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| 위험 조정 선별 검사 평가× | 로지스틱 회귀× | |
|---|---|---|
| 분야≠ | 역학 | 연구 통계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | Late 1990s–2000s (formal statistical framework ~1997–2009) | 1958 |
| 창시자≠ | Margaret Sullivan Pepe and colleagues (covariate-adjusted ROC methodology) | David Roxbee Cox |
| 유형≠ | Analytical study design | Method |
| 원전≠ | Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press. ISBN: 978-0198565826 | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 별칭≠ | risk-stratified screening accuracy study, covariate-adjusted diagnostic accuracy evaluation, risk-adjusted screening performance assessment, RASTE | logit model, binomial logistic regression, LR |
| 관련≠ | 6 | 3 |
| 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. | 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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