방법 비교

	베이즈 선별 검사 평가	코호트 연구
분야	역학	역학
계열	Process / pipeline	Process / pipeline
기원 연도	1763 (theorem); clinical screening application formalized ~1959–1970s	Mid-20th century (formal epidemiological design codified ~1950s)
창시자	Thomas Bayes (theorem, 1763); applied to clinical screening by Ledley & Lusted (1959)	Doll & Hill (British Doctors Study, 1951); Snow (cholera, 1854)
유형	Bayesian analytical framework for test evaluation	Observational longitudinal study design
원전	Fletcher, R. H., Fletcher, S. W., & Fletcher, G. S. (2014). Clinical Epidemiology: The Essentials (5th ed.). Lippincott Williams & Wilkins. ISBN: 978-1451144475	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641
별칭	Bayesian diagnostic test evaluation, Bayesian predictive value analysis, posterior predictive value approach, Bayes theorem screening	longitudinal study, follow-up study, panel study, incidence study
관련	6	6
요약	Bayesian screening test evaluation applies Bayes' theorem to quantify how a screening test result changes the probability that an individual truly has a disease. Rather than reporting sensitivity and specificity in isolation, the approach centres on predictive values — the probability of disease given a positive or negative test — which depend critically on disease prevalence in the population being screened. The framework allows systematic updating of pre-test probability to post-test probability and supports decision-making under uncertainty.	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.