Why decision curves go above or below treat-all and treat-none: a PPV- and calibration-based guide for clinical prediction models

2026-03-27

AI Summary

This study addresses the limited interpretability of net benefit in clinical decision curve analysis, which often stems from the absence of an intuitive comparison against “treat all” or “treat none” strategies. The authors innovatively reframe decision curves through the lens of positive predictive value (PPV) and calibration, explicitly linking net benefit to threshold-specific observed risk and PPV. For the first time, this approach establishes a direct connection between decision performance and both PPV and calibration within clinically relevant subgroups. The proposed PPV curve serves as a complementary tool to traditional decision curves, substantially enhancing the clinical interpretability of a model’s net benefit and enabling clinicians to more clearly discern at which decision thresholds model-guided interventions yield tangible clinical utility.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Decision/Utility Theory
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Net benefit is widely used and reported to evaluate the clinical utility of prediction models, yet its interpretation often remains difficult in practice. In this didactical note, we develop two complementary interpretations that make net benefit easier to understand for clinical audiences. We show that comparisons with treat-none and treat-all can be expressed through threshold-specific observed risk in patients above and below the decision threshold, linking decision-curve performance to calibration in clinically relevant subgroups. We also show how net benefit relates to positive predictive value, offering a more intuitive explanation of when acting on model predictions is justified. We derive and illustrate these results and propose positive predictive value curves as a practical complement to decision curves.

Problem

• decision curve
• net benefit
• clinical prediction models
• positive predictive value
• calibration

Innovation

• net benefit
• decision curve analysis
• calibration
• positive predictive value
• clinical prediction models