Linganisha mbinu

	Jaribio la Kliniki la Kubatilishwa kwa Bayesian	Utafiti wa Uhakiki wa Utambuzi wa Kibayesiani
Nyanja	Epidemiolojia	Epidemiolojia
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1980s–2000s (formal methodology consolidated ~2004–2006)	1995–2001
Mwanzilishi	Donald A. Berry and David J. Spiegelhalter (applied Bayesian inference formally to RCT design)	Joseph, Gyorkos & Coupal; Dendukuri & Joseph (formal Bayesian DTA framework)
Aina	Randomized experimental study with Bayesian inference	Bayesian inferential study design
Chanzo asilia	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.
Majina mbadala	Bayesian RCT, Bayesian adaptive trial, Bayesian clinical trial design, BRCT	Bayesian DTA study, Bayesian test evaluation, Bayesian diagnostic test accuracy, BDAS
Zinazohusiana	5	6
Muhtasari	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.