手法を比較

	ベイジアン症例対照研究	ベイズ的無作為化臨床試験
分野	疫学	疫学
系統	Process / pipeline	Process / pipeline
提唱年	1990s–2000s (systematic application); Bayesian inference foundations: Bayes/Laplace 18th–19th c.	1980s–2000s (formal methodology consolidated ~2004–2006)
提唱者	Sander Greenland (Bayesian epidemiology formalization); earlier Bayesian logistic methods: Leonard (1972)	Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design)
種類	Observational analytic study with Bayesian inference	Randomized experimental study with Bayesian inference
原典	Greenland, S. (2006). Bayesian perspectives for epidemiological research: I. Foundations and basic methods. International Journal of Epidemiology, 35(3), 765-775.	Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756
別名	Bayesian case-control design, Bayesian odds ratio estimation, Bayesian matched case-control, Bayesian logistic regression case-control	Bayesian RCT, Bayesian adaptive trial, Bayesian clinical trial design, BRCT
関連	6	5
概要	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.	A Bayesian randomized clinical trial (Bayesian RCT) combines the rigour of random treatment allocation with Bayesian statistical inference, allowing researchers to incorporate prior evidence and update beliefs continuously as trial data accumulate. Unlike the classical frequentist RCT, it yields direct probability statements about treatment effects and supports pre-specified adaptive stopping rules based on posterior probabilities.