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| Bayesian diagnostic accuracy study× | Beijesa gadījumu-kontroles pētījums× | |
|---|---|---|
| Nozare | Epidemioloģija | Epidemioloģija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1995–2001 | 1990s–2000s (systematic application); Bayesian inference foundations: Bayes/Laplace 18th–19th c. |
| Autors≠ | Joseph, Gyorkos & Coupal; Dendukuri & Joseph (formal Bayesian DTA framework) | Sander Greenland (Bayesian epidemiology formalization); earlier Bayesian logistic methods: Leonard (1972) |
| Tips≠ | Bayesian inferential study design | Observational analytic study with Bayesian inference |
| Pirmavots≠ | Dendukuri, N., & Joseph, L. (2001). Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics, 57(1), 158–167. DOI ↗ | Greenland, S. (2006). Bayesian perspectives for epidemiological research: I. Foundations and basic methods. International Journal of Epidemiology, 35(3), 765-775. DOI ↗ |
| Citi nosaukumi | Bayesian DTA study, Bayesian test evaluation, Bayesian diagnostic test accuracy, BDAS | Bayesian case-control design, Bayesian odds ratio estimation, Bayesian matched case-control, Bayesian logistic regression case-control |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | 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 Bayesian case-control study applies Bayesian statistical inference to the classic case-control epidemiological design, formally combining prior knowledge about exposure-disease associations with observed case and control data to estimate posterior odds ratios and credible intervals. Rather than relying solely on observed data, the Bayesian framework allows investigators to incorporate external evidence — from prior studies, expert knowledge, or mechanistic understanding — into the analysis, yielding probability statements about effect sizes that are often more interpretable than classical p-values and confidence intervals. |
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