Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Evaluación meta-analítica de pruebas de cribado× | Meta-regresión× | |
|---|---|---|
| Campo≠ | Epidemiología | Metaanálisis |
| Familia≠ | Process / pipeline | Regression model |
| Año de origen≠ | 2000s (formal bivariate/HSROC framework ~2001–2005) | 2002 |
| Autor original≠ | Reitsma et al. (bivariate model); Rutter & Gatsonis (HSROC model) | Simon Thompson & Julian Higgins |
| Tipo≠ | Quantitative evidence-synthesis method | Weighted regression for effect-size heterogeneity |
| Fuente 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 ↗ | Thompson, S. G., & Higgins, J. P. T. (2002). How should meta-regression analyses be undertaken and interpreted? Statistics in Medicine, 21(11), 1559–1573. DOI ↗ |
| Alias | diagnostic test accuracy meta-analysis, DTA meta-analysis, screening accuracy synthesis, meta-analytic DTA | Meta-Analytic Regression, Weighted Regression in Meta-Analysis, Moderator Analysis, Meta-regresyon |
| Relacionados | 2 | 2 |
| Resumen≠ | 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. | Meta-regression is a statistical technique that extends conventional meta-analysis by regressing study-level effect sizes on one or more study characteristics (moderators) to explain between-study heterogeneity. Formalized by Thompson and Higgins in 2002, it uses weighted least squares — weighting each study by the inverse of its variance — within a mixed-effects framework, allowing researchers to identify which study features systematically account for variation in observed effects across the literature. |
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