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

2026-04-22

AI Summary

This study addresses the surrogate paradox in clinical trials—where a treatment appears beneficial on a surrogate endpoint yet harms the true clinical outcome—by proposing a novel function-class-based meta-analytic framework. Leveraging data from K completed studies, the method estimates the probability of encountering the surrogate paradox in a new trial when only the surrogate is observed, without relying on the conventional assumption of transportability of the conditional mean function. The approach innovatively models bias through a flexible function class and employs Bayesian inference to quantify the resilience probability of the surrogate against the paradox. Simulations and an application to schizophrenia clinical trial data demonstrate that the proposed method effectively assesses surrogate paradox risk, thereby overcoming key limitations of traditional transportability-based frameworks.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Reasoning under Uncertainty: Probabilistic Programming
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

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.

Problem

• surrogate paradox
• surrogate marker
• meta-analysis
• resilience probability
• clinical trials

Innovation

• surrogate resilience
• functional class
• surrogate paradox
• meta-analysis
• transportability