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

2026-03-19

AI Summary

Binary endpoints are frequently employed as surrogate markers for time-to-event outcomes, yet the validity of their association with true endpoints—both at the individual and trial levels—across varying trial designs remains insufficiently evaluated. This study presents the first comprehensive assessment of the performance of these two types of surrogacy measures under diverse design configurations, leveraging a meta-analytic framework informed by large-scale simulations and real-world clinical trial data. The findings elucidate how key design parameters influence the accuracy of surrogacy estimation, thereby offering empirical evidence and methodological guidance to inform regulatory decisions regarding the appropriate use of binary surrogate endpoints in clinical trials.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Metareasoning and Metaheuristics
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

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.

Problem

• surrogate endpoint
• binary endpoint
• time-to-event endpoint
• association metrics
• validation

Innovation

• surrogate endpoint
• binary endpoint
• time-to-event
• individual-level association
• trial-level association