Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pētījums par diagnostisko precizitāti, koriģētu pret risku× | ROC analīze (Receiver Operating Characteristic)× | |
|---|---|---|
| Nozare≠ | Epidemioloģija | Statistika |
| Saime≠ | Process / pipeline | Hypothesis test |
| Izcelsmes gads≠ | Conceptual roots 1980s–1990s; covariate-adjusted ROC formally introduced 2009 | 1954 (signal detection); 1982 (AUC formalization) |
| Autors≠ | Margaret Pepe and colleagues; covariate-adjusted ROC formalized by Janes & Pepe (2009) | Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics) |
| Tips≠ | Observational clinical study design with covariate adjustment | 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 | case-mix-adjusted diagnostic accuracy, stratified diagnostic accuracy study, covariate-adjusted diagnostic accuracy, risk-stratified DTA study | ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis |
| Saistītās≠ | 6 | 4 |
| Kopsavilkums≠ | A risk-adjusted diagnostic accuracy study evaluates how well an index test identifies a target condition while explicitly accounting for patient-level risk factors that influence either disease prevalence or test performance. By adjusting for case-mix, it yields accuracy estimates — sensitivity, specificity, and AUC — that are not confounded by the composition of the study sample, enabling fairer comparisons across populations and clinical settings. | 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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