction term in the model can compromise the safety of the trial and reduce the pointwise reliability of the estimated MTD curve.