Σύγκριση μεθόδων

	Μελέτη Περίπτωσης-Ελέγχου με Μπεϋζιανή Προσέγγιση	Μελέτη "φωλιασμένης" περίπτωσης-μάρτυρα
Πεδίο	Επιδημιολογία	Επιδημιολογία
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1990s–2000s (systematic application); Bayesian inference foundations: Bayes/Laplace 18th–19th c.	1973–1977
Δημιουργός	Sander Greenland (Bayesian epidemiology formalization); earlier Bayesian logistic methods: Leonard (1972)	Nathan Mantel (1973); D. C. Thomas (1977 formalization)
Τύπος	Observational analytic study with Bayesian inference	Hybrid observational study design
Θεμελιώδης πηγή	Greenland, S. (2006). Bayesian perspectives for epidemiological research: I. Foundations and basic methods. International Journal of Epidemiology, 35(3), 765-775.	Thomas, D. C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. Journal of the Royal Statistical Society, Series A, 140(4), 469–491.
Εναλλακτικές ονομασίες	Bayesian case-control design, Bayesian odds ratio estimation, Bayesian matched case-control, Bayesian logistic regression case-control	NCC study, nested CC design, case-control within cohort, density sampling case-control
Συναφείς	6	6
Σύνοψη	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.	A nested case-control study is an efficient observational design embedded within a defined cohort. For each participant who develops the outcome of interest (a case), a small number of matched controls are sampled from those still at risk at the same point in time. This density-sampling strategy yields odds ratios that approximate incidence-rate ratios from the full cohort at a fraction of the data-collection cost — making it the preferred alternative when measuring exposures for all cohort members would be prohibitively expensive or technically demanding.