Comparer des méthodes

	Analyse ROC Robuste	Analyse ROC (Courbe Caractéristique d'Opération du Récepteur)
Domaine	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test
Année d'origine	1990s–2000s	1954 (signal detection); 1982 (AUC formalization)
Auteur d'origine	Multiple contributors (Pepe, Qin, Zhou, and others)	Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)
Type	Robust diagnostic accuracy evaluation	Diagnostic accuracy evaluation
Source fondatrice	Pepe, M. S. (2000). An interpretation for the ROC curve and inference using GLM procedures. Biometrics, 56(2), 352–359.	Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36.
Alias	robust AUC analysis, outlier-resistant ROC, robust diagnostic accuracy analysis, robust sensitivity-specificity analysis	ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis
Apparentées	3	4
Résumé	Robust ROC analysis evaluates the diagnostic accuracy of a continuous or ordinal biomarker in distinguishing between two groups (e.g., diseased vs. healthy) while protecting against the distorting effects of outliers, non-normality, or distributional violations that can bias standard parametric ROC estimates and AUC confidence intervals.	ROC analysis evaluates how well a continuous or ordinal test variable discriminates between two binary outcome classes. By plotting the true positive rate (sensitivity) against the false positive rate (1 − specificity) across all decision thresholds, it produces a curve whose area under the curve (AUC) quantifies overall discriminative power, ranging from 0.5 (chance) to 1.0 (perfect discrimination).