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

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.