Evidence-appraisal glossary
Decision curve analysis
Decision curve analysis judges whether a prediction model or diagnostic test is clinically useful, not just accurate. It plots net benefit across a range of threshold probabilities, weighing the gain from true positives against the harm from false positives, and compares the model to treat-all and treat-none strategies.
Also called: DCA, Net benefit analysis, Net benefit approach.
What it is
Decision curve analysis (DCA) asks a question that accuracy measures like AUC cannot: would acting on this model actually help patients? Traditional metrics count errors but ignore that a missed disease and a needless treatment carry different costs. DCA builds that trade-off in.
The key idea is the threshold probability: the risk level at which a person would opt for treatment. That threshold encodes how they weigh a false positive (unnecessary treatment) against a false negative (missed disease). At each threshold, DCA computes net benefit, true positives minus a weighted penalty for false positives, and plots it across a plausible threshold range.
How to use it when reading a study
Check that the model's curve sits above both default strategies (treat all, treat none) across thresholds clinicians would realistically use. If it only wins at implausible thresholds, its practical value is thin. Confirm the threshold range matches the decision context, and that DCA supplements, not replaces, calibration and discrimination.
This is a plain-language methodology definition for reading research. It is general education, not medical advice.