Salīdzināt metodes

	Meta-regresijas balstīta ātrā pārskatīšana	Meta-regresijas metaanalīze
Nozare	Zinātnometrija	Zinātnometrija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2000s–2010s (convergence of rapid review and meta-regression)	1993–1999
Autors	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)
Tips	Quantitative evidence synthesis variant	Quantitative evidence synthesis with covariate modeling
Pirmavots	Thompson, S. G., & Sharp, S. J. (1999). Explaining heterogeneity in meta-analysis: A comparison of methods. Statistics in Medicine, 18(20), 2693–2708.	Thompson, S. G., & Sharp, S. J. (1999). Explaining heterogeneity in meta-analysis: a comparison of methods. Statistics in Medicine, 18(20), 2693–2708.
Citi nosaukumi	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
Saistītās	5	4
Kopsavilkums	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.