방법 비교

	환자 증례 구성(case-mix)을 고려한 위험 조정 진단 정확도 연구	ROC 분석 (수신자 조작 특성)
분야	역학	통계학
계열	Process / pipeline	Hypothesis test
기원 연도	Conceptual roots 1980s–1990s; covariate-adjusted ROC formally introduced 2009	1954 (signal detection); 1982 (AUC formalization)
창시자	Margaret Pepe and colleagues; covariate-adjusted ROC formalized by Janes & Pepe (2009)	Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)
유형	Observational clinical study design with covariate adjustment	Diagnostic accuracy evaluation
원전	Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press. ISBN: 978-0198509844	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	case-mix-adjusted diagnostic accuracy, stratified diagnostic accuracy study, covariate-adjusted diagnostic accuracy, risk-stratified DTA study	ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis
관련	6	4
요약	A risk-adjusted diagnostic accuracy study evaluates how well an index test identifies a target condition while explicitly accounting for patient-level risk factors that influence either disease prevalence or test performance. By adjusting for case-mix, it yields accuracy estimates — sensitivity, specificity, and AUC — that are not confounded by the composition of the study sample, enabling fairer comparisons across populations and clinical 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).