Comparing Biomarkers as Trial Level General Surrogates

TL;DR

This paper introduces a Bayesian non-parametric method with cross-validation to evaluate and compare trial level general surrogates, aiding in predicting clinical efficacy efficiently.

Contribution

It develops a novel approach for comparing candidate trial level surrogates using Bayesian modeling and cross-validation, addressing a gap in existing surrogate evaluation methods.

Findings

Identified two immune measures as potential surrogates.

Successfully predicted efficacy in a trial without clinical outcomes.

Method performs well across various simulated scenarios.

Abstract

An intermediate response measure that accurately predicts efficacy in a new setting can reduce trial cost and time to product licensure. In this paper, we define a trial level general surrogate as a trial level intermediate response that accurately predicts trial level clinical responses. Methods for evaluating trial level general surrogates have been developed previously. Many methods in the literature use trial level intermediate responses for prediction. However, all existing methods focus on surrogate evaluation and prediction in new settings, rather than comparison of candidate trial level surrogates, and few formalize the use of cross validation to quantify the expected prediction error. Our proposed method uses Bayesian non-parametric modeling and cross-validation to estimate the absolute prediction error for use in evaluating and comparing candidate trial level general surrogates. Simulations show that our method performs well across a variety of scenarios. We use our method to evaluate and to compare candidate trial level general surrogates in several multi-national trials of a pentavalent rotavirus vaccine. We identify two immune measures that have potential value as trial level general surrogates and use the measures to predict efficacy in a trial with no clinical outcomes measured.