Surrogate endpoint metaregression: useful statistics for regulators and trialists

Abstract

Objectives

The main purpose of using a surrogate endpoint is to estimate the treatment effect on the true endpoint sooner than with a true endpoint. Based on a metaregression of historical randomized trials with surrogate and true endpoints, we discuss statistics for applying and evaluating surrogate endpoints.

Methods

We computed statistics from 2 types of linear metaregressions for trial-level data: simple random effects and novel random effects with correlations among estimated treatment effects in trials with more than 2 arms. A key statistic is the estimated intercept of the metaregression line. An intercept that is small or not statistically significant increases confidence when extrapolating to a new treatment because of consistency with a single causal pathway and invariance to labeling of treatments as controls. For a regulator applying the metaregression to a new treatment, a useful statistic is the 95% prediction interval. For a clinical trialist planning a trial of a new treatment, useful statistics are the surrogate threshold effect proportion, the sample size multiplier adjusted for dropouts, and the novel true endpoint advantage.

Results

We illustrate these statistics with surrogate endpoint metaregressions involving antihypertension treatment, breast cancer screening, and colorectal cancer treatment.

Conclusion

Regulators and trialists should consider using these statistics when applying and evaluating surrogate endpoints.