Integrating tumor burden with survival outcome for treatment effect evaluation in oncology trials

TL;DR

This paper introduces a Bayesian framework that combines tumor burden and survival data to better evaluate treatment effects in early-phase oncology trials with limited data.

Contribution

It presents a novel joint modeling approach and estimand that improve treatment effect assessment in data-scarce early-phase cancer trials.

Findings

Controlled Type I error rates in simulations

Effective integration of tumor burden and survival data

Potential as a new endpoint for Phase 3 trials

Abstract

In early-phase cancer clinical trials, the limited availability of data presents significant challenges in developing a framework to efficiently quantify treatment effectiveness. To address this, we propose a novel utility-based Bayesian approach for assessing treatment effects in these trials, where data scarcity is a major concern. Our approach synthesizes tumor burden, a key biomarker for evaluating patient response to oncology treatments, and survival outcome, a widely used endpoint for assessing clinical benefits, by jointly modeling longitudinal and survival data. The proposed method, along with its novel estimand, aims to efficiently capture signals of treatment efficacy in early-phase studies and holds potential for development as an endpoint in Phase 3 confirmatory studies. We conduct simulations to investigate the frequentist characteristics of the proposed estimand in a simple setting, which demonstrate relatively controlled Type I error rates when testing the treatment effect on outcomes.