Metrics to find a surrogate endpoint of OS in metastatic oncology trials: a simulation study

TL;DR

This study uses simulations to evaluate how well various patient-level metrics predict the trial-level association of surrogate endpoints with overall survival in metastatic cancer trials, highlighting limitations in current metrics.

Contribution

It introduces a simulation framework to assess the predictive power of patient-level metrics for surrogate endpoints in oncology trials, revealing diminishing returns with added biological factors.

Findings

Patient and trial level metrics show tight correlations.

Associations decrease as biological impact parameter {} increases.

Adding more biological factors yields limited improvement.

Abstract

Surrogate endpoint (SE) for overall survival in cancer patients is essential to improving the efficiency of oncology drug development. In practice, we may discover a new patient level association with survival, based on one or more clinical or biological features, in a discovery cohort; and then measure the trial level association across studies in a meta-analysis to validate the SE. To understand how well various patient level metrics would indicate the eventual trial level association, we considered causal biological trajectories based on bi-exponential functions, modeled the strength of their impact on survival hazards via a parameter {\alpha}, and simulated the trajectories and survival times in randomized trials simultaneously. We set an early time point in the trials when the trajectory measurement became the SE value. From simulated discovery cohorts, we compared patient level metrics including C index, integrated brier score, and log hazard ratio between SE values and survival times. We assembled multiple simulated studies to enable meta-analyses to estimate the trial level association. Across all the simulation scenarios considered here, we found tight correlations among the three patient level metrics and similar correlations between any of them and the trial level metric. Despite the continual increase in {\alpha}, both patient and trial level metrics often plateaued together; their association always decreased quickly as {\alpha} increased. This suggests that incorporating additional biological factors into a composite SE is likely to have diminishing returns on improving both patient level and trial level association.