Evaluation of Individual and Trial Level Association Metrics in the Validation of a Binary Surrogate Endpoint for a True Time-to-Event Endpoint

TL;DR

This paper systematically evaluates how well individual and trial-level association metrics validate binary surrogate endpoints for time-to-event outcomes, using simulations and clinical data to inform regulatory decisions.

Contribution

It provides a comprehensive simulation-based assessment of association metrics' performance in surrogate validation for binary endpoints.

Findings

Trial-level association estimates vary with trial design.

Individual-level association metrics show consistent performance.

Simulation results inform best practices for surrogate validation.

Abstract

Candidate binary endpoints are often considered as surrogates for time-to-event (TTE) clinical endpoints, primarily because they can be assessed at earlier time points. To be submitted for regulatory approval candidate binary endpoints need to validated. The most well-known method for performing such validation employs a meta-analytic framework to estimate individual-level and trial-level association. However, the performance of these association estimates in the context of a binary surrogate has not yet been examined through a comprehensive simulation study. This research aims to systematically investigate the performance of association estimates at the trial-level and at the individual-level under various trial design choices, using both simulation studies and clinical trial data, where available.