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| Studi Akurasi Diagnostik Adaptif× | Studi Akurasi Diagnostik Bayesian× | |
|---|---|---|
| Bidang | Epidemiologi | Epidemiologi |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 2000s–2010s (adaptive designs codified for diagnostics ~2010s) | 1995–2001 |
| Pencetus≠ | 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) |
| Tipe≠ | Adaptive observational/experimental study design | Bayesian inferential study design |
| Sumber perintis≠ | 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. DOI ↗ | Dendukuri, N., & Joseph, L. (2001). Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics, 57(1), 158–167. DOI ↗ |
| Alias | 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 |
| Terkait | 6 | 6 |
| Ringkasan≠ | 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. |
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