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

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 didactic 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 clinically 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 may make decision curve analysis more accessible to clinical audiences.