방법 비교

	베이지안 사례 시리즈	코호트 연구
분야	역학	역학
계열	Process / pipeline	Process / pipeline
기원 연도	1995–2006 (core SCCS method 1995; Bayesian extensions throughout 2000s)	Mid-20th century (formal epidemiological design codified ~1950s)
창시자	Farrington, Whitaker, and colleagues (self-controlled case series); Bayesian extension by multiple authors in pharmacovigilance	Doll & Hill (British Doctors Study, 1951); Snow (cholera, 1854)
유형	Observational analytical study design with Bayesian inference	Observational longitudinal study design
원전	Strom, B. L. (Ed.). (2001). Pharmacoepidemiology (3rd ed.). Wiley. [Chapter on case series and signal detection]	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641
별칭	Bayesian case-series, BCS analysis, Bayesian self-controlled case series, Bayesian SCCS	longitudinal study, follow-up study, panel study, incidence study
관련	1	6
요약	Bayesian case series is an observational epidemiological method that applies Bayesian inference to case series data — typically records of patients who experienced both a drug or vaccine exposure and an adverse health event. By incorporating prior evidence and computing posterior estimates of the incidence rate ratio within pre-specified risk windows, the method quantifies the strength of a temporal association between an exposure and an outcome while controlling for fixed individual-level confounding.	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.