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| ROC 분석 (수신자 조작 특성)× | 민감도와 특이도× | |
|---|---|---|
| 분야≠ | 통계학 | 연구 통계 |
| 계열≠ | Hypothesis test | Process / pipeline |
| 기원 연도≠ | 1954 (signal detection); 1982 (AUC formalization) | 1978 |
| 창시자≠ | Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics) | Multiple sources in medical diagnosis and signal detection |
| 유형≠ | Diagnostic accuracy evaluation | Concept |
| 원전≠ | 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. DOI ↗ | Altman, D. G., & Bland, J. M. (1994). Diagnostic tests 1: Sensitivity and specificity. BMJ, 308(6943), 1552. link ↗ |
| 별칭 | ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis | diagnostic accuracy, true positive rate, true negative rate, receiver operating characteristic |
| 관련 | 4 | 4 |
| 요약≠ | 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). | Sensitivity and specificity are fundamental metrics of diagnostic test accuracy. Sensitivity is the probability that a test correctly identifies a person with the disease (true positive rate: TP / (TP + FN)). Specificity is the probability that a test correctly identifies a person without the disease (true negative rate: TN / (TN + FP)). Every test involves a trade-off: increasing sensitivity (catching all sick people) often reduces specificity (more false alarms). Choice of test threshold depends on the clinical context: screening for serious diseases favors sensitivity; confirming a diagnosis favors specificity. |
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