So sánh phương pháp

	Nghiên cứu bệnh-chứng lồng ghép Bayes	Nghiên cứu đoàn hệ Bayes
Lĩnh vực	Dịch tễ học	Dịch tễ học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1977 (nested case-control); Bayesian adaptation developed through 1990s–2010s	1990s–2000s (widespread adoption in epidemiology)
Người khởi xướng	Nested case-control: D. C. Thomas (1977); Bayesian extension: various authors in biostatistics	Bayesian framework: Thomas Bayes / Pierre-Simon Laplace; applied to cohort epidemiology from the 1990s onward
Loại	Observational analytical study design with Bayesian inference	Observational longitudinal study with Bayesian inference
Công trình gốc	Thomas, D. C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. Journal of the Royal Statistical Society, Series A, 140(4), 469–491.	Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756
Tên gọi khác	Bayesian NCC, Bayesian nested case-referent study, Bayesian sampled case-control within cohort	Bayesian longitudinal cohort, Bayesian prospective cohort, Bayesian cohort analysis, Bayesian follow-up study
Liên quan	5	5
Tóm tắt	A Bayesian nested case-control study embeds a case-control sampling scheme within a defined prospective cohort and then estimates exposure-outcome associations using Bayesian inference. Cases are individuals in the cohort who develop the outcome of interest; controls are sampled from the risk set at the time each case is identified. The Bayesian framework allows incorporation of prior knowledge — from earlier studies, expert opinion, or biological plausibility — and produces full posterior distributions for effect estimates rather than single-point estimates with 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.