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| 다기관 선별검사 평가× | 횡단면 역학 연구× | |
|---|---|---|
| 분야 | 역학 | 역학 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1976–2003 (core diagnostic accuracy framework; multicenter STARD standards formalized 2003) | 1960s (formal codification); widely practiced since mid-20th century |
| 창시자≠ | Methodological consensus (STARD group, Bossuyt et al.); broader diagnostic accuracy tradition rooted in Hanley & McNeil (1982) and Sackett & Haynes (1976) | Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s) |
| 유형≠ | Observational diagnostic accuracy study | Observational, descriptive/analytic epidemiological design |
| 원전≠ | 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. Annals of Internal Medicine, 138(1), 40-44. 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 |
| 별칭 | multicenter diagnostic accuracy study, multisite screening evaluation, multicenter test performance study, multicenter DTA study | prevalence study, cross-sectional survey, transversal study, cross-sectional design |
| 관련 | 6 | 6 |
| 요약≠ | A multicenter screening test evaluation measures the diagnostic accuracy of a screening test — its sensitivity, specificity, predictive values, and ROC-curve area — by enrolling participants across two or more independent clinical sites. Conducting the study at multiple centers broadens the patient spectrum, tests generalizability across different laboratory conditions and patient populations, and produces more externally valid accuracy estimates than a single-center study. | 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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