方法对比

	贝叶斯随机对照试验	贝叶斯诊断准确性研究
领域	流行病学	流行病学
方法族	Process / pipeline	Process / pipeline
起源年份	1980s–2000s (formal methodology consolidated ~2004–2006)	1995–2001
提出者	Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design)	Joseph, Gyorkos & Coupal; Dendukuri & Joseph (formal Bayesian DTA framework)
类型	Randomized experimental study with Bayesian inference	Bayesian inferential study design
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	Dendukuri, N., & Joseph, L. (2001). Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics, 57(1), 158–167.
别名	Bayesian RCT, Bayesian adaptive trial, Bayesian clinical trial design, BRCT	Bayesian DTA study, Bayesian test evaluation, Bayesian diagnostic test accuracy, BDAS
相关	5	6
摘要	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.	A Bayesian diagnostic accuracy study evaluates how well a medical test distinguishes between people who have a condition and those who do not, using Bayesian statistical methods that formally incorporate prior knowledge into the estimation of sensitivity, specificity, and related measures. Unlike classical approaches that rely solely on the observed sample, Bayesian inference combines a likelihood model of the data with prior probability distributions to produce posterior estimates with intuitive credible intervals.