So sánh phương pháp

	Thử nghiệm lâm sàng đa trung tâm Giai đoạn III	Thử nghiệm lâm sàng ngẫu nhiên kiểu Bayes
Lĩnh vực	Dịch tễ học	Dịch tễ học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1940s–1990s (formalized through ICH harmonization ~1990s)	1980s–2000s (formal methodology consolidated ~2004–2006)
Người khởi xướng	Codified through ICH E9 guideline (1998) and decades of regulatory practice (FDA, EMA)	Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design)
Loại	Confirmatory interventional study design	Randomized experimental study with Bayesian inference
Công trình gốc	Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M., & Granger, C. B. (2015). Fundamentals of Clinical Trials (5th ed.). Springer. ISBN: 978-3319185385	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	Phase III multicenter RCT, confirmatory multicenter trial, Phase 3 multicenter study, pivotal multicenter trial	Bayesian RCT, Bayesian adaptive trial, Bayesian clinical trial design, BRCT
Liên quan	6	5
Tóm tắt	A multicenter Phase III clinical trial is the definitive confirmatory study that tests whether a new intervention produces a clinically meaningful benefit over a comparator in a large, representative patient population enrolled at two or more independent research sites. It is the primary evidence basis for regulatory approval by agencies such as the FDA and EMA, combining the statistical power of large samples with the external validity gained from geographic and demographic diversity across sites.	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.