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| Kajian Ketepatan Diagnostik Bayesian× | Kajian Meta-Analitik Ketepatan Diagnostik× | |
|---|---|---|
| Bidang | Epidemiologi | Epidemiologi |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 1995–2001 | 1993–2005 (foundational models) |
| Pengasas≠ | Joseph, Gyorkos & Coupal; Dendukuri & Joseph (formal Bayesian DTA framework) | Moses, Shapiro & Littenberg (SROC framework, 1993); Reitsma et al. (bivariate model, 2005) |
| Jenis≠ | Bayesian inferential study design | Quantitative systematic synthesis |
| Sumber perintis≠ | 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 ↗ |
| Alias | 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 |
| Berkaitan≠ | 6 | 2 |
| Ringkasan≠ | 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. |
| ScholarGateSet data ↗ |
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