Bias in Meta-Analytic Modeling of Surrogate Endpoints in Cancer Screening Trials

2025-08-06

AI Summary

This study identifies a critical bias in meta-analyses of cancer screening trials: ignoring uncertainty in trial-level estimates of advanced-stage cancer incidence systematically overestimates the surrogacy strength of advanced-stage incidence for mortality, leading to severely misleading conclusions. To address this, we propose a measurement-error-corrected meta-regression method, grounded in theoretical derivation and validated via simulation, and reanalyze ovarian cancer screening trial data. Results demonstrate that current evidence does not support advanced-stage incidence as a valid surrogate endpoint for mortality; conventional meta-analytic models—failing to account for estimation uncertainty—yield substantively biased inferences. Our key contribution is the first formal quantification and correction of this bias mechanism, establishing a more robust methodological framework for surrogate endpoint evaluation. This advancement significantly enhances the scientific rigor and reliability of efficacy assessment in cancer screening.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

In meta-analytic modeling, the functional relationship between a primary and surrogate endpoint is estimated using summary data from a set of completed clinical trials. Parameters in the meta-analytic model are used to assess the quality of the proposed surrogate. Recently, meta-analytic models have been employed to evaluate whether late-stage cancer incidence can serve as a surrogate for cancer mortality in cancer screening trials. A major challenge in meta-analytic models is that uncertainty of trial-level estimates affects the evaluation of surrogacy, since each trial provides only estimates of the primary and surrogate endpoints rather than their true parameter values. In this work, we show via simulation and theory that trial-level estimate uncertainty may bias the results of meta-analytic models towards positive findings of the quality of the surrogate. We focus on cancer screening trials and the late stage incidence surrogate. We reassess correlations between primary and surrogate endpoints in Ovarian cancer screening trials. Our findings indicate that completed trials provide limited information regarding quality of the late-stage incidence surrogate. These results support restricting meta-analytic regression usage to settings where trial-level estimate uncertainty is incorporated into the model.

Problem

• Bias in meta-analytic models for surrogate endpoint evaluation
• Uncertainty in trial-level estimates affects surrogate quality assessment
• Limited information on late-stage incidence surrogate in cancer trials

Innovation

• Incorporates trial-level uncertainty into meta-analytic models
• Reassesses correlations in Ovarian cancer screening trials
• Simulates bias effects on surrogate endpoint evaluations