방법 비교

	메타분석적 선별검사 평가	ROC 분석 (수신자 조작 특성)
분야	역학	통계학
계열	Process / pipeline	Hypothesis test
기원 연도	2000s (formal bivariate/HSROC framework ~2001–2005)	1954 (signal detection); 1982 (AUC formalization)
창시자	Reitsma et al. (bivariate model); Rutter & Gatsonis (HSROC model)	Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)
유형	Quantitative evidence-synthesis method	Diagnostic accuracy evaluation
원전	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.	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.
별칭	diagnostic test accuracy meta-analysis, DTA meta-analysis, screening accuracy synthesis, meta-analytic DTA	ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis
관련	2	4
요약	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.	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).