Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Avaliação Meta-analítica de Testes de Rastreio× | Análise ROC (Receiver Operating Characteristic)× | |
|---|---|---|
| Área≠ | Epidemiologia | Estatística |
| Família≠ | Process / pipeline | Hypothesis test |
| Ano de origem≠ | 2000s (formal bivariate/HSROC framework ~2001–2005) | 1954 (signal detection); 1982 (AUC formalization) |
| Autor original≠ | Reitsma et al. (bivariate model); Rutter & Gatsonis (HSROC model) | Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics) |
| Tipo≠ | Quantitative evidence-synthesis method | Diagnostic accuracy evaluation |
| Fonte seminal≠ | Reitsma, J. B., Glas, A. S., Rutjes, A. W. S., Scholten, R. J. P. M., Bossuyt, P. M., & Zwinderman, A. H. (2005). Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. Journal of Clinical Epidemiology, 58(10), 982–990. DOI ↗ | 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 | diagnostic test accuracy meta-analysis, DTA meta-analysis, screening accuracy synthesis, meta-analytic DTA | ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis |
| Relacionados≠ | 2 | 4 |
| Resumo≠ | Meta-analytic screening test evaluation is a quantitative evidence-synthesis approach that pools sensitivity, specificity, and related accuracy indices across multiple primary studies of the same screening or diagnostic test. It produces summary estimates of a test's ability to correctly identify disease-positive and disease-negative individuals, typically using the bivariate random-effects model or the Hierarchical Summary ROC (HSROC) framework, and visualises results with summary ROC curves and forest plots. | 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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