Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Avaliação de Testes de Rastreamento Ajustada ao Risco× | Análise ROC (Receiver Operating Characteristic)× | |
|---|---|---|
| Área≠ | Epidemiologia | Estatística |
| Família≠ | Process / pipeline | Hypothesis test |
| Ano de origem≠ | Late 1990s–2000s (formal statistical framework ~1997–2009) | 1954 (signal detection); 1982 (AUC formalization) |
| Autor original≠ | Margaret Sullivan Pepe and colleagues (covariate-adjusted ROC methodology) | Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics) |
| Tipo≠ | Analytical study design | Diagnostic accuracy evaluation |
| Fonte seminal≠ | Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press. ISBN: 978-0198565826 | 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 ↗ |
| Outros nomes | risk-stratified screening accuracy study, covariate-adjusted diagnostic accuracy evaluation, risk-adjusted screening performance assessment, RASTE | ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis |
| Relacionados≠ | 6 | 4 |
| Resumo≠ | Risk-adjusted screening test evaluation assesses the sensitivity, specificity, and overall discriminatory accuracy of a screening test after accounting for patient-level risk factors (covariates) that independently influence test results or disease prevalence. By conditioning performance metrics on observed covariates — age, sex, comorbidities, or prior screening history — this approach yields accuracy estimates that are not confounded by differences in population risk profiles, enabling fair comparisons across subgroups or study 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). |
| ScholarGateConjunto de dados ↗ |
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