Why decision curves cross treat-all and treat-none: A PPV and calibration perspective.
- 1Universitätsspital Basel, Switzerland.
Abstract
Net benefit is widely used to assess whether clinical prediction models can improve decision-making, but its interpretation is often difficult in practice. In this note, we show how decision curves can be understood through two familiar quantities: positive predictive value and observed event rates in threshold-defined patient groups. We show that a model has positive net benefit compared with treating no one when, among patients classified as positive, the observed event rate exceeds the decision threshold. Similarly, a model outperforms treating everyone when, among patients classified as negative, the observed event rate is below the decision threshold. These results connect decision-curve performance directly to interpretable forms of threshold-specific calibration. We also describe how positive predictive value curves can complement decision curves by showing, at each threshold, the observed event rate among patients selected for action. Together, these perspectives help explain why a prediction model performs better or worse than default strategies and clarify how decision curve comparisons relate to familiar epidemiological quantities.
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BibTeX
@article{hoessly2026decision,
title = {{Why decision curves cross treat-all and treat-none: A PPV and calibration perspective.}},
author = {Linard Hoessly},
journal = {Global epidemiology},
year = {2026},
month = oct,
doi = {10.1016/j.gloepi.2026.100290},
url = {https://europepmc.org/article/MED/42898621},
note = {CC BY},
}
RIS
TY - JOUR
TI - Why decision curves cross treat-all and treat-none: A PPV and calibration perspective.
AU - Linard Hoessly
PY - 2026
DA - 2026/10/08
JO - Global epidemiology
DO - 10.1016/j.gloepi.2026.100290
UR - https://europepmc.org/article/MED/42898621
AB - Net benefit is widely used to assess whether clinical prediction models can improve decision-making, but its interpretation is often difficult in practice. In this note, we show how decision curves can be understood through two familiar quantities: positive predictive value and observed event rates in threshold-defined patient groups. We show that a model has positive net benefit compared with treating no one when, among patients classified as positive, the observed event rate exceeds the decision threshold. Similarly, a model outperforms treating everyone when, among patients classified as negative, the observed event rate is below the decision threshold. These results connect decision-curve performance directly to interpretable forms of threshold-specific calibration. We also describe how positive predictive value curves can complement decision curves by showing, at each threshold, the observed event rate among patients selected for action. Together, these perspectives help explain why a prediction model performs better or worse than default strategies and clarify how decision curve comparisons relate to familiar epidemiological quantities.
SN - 2590-1133
ER -