A simple meta-analytic approach for using a binary surrogate endpoint to predict the effect of intervention on true endpoint

Abstract

A surrogate endpoint is an endpoint that is obtained sooner, at lower cost, or less invasively than the true endpoint for a health outcome and is used to make conclusions about the effect of intervention on the true endpoint. In this approach, each previous trial with surrogate and true endpoints contributes an estimated predicted effect of intervention on true endpoint in the trial of interest based on the surrogate endpoint in the trial of interest. These predicted quantities are combined in a simple random-effects meta-analysis to estimate the predicted effect of intervention on true endpoint in the trial of interest. Validation involves comparing the average prediction error of the aforementioned approach with (i) the average prediction error of a standard meta-analysis using only true endpoints in the other trials and (ii) the average clinically meaningful difference in true endpoints implicit in the trials. Validation is illustrated using data from multiple randomized trials of patients with advanced colorectal cancer in which the surrogate endpoint was tumor response and the true endpoint was median survival time.