Comparer des méthodes

	Analyse ROC (Courbe Caractéristique d'Opération du Récepteur)	Sensibilité et spécificité
Domaine	Statistique	Statistiques de recherche
Famille	Hypothesis test	Process / pipeline
Année d'origine	1954 (signal detection); 1982 (AUC formalization)	1978
Auteur d'origine	Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)	Multiple sources in medical diagnosis and signal detection
Type	Diagnostic accuracy evaluation	Concept
Source fondatrice	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.	Altman, D. G., & Bland, J. M. (1994). Diagnostic tests 1: Sensitivity and specificity. BMJ, 308(6943), 1552.
Alias	ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis	diagnostic accuracy, true positive rate, true negative rate, receiver operating characteristic
Apparentées	4	4
Résumé	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).	Sensitivity and specificity are fundamental metrics of diagnostic test accuracy. Sensitivity is the probability that a test correctly identifies a person with the disease (true positive rate: TP / (TP + FN)). Specificity is the probability that a test correctly identifies a person without the disease (true negative rate: TN / (TN + FP)). Every test involves a trade-off: increasing sensitivity (catching all sick people) often reduces specificity (more false alarms). Choice of test threshold depends on the clinical context: screening for serious diseases favors sensitivity; confirming a diagnosis favors specificity.