Comparer des méthodes

	Méta-régression	Analyse ROC (Courbe Caractéristique d'Opération du Récepteur)
Domaine	Méta-analyse	Statistique
Famille	Regression model	Hypothesis test
Année d'origine	2002	1954 (signal detection); 1982 (AUC formalization)
Auteur d'origine	Simon Thompson & Julian Higgins	Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)
Type	Weighted regression for effect-size heterogeneity	Diagnostic accuracy evaluation
Source fondatrice	Thompson, S. G., & Higgins, J. P. T. (2002). How should meta-regression analyses be undertaken and interpreted? Statistics in Medicine, 21(11), 1559–1573.	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	Meta-Analytic Regression, Weighted Regression in Meta-Analysis, Moderator Analysis, Meta-regresyon	ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis
Apparentées	2	4
Résumé	Meta-regression is a statistical technique that extends conventional meta-analysis by regressing study-level effect sizes on one or more study characteristics (moderators) to explain between-study heterogeneity. Formalized by Thompson and Higgins in 2002, it uses weighted least squares — weighting each study by the inverse of its variance — within a mixed-effects framework, allowing researchers to identify which study features systematically account for variation in observed effects across the literature.	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).