Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Байєсівське дослідження діагностичної точності× | Мета-аналітичне дослідження діагностичної точності× | |
|---|---|---|
| Галузь | Епідеміологія | Епідеміологія |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1995–2001 | 1993–2005 (foundational models) |
| Автор методу≠ | Joseph, Gyorkos & Coupal; Dendukuri & Joseph (formal Bayesian DTA framework) | Moses, Shapiro & Littenberg (SROC framework, 1993); Reitsma et al. (bivariate model, 2005) |
| Тип≠ | Bayesian inferential study design | Quantitative systematic synthesis |
| Основоположне джерело≠ | Dendukuri, N., & Joseph, L. (2001). Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics, 57(1), 158–167. DOI ↗ | Reitsma, J. B., Glas, A. S., Rutjes, A. W., Scholten, R. J., Bossuyt, P. M., & Zwinderman, A. H. (2005). Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. Journal of Clinical Epidemiology, 58(10), 982–990. DOI ↗ |
| Інші назви | Bayesian DTA study, Bayesian test evaluation, Bayesian diagnostic test accuracy, BDAS | DTA meta-analysis, diagnostic meta-analysis, systematic review of diagnostic accuracy, pooled diagnostic accuracy |
| Пов'язані≠ | 6 | 2 |
| Підсумок≠ | 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 meta-analytic diagnostic accuracy study systematically identifies and pools sensitivity and specificity data from multiple primary diagnostic test accuracy studies. Using the bivariate or hierarchical summary ROC (HSROC) model, it produces a joint summary of a test's ability to correctly classify diseased and non-diseased individuals across diverse clinical settings, accounting for the inherent trade-off between sensitivity and specificity. |
| ScholarGateНабір даних ↗ |
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