Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers.
BACKGROUND
Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques.


METHODS
In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques.


RESULTS
Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve.


CONCLUSIONS
Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided.
Vickers
Andrew J
AJ
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 307 East 63rd Street, New York, NY 10065, USA. vickersa@mskcc.org
Cronin
Angel M
AM
Elkin
Elena B
EB
Gonen
Mithat
M
D015415:Biomarkers / Q000032:analysis; D003198:Computer Simulation; D016001:Confidence Intervals; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003955:Diagnostic Tests, Routine; D006801:Humans; D016015:Logistic Models; D008297:Male; D008967:Molecular Biology; D011237:Predictive Value of Tests; D011336:Probability; D011471:Prostatic Neoplasms / Q000175:diagnosis
D015415:Biomarkers