Why decision curves go above or below treat-all and treat-none: a PPV- and calibration-based guide for clinical prediction models
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914583005888512 |
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| author | Hoessly, Linard |
| author_facet | Hoessly, Linard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26184 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Why decision curves go above or below treat-all and treat-none: a PPV- and calibration-based guide for clinical prediction models Hoessly, Linard Applications 62C05 (Primary) 62F07, 62H30, 62P10 (Secondary) 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. |
| title | Why decision curves go above or below treat-all and treat-none: a PPV- and calibration-based guide for clinical prediction models |
| topic | Applications 62C05 (Primary) 62F07, 62H30, 62P10 (Secondary) |
| url | https://arxiv.org/abs/2603.26184 |