Evaluation of Time-to-event Surrogate Endpoint Endpoint ─Meta-analytic Approach─

Abstract

Cancer clinical trials often evaluate overall survival (OS) as the primary endpoint for measuring clinically important effects for treatments. However, observing OS may require considerable time and cost for patient follow-up. Thus, surrogate endpoints were widely employed for OS to accelerate trials, facilitating the introduction of potentially beneficial treatments. For any surrogate endpoint to be valid, it must be tightly correlated to OS, and the treatments effects on OS must be predictable by those on the surrogate. This article reviews meta-analytic approaches (based on a set of randomized controlled trials) for statistically validating a surrogate endpoint for OS based on two criteria (individual-level surrogacy and trial-level surrogacy). We review the traditional two-step approach via copula models, one-step approaches via frailty models, and approaches with frailty-copula models. We illustrate these approaches using the gastric cancer dataset from the GASTRIC group.