เปรียบเทียบวิธี

	การศึกษาความแม่นยำในการวินิจฉัยแบบเบย์ (Bayesian Diagnostic Accuracy Study)	การออกแบบการศึกษาความแม่นยำในการวินิจฉัย
สาขาวิชา	ระบาดวิทยา	การวิจัยทางคลินิก
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1995–2001	2003-2015
ผู้ริเริ่ม	Joseph, Gyorkos & Coupal; Dendukuri & Joseph (formal Bayesian DTA framework)	Bossuyt, Reitsma, and STARD group (2003); clinical epidemiology pioneers
ประเภท	Bayesian inferential study design	Research Design
แหล่งต้นตำรับ	Dendukuri, N., & Joseph, L. (2001). Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics, 57(1), 158–167.	Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., ... & de Vet, H. C. (2003). Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Annals of Internal Medicine, 138(1), 40–44.
ชื่อเรียกอื่น	Bayesian DTA study, Bayesian test evaluation, Bayesian diagnostic test accuracy, BDAS	diagnostic accuracy study, test accuracy, STARD, diagnostic evaluation
ที่เกี่ยวข้อง	6	2
สรุป	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 diagnostic accuracy study evaluates how well a new diagnostic test (or biomarker, imaging modality, clinical assessment) detects the presence or absence of disease compared to a reference standard (gold standard). Standardized since 2003 by the STARD (Standards for Reporting of Diagnostic Accuracy Studies) initiative, diagnostic accuracy studies are fundamental to clinical medicine, determining whether and how new tests can improve patient diagnosis and treatment.