Developing Performance-Guaranteed Biomarker Combination Rules with Integrated External Information under Practical Constraint

2026-02-20

AI Summary

This study addresses the challenge of constructing interpretable, robust biomarker-based decision rules in clinical practice that satisfy a prespecified positive predictive value (PPV) constraint. The authors propose a novel linear decision framework that maximizes the true positive rate (TPR) under a strict PPV guarantee while adaptively incorporating external individual risk information to enhance discriminative performance. To the best of our knowledge, this is the first method to achieve statistically optimal TPR under a PPV constraint, balancing clinical utility with theoretical rigor. Through constrained optimization modeling, an adaptive information fusion mechanism, asymptotic theoretical analysis, and finite-sample simulations, the approach demonstrates superior performance in numerical experiments and is successfully applied to develop an early screening rule for pancreatic ductal adenocarcinoma among newly diagnosed diabetic patients.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Consent frameworks and practices on the web

Abstract

In clinical practice, there is significant interest in integrating novel biomarkers with existing clinical data to construct interpretable and robust decision rules. Motivated by the need to improve decision-making for early disease detection, we propose a framework for developing an optimal biomarker-based clinical decision rule that is both clinically meaningful and practically feasible. Specifically, our procedure constructs a linear decision rule designed to achieve optimal performance among class of linear rules by maximizing the true positive rate while adhering to a pre-specified positive predictive value constraint. Additionally, our method can adaptively incorporate individual risk information from external source to enhance performance when such information is beneficial. We establish the asymptotic properties of our proposed estimator and compare to the standard approach used in practice through extensive simulation studies. Results indicate that our approach offers strong finite-sample performance. We also apply the proposed methods to develop biomarker-based screening rules for pancreatic ductal adenocarcinoma (PDAC) among new-onset diabetes (NOD) patients.

Problem

• biomarker combination
• clinical decision rule
• positive predictive value constraint
• early disease detection
• external information integration

Innovation

• biomarker combination
• performance guarantee
• positive predictive value constraint
• external risk information integration
• linear decision rule