Võrdle meetodeid

	Meta-analytic Screening Test Evaluation	Meta-regressioon	ROC-analüüs (vastuvõtja töökarakteristik)
Valdkond	Epidemioloogia	Metaanalüüs	Statistika
Perekond	Process / pipeline	Regression model	Hypothesis test
Tekkeaasta	2000s (formal bivariate/HSROC framework ~2001–2005)	2002	1954 (signal detection); 1982 (AUC formalization)
Looja	Reitsma et al. (bivariate model); Rutter & Gatsonis (HSROC model)	Simon Thompson & Julian Higgins	Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)
Tüüp	Quantitative evidence-synthesis method	Weighted regression for effect-size heterogeneity	Diagnostic accuracy evaluation
Algallikas	Reitsma, J. B., Glas, A. S., Rutjes, A. W. S., Scholten, R. J. P. M., Bossuyt, P. M., & Zwinderman, A. H. (2005). Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. Journal of Clinical Epidemiology, 58(10), 982–990.	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.
Rööpnimetused	diagnostic test accuracy meta-analysis, DTA meta-analysis, screening accuracy synthesis, meta-analytic DTA	Meta-Analytic Regression, Weighted Regression in Meta-Analysis, Moderator Analysis, Meta-regresyon	ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis
Seotud	2	2	4
Kokkuvõte	Meta-analytic screening test evaluation is a quantitative evidence-synthesis approach that pools sensitivity, specificity, and related accuracy indices across multiple primary studies of the same screening or diagnostic test. It produces summary estimates of a test's ability to correctly identify disease-positive and disease-negative individuals, typically using the bivariate random-effects model or the Hierarchical Summary ROC (HSROC) framework, and visualises results with summary ROC curves and forest plots.	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).