Comparer des méthodes

	Évaluation de tests de dépistage appariés	Analyse ROC (Courbe Caractéristique d'Opération du Récepteur)
Domaine	Épidémiologie	Statistique
Famille	Process / pipeline	Hypothesis test
Année d'origine	1980s–2000s (formalized alongside diagnostic accuracy methodology)	1954 (signal detection); 1982 (AUC formalization)
Auteur d'origine	Methodological synthesis from matched case-control and diagnostic accuracy traditions (Pepe, Zhou, and others)	Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)
Type	Observational diagnostic study with matched design	Diagnostic accuracy evaluation
Source fondatrice	Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press. ISBN: 978-0198509844	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	matched diagnostic accuracy study, paired screening evaluation, matched-pair test performance study, matched screening assessment	ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis
Apparentées	6	4
Résumé	Matched screening test evaluation assesses the sensitivity, specificity, and predictive values of a screening or diagnostic test using a matched design, in which disease-positive cases are paired with one or more disease-free controls selected to share key characteristics such as age, sex, or clinical setting. Matching controls for confounders before measuring test performance produces more precise and less biased estimates of diagnostic accuracy, and enables direct paired comparisons of competing tests within the same subjects.	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).