Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Meta-Regresie-Bazată pe Revizuire Rapidă× | Meta-analiză bazată pe meta-regresie× | |
|---|---|---|
| Domeniu | Scientometrie | Scientometrie |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 2000s–2010s (convergence of rapid review and meta-regression) | 1993–1999 |
| Autorul original≠ | Meta-regression: Simon Thompson & Stephen Sharp (1999); Rapid review methodology: Cochrane, WHO, and health technology assessment bodies (2000s onward) | Stephen G. Thompson & Simon J. Sharp (systematic framework); earlier work by Berlin, Longnecker & Greenland (1993) |
| Tip≠ | Quantitative evidence synthesis variant | Quantitative evidence synthesis with covariate modeling |
| Sursa seminală | Thompson, S. G., & Sharp, S. J. (1999). Explaining heterogeneity in meta-analysis: A comparison of methods. Statistics in Medicine, 18(20), 2693–2708. DOI ↗ | Thompson, S. G., & Sharp, S. J. (1999). Explaining heterogeneity in meta-analysis: a comparison of methods. Statistics in Medicine, 18(20), 2693–2708. DOI ↗ |
| Denumiri alternative | rapid review with meta-regression, accelerated meta-regression review, rapid synthesis with meta-regression, RRMR | meta-regression, meta-analytic regression, weighted regression meta-analysis, MR-MA |
| Înrudite≠ | 5 | 4 |
| Rezumat≠ | A meta-regression-based rapid review is an accelerated evidence synthesis that combines the time-efficient protocols of a rapid review with meta-regression analysis to identify which study-level or population-level characteristics explain variability in effect sizes across included studies. By streamlining search and screening steps without sacrificing the explanatory power of regression modeling, this approach delivers actionable heterogeneity insights under decision-making time constraints. | Meta-regression-based meta-analysis extends standard meta-analysis by fitting a weighted regression model in which study-level characteristics (moderators) predict observed effect sizes. Rather than simply pooling effects, this approach asks why effects vary across studies — linking heterogeneity in outcomes to differences in population, intervention, design, or measurement features. It is the primary tool for explaining between-study variance in quantitative evidence synthesis. |
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