So sánh phương pháp

	Nghiên cứu bệnh-chứng Bayes	Thử nghiệm lâm sàng ngẫu nhiên kiểu Bayes
Lĩnh vực	Dịch tễ học	Dịch tễ học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1990s–2000s (systematic application); Bayesian inference foundations: Bayes/Laplace 18th–19th c.	1980s–2000s (formal methodology consolidated ~2004–2006)
Người khởi xướng	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)
Loại	Observational analytic study with Bayesian inference	Randomized experimental study with Bayesian inference
Công trình gốc	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
Tên gọi khác	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
Liên quan	6	5
Tóm tắt	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.