방법 비교

	위험 조정 선별 검사 평가	ROC 분석 (수신자 조작 특성)
분야	역학	통계학
계열	Process / pipeline	Hypothesis test
기원 연도	Late 1990s–2000s (formal statistical framework ~1997–2009)	1954 (signal detection); 1982 (AUC formalization)
창시자	Margaret Sullivan Pepe and colleagues (covariate-adjusted ROC methodology)	Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)
유형	Analytical study design	Diagnostic accuracy evaluation
원전	Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press. ISBN: 978-0198565826	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	risk-stratified screening accuracy study, covariate-adjusted diagnostic accuracy evaluation, risk-adjusted screening performance assessment, RASTE	ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis
관련	6	4
요약	Risk-adjusted screening test evaluation assesses the sensitivity, specificity, and overall discriminatory accuracy of a screening test after accounting for patient-level risk factors (covariates) that independently influence test results or disease prevalence. By conditioning performance metrics on observed covariates — age, sex, comorbidities, or prior screening history — this approach yields accuracy estimates that are not confounded by differences in population risk profiles, enabling fair comparisons across subgroups or study settings.	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).