A Functional-Class Meta-Analytic Framework for Quantifying Surrogate Resilience

TL;DR

This paper introduces a functional class-based meta-analytic method to evaluate the resilience of surrogate markers against the surrogate paradox in clinical trials, addressing a key challenge in treatment effect assessment.

Contribution

It proposes a novel approach that estimates the probability of surrogate paradox in new studies without assuming direct transportability of functions.

Findings

Method performs well in simulation studies.

Applied to schizophrenia clinical trial data.

Provides a probabilistic measure of surrogate resilience.

Abstract

A surrogate marker is a biomarker or other physical measurement used to replace a primary outcome in clinical trials to evaluate a treatment effect when the primary outcome of interest is costly, invasive, or takes a long time to observe. However, replacing a primary outcome with a surrogate can lead to the "surrogate paradox," in which a treatment appears beneficial based on the surrogate but is actually harmful with respect to the primary outcome. In this paper, we propose a functional class-based method to assess resilience to the surrogate paradox in a meta-analytic setting. Our method leverages data from K completed studies in which the surrogate marker and primary outcome have been measured to make inference on a new study in which only the surrogate is measured. We do not assume direct transportability of the conditional mean function from the completed studies to the new study; instead, we consider deviations of functions from those observed in the completed studies to estimate the "resilience probability" i.e., the probability of the surrogate paradox in the new study. We investigate the performance of our proposed method through a simulation study and apply our method to data from clinical trials in schizophrenia.