Estimation and Comparison of Receiver Operating Characteristic Curves

By M. Pepe, G. Longton, H. Janes. The Stata Journal, 2009

311 citations recorded by the source corpus.

The receiver operating characteristic (ROC) curve displays the capacity of a marker or diagnostic test to discriminate between two groups of subjects, cases versus controls. We present a comprehensive suite of Stata commands for performing ROC analysis. Nonparametric, semiparametric, and parametric estimators are calculated. Comparisons between curves are based on the area or partial area under the ROC curve. Alternatively, pointwise comparisons between ROC curves or inverse ROC curves can be made. We describe options to adjust these analyses for covariates and to perform ROC regression in a companion article. We use a unified framework by representing the ROC curve as the distribution of the marker in cases where we have standardized it to the control reference distribution.

Subfields: Artificial Intelligence, Cardiology and Cardiovascular Medicine, Epidemiology

View Paper: https://www.semanticscholar.org/paper/d2414b15d2d61473c6a7fb671c4c5e086afb3633

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In Epidemiology

Citations in the corpus

Evaluating early warning indicators of banking crises: Satisfying policy requirements (2014, background): “WP 421 Evaluating early warning indicators of banking crises: Satisfying policy requirements 5 evaluation of its forecasting performance as these components define the loss function (eg Pesaran and Skouras (2002) and Granger and Machina (2006) and references therein).”
Evaluation metrics and statistical tests for machine learning (2024, background): “1, it follows that a ROC curve is always monotonically increasing function inside the unit square tied to the points (0, 0) and (1, 1) so that closer the ROC curve is to (0, 1) the better the predictions are 23 .”
Receiver operating characteristic (ROC) curve for medical researchers (2011, unknown): “Both parametric (binormal assumption) and non-parametric methods are available in the literature [13,18] but most statistical softwares do not have option to calculate the partial area under ROC curve (Table III).”
pROC: an open-source package for R and S+ to analyze and compare ROC curves (2011, methodology): “Pcvsuite, ROCR and ROC can compute AUC or pAUC, but the pAUC can only be defined as a portion of specificity.”
Development and Evaluation of a Genetic Risk Score for Obesity (2013, unknown): “…around the R2 and AUC metrics (comparing the difference in estimated metric values across 1,000 random samples drawn with replacement from the ARIC database; see Pepe, Longton, and Janes 2009) and by applying Pencina’s method (Pencina, D’Agostino, and Vasan 2008) to test change in the IDI metric.”
easyROC: An Interactive Web-tool for ROC Curve Analysis Using R Language Environment (2016, methodology): “It performs statistical tests for the comparison of ROC curves using DeLong et al. (1988), Venkatraman and Begg (1996) and Venkatraman (2000) for AUC, and Hanley and McNeil (1983) and Pepe et al. (2009) for both AUC and pAUC.”