Linganisha mbinu

	Uchanganuzi wa Tathmini ya Vipimo vya Uchunguzi wa Kibayesiani	Utafiti wa Uhakiki wa Utambuzi wa Kibayesiani
Nyanja	Epidemiolojia	Epidemiolojia
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1763 (theorem); clinical screening application formalized ~1959–1970s	1995–2001
Mwanzilishi	Thomas Bayes (theorem, 1763); applied to clinical screening by Ledley & Lusted (1959)	Joseph, Gyorkos & Coupal; Dendukuri & Joseph (formal Bayesian DTA framework)
Aina	Bayesian analytical framework for test evaluation	Bayesian inferential study design
Chanzo asilia	Fletcher, R. H., Fletcher, S. W., & Fletcher, G. S. (2014). Clinical Epidemiology: The Essentials (5th ed.). Lippincott Williams & Wilkins. ISBN: 978-1451144475	Dendukuri, N., & Joseph, L. (2001). Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics, 57(1), 158–167.
Majina mbadala	Bayesian diagnostic test evaluation, Bayesian predictive value analysis, posterior predictive value approach, Bayes theorem screening	Bayesian DTA study, Bayesian test evaluation, Bayesian diagnostic test accuracy, BDAS
Zinazohusiana	6	6
Muhtasari	Bayesian screening test evaluation applies Bayes' theorem to quantify how a screening test result changes the probability that an individual truly has a disease. Rather than reporting sensitivity and specificity in isolation, the approach centres on predictive values — the probability of disease given a positive or negative test — which depend critically on disease prevalence in the population being screened. The framework allows systematic updating of pre-test probability to post-test probability and supports decision-making under uncertainty.	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.