方法对比

	贝叶斯巢式病例对照研究	贝叶斯病例对照研究
领域	流行病学	流行病学
方法族	Process / pipeline	Process / pipeline
起源年份	1977 (nested case-control); Bayesian adaptation developed through 1990s–2010s	1990s–2000s (systematic application); Bayesian inference foundations: Bayes/Laplace 18th–19th c.
提出者	Nested case-control: D. C. Thomas (1977); Bayesian extension: various authors in biostatistics	Sander Greenland (Bayesian epidemiology formalization); earlier Bayesian logistic methods: Leonard (1972)
类型	Observational analytical study design with Bayesian inference	Observational analytic study with Bayesian inference
开创性文献	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.	Greenland, S. (2006). Bayesian perspectives for epidemiological research: I. Foundations and basic methods. International Journal of Epidemiology, 35(3), 765-775.
别名	Bayesian NCC, Bayesian nested case-referent study, Bayesian sampled case-control within cohort	Bayesian case-control design, Bayesian odds ratio estimation, Bayesian matched case-control, Bayesian logistic regression case-control
相关	5	6
摘要	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 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.