手法を比較

	ベイジアンネスト症例対照研究	症例対照研究
分野	疫学	疫学
系統	Process / pipeline	Process / pipeline
提唱年	1977 (nested case-control); Bayesian adaptation developed through 1990s–2010s	1950s (formal methodology); precursors in the 1920s
提唱者	Nested case-control: D. C. Thomas (1977); Bayesian extension: various authors in biostatistics	Janet Lane-Claypon (early precursors, 1926); formalized by Brian MacMahon and Jerome Cornfield in the 1950s–1960s
種類	Observational analytical study design with Bayesian inference	Observational analytic study design
原典	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.	Schlesselman, J.J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press. ISBN: 978-0195027860
別名	Bayesian NCC, Bayesian nested case-referent study, Bayesian sampled case-control within cohort	case-referent study, case-control design, retrospective case-control, case-control analysis
関連	5	6
概要	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 case-control study is a retrospective observational design in which individuals who have developed a disease or outcome of interest (cases) are compared with individuals who have not (controls) to determine whether prior exposure to a putative risk factor differs between the two groups. The primary measure of association is the odds ratio, which approximates the relative risk when the outcome is rare. Case-control studies are especially efficient for investigating rare diseases and generating etiological hypotheses.