Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Avaliação de Teste de Triagem Pareado× | Análise ROC (Receiver Operating Characteristic)× | |
|---|---|---|
| Área≠ | Epidemiologia | Estatística |
| Família≠ | Process / pipeline | Hypothesis test |
| Ano de origem≠ | 1980s–2000s (formalized alongside diagnostic accuracy methodology) | 1954 (signal detection); 1982 (AUC formalization) |
| Autor original≠ | Methodological synthesis from matched case-control and diagnostic accuracy traditions (Pepe, Zhou, and others) | Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics) |
| Tipo≠ | Observational diagnostic study with matched design | Diagnostic accuracy evaluation |
| Fonte seminal≠ | Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press. ISBN: 978-0198509844 | 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 ↗ |
| Outros nomes | 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 |
| Relacionados≠ | 6 | 4 |
| Resumo≠ | 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). |
| ScholarGateConjunto de dados ↗ |
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