手法を比較

	メタアナリティックスクリーニング検査評価	ROC分析（受信者操作特性曲線）
分野	疫学	統計学
系統	Process / pipeline	Hypothesis test
提唱年	2000s (formal bivariate/HSROC framework ~2001–2005)	1954 (signal detection); 1982 (AUC formalization)
提唱者	Reitsma et al. (bivariate model); Rutter & Gatsonis (HSROC model)	Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)
種類	Quantitative evidence-synthesis method	Diagnostic accuracy evaluation
原典	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.
別名	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
関連	2	4
概要	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).