Comparer des méthodes

	Étude bayésienne de la précision diagnostique	Évaluation des tests et programmes de dépistage
Domaine	Épidémiologie	Épidémiologie
Famille	Process / pipeline	Process / pipeline
Année d'origine	1995–2001	1968 (Wilson-Jungner principles); statistical framework developed 1970s–2000s
Auteur d'origine	Joseph, Gyorkos & Coupal; Dendukuri & Joseph (formal Bayesian DTA framework)	Wilson & Jungner (WHO criteria, 1968); foundational work by Pepe, Altman, and others in statistical test evaluation
Type	Bayesian inferential study design	Observational diagnostic / epidemiological evaluation design
Source fondatrice	Dendukuri, N., & Joseph, L. (2001). Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics, 57(1), 158–167.	Wilson, J. M. G., & Jungner, G. (1968). Principles and Practice of Screening for Disease. World Health Organization. Public Health Papers No. 34.
Alias	Bayesian DTA study, Bayesian test evaluation, Bayesian diagnostic test accuracy, BDAS	screening study, screening performance evaluation, screening accuracy assessment, STE
Apparentées	6	6
Résumé	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.	Screening test evaluation is a systematic epidemiological approach for assessing whether a test or program can accurately and cost-effectively identify individuals with a condition before symptoms appear. It quantifies diagnostic performance metrics — sensitivity, specificity, predictive values, and the ROC curve — and evaluates whether a screening program meets established public health criteria for adoption and harm-benefit balance.