方法对比

	贝叶斯队列研究	贝叶斯随机对照试验
领域	流行病学	流行病学
方法族	Process / pipeline	Process / pipeline
起源年份	1990s–2000s (widespread adoption in epidemiology)	1980s–2000s (formal methodology consolidated ~2004–2006)
提出者	Bayesian framework: Thomas Bayes / Pierre-Simon Laplace; applied to cohort epidemiology from the 1990s onward	Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design)
类型	Observational longitudinal study with Bayesian inference	Randomized experimental study with Bayesian inference
开创性文献	Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756	Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756
别名	Bayesian longitudinal cohort, Bayesian prospective cohort, Bayesian cohort analysis, Bayesian follow-up study	Bayesian RCT, Bayesian adaptive trial, Bayesian clinical trial design, BRCT
相关	5	5
摘要	A Bayesian cohort study follows a defined group of individuals over time to estimate incidence, risk, or rate of outcomes, while using Bayesian statistical inference to incorporate prior knowledge and quantify uncertainty through posterior probability distributions rather than classical p-values and confidence intervals. It combines the longitudinal observational design of a cohort study with the probability-updating logic of Bayesian analysis, allowing richer uncertainty quantification and sequential updating as data accumulate.	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.