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| Bayesian Case-Control Study× | Logistiline regressioon× | |
|---|---|---|
| Valdkond≠ | Epidemioloogia | Uurimisstatistika |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | 1990s–2000s (systematic application); Bayesian inference foundations: Bayes/Laplace 18th–19th c. | 1958 |
| Looja≠ | Sander Greenland (Bayesian epidemiology formalization); earlier Bayesian logistic methods: Leonard (1972) | David Roxbee Cox |
| Tüüp≠ | Observational analytic study with Bayesian inference | Method |
| Algallikas≠ | Greenland, S. (2006). Bayesian perspectives for epidemiological research: I. Foundations and basic methods. International Journal of Epidemiology, 35(3), 765-775. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Rööpnimetused≠ | Bayesian case-control design, Bayesian odds ratio estimation, Bayesian matched case-control, Bayesian logistic regression case-control | logit model, binomial logistic regression, LR |
| Seotud≠ | 6 | 3 |
| Kokkuvõte≠ | 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. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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