Extensions to Decision Curve Analysis for Evaluating Diagnostic Tests
Author Information
Author(s): Andrew J Vickers, Angel M Cronin, Elena B Elkin, Mithat Gonen
Primary Institution: Memorial Sloan-Kettering Cancer Center
Hypothesis
Can decision curve analysis be effectively extended to improve the evaluation of diagnostic tests, prediction models, and molecular markers?
Conclusion
Decision curve analysis can be easily extended to many applications common to performance measures for prediction models.
Supporting Evidence
- Decision curve analysis combines accuracy measures with clinical applicability.
- Simulation studies showed that repeated 10-fold cross-validation provided the best method for correcting a decision curve for overfit.
- Decision curve analysis allows assessment of clinical relevance without needing additional data.
Takeaway
This study shows a new way to evaluate medical tests that helps doctors make better decisions without needing extra data.
Methodology
The study presents extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data, and calculation of decision curves directly from predicted probabilities.
Potential Biases
Models evaluated on the same data set used to build them are at risk for overfitting.
Limitations
The study does not provide methods for correcting decision curve analysis for overfit or for calculating confidence intervals.
Participant Demographics
The study involved 740 men undergoing biopsy for prostate cancer.
Digital Object Identifier (DOI)
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