手法を比較

	メタ回帰に基づく迅速レビュー	システマティック・レビュー
分野	科学計量学	科学計量学
系統	Process / pipeline	Process / pipeline
提唱年	2000s–2010s (convergence of rapid review and meta-regression)	1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines)
提唱者	Meta-regression: Simon Thompson & Stephen Sharp (1999); Rapid review methodology: Cochrane, WHO, and health technology assessment bodies (2000s onward)	Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004)
種類	Quantitative evidence synthesis variant	Evidence synthesis methodology
原典	Thompson, S. G., & Sharp, S. J. (1999). Explaining heterogeneity in meta-analysis: A comparison of methods. Statistics in Medicine, 18(20), 2693–2708.	Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401.
別名	rapid review with meta-regression, accelerated meta-regression review, rapid synthesis with meta-regression, RRMR	SLR, systematic review, evidence synthesis review, structured literature review
関連	5	5
概要	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.	A systematic literature review (SLR) is a structured, reproducible method for identifying, appraising, and synthesizing all relevant studies on a research question. Unlike a narrative review, it follows an explicit, pre-specified protocol — from database search strings through inclusion criteria to data extraction — so that the process is transparent, auditable, and replicable by other researchers. It is widely used in medicine, education, software engineering, and the social sciences to produce the most comprehensive possible evidence base on a topic.