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	Studio Bayesiano sull'Accuratezza Diagnostica	Sperimentazione Clinica Randomizzata Bayesiana
Campo	Epidemiologia	Epidemiologia
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1995–2001	1980s–2000s (formal methodology consolidated ~2004–2006)
Ideatore	Joseph, Gyorkos & Coupal; Dendukuri & Joseph (formal Bayesian DTA framework)	Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design)
Tipo	Bayesian inferential study design	Randomized experimental study with Bayesian inference
Fonte seminale	Dendukuri, N., & Joseph, L. (2001). Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics, 57(1), 158–167.	Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756
Alias	Bayesian DTA study, Bayesian test evaluation, Bayesian diagnostic test accuracy, BDAS	Bayesian RCT, Bayesian adaptive trial, Bayesian clinical trial design, BRCT
Correlati	6	5
Sintesi	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.	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.