Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Novērtēšana ar atbilstošu skrīninga testu× | ROC analīze (Receiver Operating Characteristic)× | |
|---|---|---|
| Nozare≠ | Epidemioloģija | Statistika |
| Saime≠ | Process / pipeline | Hypothesis test |
| Izcelsmes gads≠ | 1980s–2000s (formalized alongside diagnostic accuracy methodology) | 1954 (signal detection); 1982 (AUC formalization) |
| Autors≠ | Methodological synthesis from matched case-control and diagnostic accuracy traditions (Pepe, Zhou, and others) | Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics) |
| Tips≠ | Observational diagnostic study with matched design | Diagnostic accuracy evaluation |
| Pirmavots≠ | Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press. ISBN: 978-0198509844 | Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36. DOI ↗ |
| Citi nosaukumi | matched diagnostic accuracy study, paired screening evaluation, matched-pair test performance study, matched screening assessment | ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis |
| Saistītās≠ | 6 | 4 |
| Kopsavilkums≠ | Matched screening test evaluation assesses the sensitivity, specificity, and predictive values of a screening or diagnostic test using a matched design, in which disease-positive cases are paired with one or more disease-free controls selected to share key characteristics such as age, sex, or clinical setting. Matching controls for confounders before measuring test performance produces more precise and less biased estimates of diagnostic accuracy, and enables direct paired comparisons of competing tests within the same subjects. | ROC analysis evaluates how well a continuous or ordinal test variable discriminates between two binary outcome classes. By plotting the true positive rate (sensitivity) against the false positive rate (1 − specificity) across all decision thresholds, it produces a curve whose area under the curve (AUC) quantifies overall discriminative power, ranging from 0.5 (chance) to 1.0 (perfect discrimination). |
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