Compara mètodes

	Prova clínica aleatòria bayesiana	Estudi bayesià de precisió diagnòstica
Camp	Epidemiologia	Epidemiologia
Família	Process / pipeline	Process / pipeline
Any d'origen	1980s–2000s (formal methodology consolidated ~2004–2006)	1995–2001
Autor original	Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design)	Joseph, Gyorkos & Coupal; Dendukuri & Joseph (formal Bayesian DTA framework)
Tipus	Randomized experimental study with Bayesian inference	Bayesian inferential study design
Font seminal	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.
Àlies	Bayesian RCT, Bayesian adaptive trial, Bayesian clinical trial design, BRCT	Bayesian DTA study, Bayesian test evaluation, Bayesian diagnostic test accuracy, BDAS
Relacionats	5	6
Resum	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.