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| 风险调整筛查试验评估× | 逻辑回归× | |
|---|---|---|
| 领域≠ | 流行病学 | 研究统计学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | Late 1990s–2000s (formal statistical framework ~1997–2009) | 1958 |
| 提出者≠ | Margaret Sullivan Pepe and colleagues (covariate-adjusted ROC methodology) | David Roxbee Cox |
| 类型≠ | Analytical study design | Method |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | risk-stratified screening accuracy study, covariate-adjusted diagnostic accuracy evaluation, risk-adjusted screening performance assessment, RASTE | logit model, binomial logistic regression, LR |
| 相关≠ | 6 | 3 |
| 摘要≠ | 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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