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| Байесовски анализ на диагностична точност× | Дизайн на изследване за диагностична точност× | |
|---|---|---|
| Област≠ | Епидемиология | Клинични изследвания |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1995–2001 | 2003-2015 |
| Създател≠ | Joseph, Gyorkos & Coupal; Dendukuri & Joseph (formal Bayesian DTA framework) | Bossuyt, Reitsma, and STARD group (2003); clinical epidemiology pioneers |
| Тип≠ | Bayesian inferential study design | Research Design |
| Основополагащ източник≠ | Dendukuri, N., & Joseph, L. (2001). Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics, 57(1), 158–167. DOI ↗ | 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 ↗ |
| Други названия | Bayesian DTA study, Bayesian test evaluation, Bayesian diagnostic test accuracy, BDAS | diagnostic accuracy study, test accuracy, STARD, diagnostic evaluation |
| Свързани≠ | 6 | 2 |
| Резюме≠ | A Bayesian diagnostic accuracy study evaluates how well a medical test distinguishes between people who have a condition and those who do not, using Bayesian statistical methods that formally incorporate prior knowledge into the estimation of sensitivity, specificity, and related measures. Unlike classical approaches that rely solely on the observed sample, Bayesian inference combines a likelihood model of the data with prior probability distributions to produce posterior estimates with intuitive credible intervals. | 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. |
| ScholarGateНабор от данни ↗ |
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