Krahasoni metodat

	Studim adaptiv i saktësisë diagnostike	Studimi Bayesian i Saktësisë Diagnostike
Fusha	Epidemiologji	Epidemiologji
Familja	Process / pipeline	Process / pipeline
Viti i origjinës	2000s–2010s (adaptive designs codified for diagnostics ~2010s)	1995–2001
Krijuesi	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)
Lloji	Adaptive observational/experimental study design	Bayesian inferential study design
Burimi themelues	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.
Emërtime të tjera	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
Të lidhura	6	6
Përmbledhja	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.