Methoden vergleichen
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| Prospektive Studie zur diagnostischen Genauigkeit× | Querschnittsepidemiologische Studie× | |
|---|---|---|
| Fachgebiet | Epidemiologie | Epidemiologie |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | Formalized 2000s; practice dates to mid-20th century | 1960s (formal codification); widely practiced since mid-20th century |
| Urheber≠ | Established through STARD initiative (Bossuyt, Reitsma et al., 2000s) | Classical epidemiology tradition; systematized by Brian MacMahon and Thomas Pugh (1960s) |
| Typ≠ | Observational / evaluative study design | Observational, descriptive/analytic epidemiological design |
| Wegweisende Quelle≠ | 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 ↗ | 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 |
| Aliasnamen | prospective DTA study, prospective test accuracy study, forward-looking diagnostic study, prospective index test evaluation | prevalence study, cross-sectional survey, transversal study, cross-sectional design |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | A prospective diagnostic accuracy study enrolls participants before any test results are known and follows them forward in time to evaluate how well an index test (the test under evaluation) distinguishes individuals with and without a target condition, using a reference standard applied independently. Key accuracy metrics include sensitivity, specificity, positive and negative predictive values, and the area under the ROC curve. The prospective design reduces many biases inherent in retrospective test evaluations. | 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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