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| 환자 증례 구성(case-mix)을 고려한 위험 조정 진단 정확도 연구× | 로지스틱 회귀× | |
|---|---|---|
| 분야≠ | 역학 | 연구 통계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | Conceptual roots 1980s–1990s; covariate-adjusted ROC formally introduced 2009 | 1958 |
| 창시자≠ | Margaret Pepe and colleagues; covariate-adjusted ROC formalized by Janes & Pepe (2009) | David Roxbee Cox |
| 유형≠ | Observational clinical study design with covariate adjustment | Method |
| 원전≠ | Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press. ISBN: 978-0198509844 | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 별칭≠ | case-mix-adjusted diagnostic accuracy, stratified diagnostic accuracy study, covariate-adjusted diagnostic accuracy, risk-stratified DTA study | logit model, binomial logistic regression, LR |
| 관련≠ | 6 | 3 |
| 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. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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