השוואת שיטות

	מחקר אדפטיבי לאיכות אבחון	מחקר בייסיאני לאבחון דיוק
תחום	אפידמיולוגיה	אפידמיולוגיה
משפחה	Process / pipeline	Process / pipeline
שנת המקור	2000s–2010s (adaptive designs codified for diagnostics ~2010s)	1995–2001
הוגה השיטה	Adaptation of STARD framework (Bossuyt et al.) combined with adaptive design principles (Jennison & Turnbull; FDA guidance)	Joseph, Gyorkos & Coupal; Dendukuri & Joseph (formal Bayesian DTA framework)
סוג	Adaptive observational/experimental study design	Bayesian inferential study design
מקור מכונן	Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L., ... & Cohen, J. F. (2015). STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ, 351, h5527.	Dendukuri, N., & Joseph, L. (2001). Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics, 57(1), 158–167.
כינויים	adaptive DTA study, adaptive diagnostic test evaluation, adaptive test accuracy trial, adaptive STARD study	Bayesian DTA study, Bayesian test evaluation, Bayesian diagnostic test accuracy, BDAS
קשורות	6	6
תקציר	An adaptive diagnostic accuracy study evaluates how well an index test distinguishes between patients with and without a target condition, while incorporating pre-specified interim analyses that allow modifications — such as sample size re-estimation, threshold adjustment, or subgroup enrichment — based on accumulating data. This design improves efficiency and ethical conduct compared to fixed-sample diagnostic studies, particularly when prior prevalence or test performance data are uncertain.	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.