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| Studija podudarnih dijagnostičkih točnosti× | Transverzalna epidemiološka studija× | |
|---|---|---|
| Područje | Epidemiologija | Epidemiologija |
| Obitelj | Process / pipeline | Process / pipeline |
| Godina nastanka≠ | 1990s–2000s (formalised with STARD 2003) | 1960s (formal codification); widely practiced since mid-20th century |
| Tvorac≠ | Evolved from matched case-control methodology; STARD standards formalised by Bossuyt et al. (2003) | Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s) |
| Vrsta≠ | Diagnostic / clinical epidemiology study design | Observational, descriptive/analytic epidemiological design |
| Temeljni izvor≠ | Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., Gatsonis, C. A., Glasziou, P. P., Irwig, L. M., Lijmer, J. G., Moher, D., Rennie, D., & de Vet, H. C. W. (2003). Towards complete and accurate reporting of studies of diagnostic accuracy: The STARD initiative. BMJ, 326(7379), 41–44. DOI ↗ | Kelsey, J. L., Whittemore, A. S., Evans, A. S., & Thompson, W. D. (1996). Methods in Observational Epidemiology (2nd ed.). Oxford University Press. ISBN: 978-0195080407 |
| Drugi nazivi | matched DAS, paired diagnostic accuracy study, matched test accuracy study, matched sensitivity-specificity study | prevalence study, cross-sectional survey, transversal study, cross-sectional design |
| Srodne≠ | 4 | 6 |
| Sažetak≠ | A matched diagnostic accuracy study evaluates how well an index test correctly identifies a target condition in study participants who have been matched on key characteristics — such as age, sex, or disease severity — to control for confounding. By pairing diseased and non-diseased subjects on relevant factors before administering the test, the design isolates the test's own discriminative performance from variation attributable to imbalanced covariates, yielding cleaner estimates of sensitivity, specificity, and related accuracy measures. | A cross-sectional epidemiological study measures the exposure(s) and outcome(s) of interest simultaneously in a defined population at a single point in time (or over a short period). Because there is no follow-up, it is the most efficient observational design for estimating disease prevalence and for generating hypotheses about associations between risk factors and health outcomes. |
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