Сравнение методов

	Байесовское исследование диагностической точности	Оценка скрининговых тестов
Область	Эпидемиология	Эпидемиология
Семейство	Process / pipeline	Process / pipeline
Год появления	1995–2001	1968 (Wilson-Jungner principles); statistical framework developed 1970s–2000s
Автор метода	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
Тип	Bayesian inferential study design	Observational diagnostic / epidemiological evaluation design
Основополагающий источник	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.
Другие названия	Bayesian DTA study, Bayesian test evaluation, Bayesian diagnostic test accuracy, BDAS	screening study, screening performance evaluation, screening accuracy assessment, STE
Связанные	6	6
Сводка	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.