Vertaile menetelmiä

	Bayesiläinen tapausselostutkimus	Bayesiläinen kohorttitutkimus
Tieteenala	Epidemiologia	Epidemiologia
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	1990s–2000s (systematic application); Bayesian inference foundations: Bayes/Laplace 18th–19th c.	1990s–2000s (widespread adoption in epidemiology)
Kehittäjä	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
Tyyppi	Observational analytic study with Bayesian inference	Observational longitudinal study with Bayesian inference
Alkuperäislähde	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
Rinnakkaisnimet	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
Liittyvät	6	5
Tiivistelmä	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.