방법 비교

	베이지안 코호트 연구	코호트 연구
분야	역학	역학
계열	Process / pipeline	Process / pipeline
기원 연도	1990s–2000s (widespread adoption in epidemiology)	Mid-20th century (formal epidemiological design codified ~1950s)
창시자	Bayesian framework: Thomas Bayes / Pierre-Simon Laplace; applied to cohort epidemiology from the 1990s onward	Doll & Hill (British Doctors Study, 1951); Snow (cholera, 1854)
유형	Observational longitudinal study with Bayesian inference	Observational longitudinal study design
원전	Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641
별칭	Bayesian longitudinal cohort, Bayesian prospective cohort, Bayesian cohort analysis, Bayesian follow-up study	longitudinal study, follow-up study, panel study, incidence study
관련	5	6
요약	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.	A cohort study assembles a group of individuals who share a common starting point — typically freedom from the outcome of interest — and follows them over time to observe who develops the outcome. By comparing incidence rates between exposed and unexposed subgroups, researchers can estimate relative risk and absolute risk differences. Cohort studies are the gold-standard observational design for measuring disease incidence and establishing temporal relationships between exposure and outcome.