The validation of surrogate endpoints in meta-analyses of randomized experiments

TLDR

The validation of surrogate endpoints has been studied since Prentice (1989), with criteria that apply only to binary endpoints, and subsequent work added a proportion explained metric and later replaced it with a relative effect measure and an individual‑level agreement metric to extend validation to multi‑trial settings. This paper proposes a new method for validating surrogate endpoints and for predicting the true treatment effect from the observed surrogate effect. The method is illustrated using data from two multicenter trial sets, one comparing chemotherapy regimens for advanced ovarian cancer and another comparing interferon‑α with placebo for age‑related macular degeneration. Freedman et al.

Abstract

The validation of surrogate endpoints has been studied by Prentice (1989). He presented a definition as well as a set of criteria, which are equivalent only if the surrogate and true endpoints are binary. Freedman et al. (1992) supplemented these criteria with the so-called 'proportion explained'. Buyse and Molenberghs (1998) proposed replacing the proportion explained by two quantities: (1) the relative effect linking the effect of treatment on both endpoints and (2) an individual-level measure of agreement between both endpoints. The latter quantity carries over when data are available on several randomized trials, while the former can be extended to be a trial-level measure of agreement between the effects of treatment of both endpoints. This approach suggests a new method for the validation of surrogate endpoints, and naturally leads to the prediction of the effect of treatment upon the true endpoint, given its observed effect upon the surrogate endpoint. These ideas are illustrated using data from two sets of multicenter trials: one comparing chemotherapy regimens for patients with advanced ovarian cancer, the other comparing interferon-alpha with placebo for patients with age-related macular degeneration.