Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Prospektīva skrīninga testu novērtējums× | Epidemioloģisks šķērsgriezuma pētījums× | |
|---|---|---|
| Nozare | Epidemioloģija | Epidemioloģija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1980s–2000s (STARD 2003, updated 2015) | 1960s (formal codification); widely practiced since mid-20th century |
| Autors≠ | Formalized through diagnostic accuracy methodology (Sackett, Haynes, Tugwell; STARD initiative) | Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s) |
| Tips≠ | Prospective observational study design | Observational, descriptive/analytic epidemiological design |
| Pirmavots≠ | Bossuyt, P. M., Reitsma, J. B., Bruns, D. E., et al. (2015). STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies. BMJ, 351, h5527. 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 |
| Citi nosaukumi | prospective diagnostic accuracy study, prospective test performance study, forward-looking screening validation, prospective DTA study | prevalence study, cross-sectional survey, transversal study, cross-sectional design |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | A prospective screening test evaluation enrolls participants before the outcome is known, applies the screening test and the reference standard in temporal sequence, and measures how accurately the test identifies individuals with or without the target condition. This forward-looking design minimizes workup bias and spectrum bias, producing estimates of sensitivity, specificity, and predictive values that are more generalizable to real clinical or public-health screening contexts than retrospective alternatives. | 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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