Comparer des méthodes

	Étude cas-témoins bayésienne	Étude de cohorte bayésienne
Domaine	Épidémiologie	Épidémiologie
Famille	Process / pipeline	Process / pipeline
Année d'origine	1990s–2000s (systematic application); Bayesian inference foundations: Bayes/Laplace 18th–19th c.	1990s–2000s (widespread adoption in epidemiology)
Auteur d'origine	Sander Greenland (Bayesian epidemiology formalization); earlier Bayesian logistic methods: Leonard (1972)	Bayesian framework: Thomas Bayes / Pierre-Simon Laplace; applied to cohort epidemiology from the 1990s onward
Type	Observational analytic study with Bayesian inference	Observational longitudinal study with Bayesian inference
Source fondatrice	Greenland, S. (2006). Bayesian perspectives for epidemiological research: I. Foundations and basic methods. International Journal of Epidemiology, 35(3), 765-775.	Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756
Alias	Bayesian case-control design, Bayesian odds ratio estimation, Bayesian matched case-control, Bayesian logistic regression case-control	Bayesian longitudinal cohort, Bayesian prospective cohort, Bayesian cohort analysis, Bayesian follow-up study
Apparentées	6	5
Résumé	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 Bayesian cohort study follows a defined group of individuals over time to estimate incidence, risk, or rate of outcomes, while using Bayesian statistical inference to incorporate prior knowledge and quantify uncertainty through posterior probability distributions rather than classical p-values and confidence intervals. It combines the longitudinal observational design of a cohort study with the probability-updating logic of Bayesian analysis, allowing richer uncertainty quantification and sequential updating as data accumulate.