Compară metode

	Analiza ROC robustă	Analiza ROC (Caracteristica Operativă a Receptorului)
Domeniu	Statistică	Statistică
Familie	Hypothesis test	Hypothesis test
Anul apariției	1990s–2000s	1954 (signal detection); 1982 (AUC formalization)
Autorul original	Multiple contributors (Pepe, Qin, Zhou, and others)	Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)
Tip	Robust diagnostic accuracy evaluation	Diagnostic accuracy evaluation
Sursa seminală	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.
Denumiri alternative	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
Înrudite	3	4
Rezumat	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).