方法对比
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| 贝叶斯筛查检验评估× | 诊断准确性研究设计× | |
|---|---|---|
| 领域≠ | 流行病学 | 临床研究 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1763 (theorem); clinical screening application formalized ~1959–1970s | 2003-2015 |
| 提出者≠ | Thomas Bayes (theorem, 1763); applied to clinical screening by Ledley & Lusted (1959) | Bossuyt, Reitsma, and STARD group (2003); clinical epidemiology pioneers |
| 类型≠ | Bayesian analytical framework for test evaluation | Research Design |
| 开创性文献≠ | Fletcher, R. H., Fletcher, S. W., & Fletcher, G. S. (2014). Clinical Epidemiology: The Essentials (5th ed.). Lippincott Williams & Wilkins. ISBN: 978-1451144475 | Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., ... & de Vet, H. C. (2003). Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Annals of Internal Medicine, 138(1), 40–44. DOI ↗ |
| 别名 | Bayesian diagnostic test evaluation, Bayesian predictive value analysis, posterior predictive value approach, Bayes theorem screening | diagnostic accuracy study, test accuracy, STARD, diagnostic evaluation |
| 相关≠ | 6 | 2 |
| 摘要≠ | Bayesian screening test evaluation applies Bayes' theorem to quantify how a screening test result changes the probability that an individual truly has a disease. Rather than reporting sensitivity and specificity in isolation, the approach centres on predictive values — the probability of disease given a positive or negative test — which depend critically on disease prevalence in the population being screened. The framework allows systematic updating of pre-test probability to post-test probability and supports decision-making under uncertainty. | A diagnostic accuracy study evaluates how well a new diagnostic test (or biomarker, imaging modality, clinical assessment) detects the presence or absence of disease compared to a reference standard (gold standard). Standardized since 2003 by the STARD (Standards for Reporting of Diagnostic Accuracy Studies) initiative, diagnostic accuracy studies are fundamental to clinical medicine, determining whether and how new tests can improve patient diagnosis and treatment. |
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