So sánh phương pháp

	Nghiên cứu độ chính xác chẩn đoán bắt cặp	Nghiên cứu dịch tễ học cắt ngang
Lĩnh vực	Dịch tễ học	Dịch tễ học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1990s–2000s (formalised with STARD 2003)	1960s (formal codification); widely practiced since mid-20th century
Người khởi xướng	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)
Loại	Diagnostic / clinical epidemiology study design	Observational, descriptive/analytic epidemiological design
Công trình gốc	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.	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
Tên gọi khác	matched DAS, paired diagnostic accuracy study, matched test accuracy study, matched sensitivity-specificity study	prevalence study, cross-sectional survey, transversal study, cross-sectional design
Liên quan	4	6
Tóm tắt	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.