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| 风险调整的诊断准确性研究× | 前瞻性诊断准确性研究× | |
|---|---|---|
| 领域 | 流行病学 | 流行病学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | Conceptual roots 1980s–1990s; covariate-adjusted ROC formally introduced 2009 | Formalized 2000s; practice dates to mid-20th century |
| 提出者≠ | Margaret Pepe and colleagues; covariate-adjusted ROC formalized by Janes & Pepe (2009) | Established through STARD initiative (Bossuyt, Reitsma et al., 2000s) |
| 类型≠ | Observational clinical study design with covariate adjustment | Observational / evaluative study design |
| 开创性文献≠ | Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press. ISBN: 978-0198509844 | Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., ... & Cohen, J. F. (2015). STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ, 351, h5527. DOI ↗ |
| 别名 | case-mix-adjusted diagnostic accuracy, stratified diagnostic accuracy study, covariate-adjusted diagnostic accuracy, risk-stratified DTA study | prospective DTA study, prospective test accuracy study, forward-looking diagnostic study, prospective index test evaluation |
| 相关 | 6 | 6 |
| 摘要≠ | 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. | A prospective diagnostic accuracy study enrolls participants before any test results are known and follows them forward in time to evaluate how well an index test (the test under evaluation) distinguishes individuals with and without a target condition, using a reference standard applied independently. Key accuracy metrics include sensitivity, specificity, positive and negative predictive values, and the area under the ROC curve. The prospective design reduces many biases inherent in retrospective test evaluations. |
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