Authors' objectives:

The broad objective of this report is to compare and contrast via simulation five meta-regression approaches that model the heterogeneity among study treatment effects: fixed effects with and without covariates; random effects with and without covariates; and control rate meta-regression.

Authors' results and conclusions: We identified and categorized 85 publications on meta-regression. We presented scenarios for which meta-regression might be informative, and expressed the most common meta-regression models in a common notation. Our expert panel made several recommendations regarding the simulation parameters. The panel also identified the need for outreach by the methodological community to the user community in advising how to conduct, interpret, and present meta-regression analyses, including the development of software and diagnostic aids to assess models. The simulation was a complete factorial design including all possible 7,776 combinations of the simulation parameters. The results were evaluated using an analysis-of-variance (ANOVA) model relating the simulation parameters to the bias in the estimation of the additive treatment effect. Across the five different meta-regression methods, six terms in a three-way ANOVA model were found to be practically important as they captured contributions to the bias of 10% or greater on average.

Authors' recommendations: Our simulation results produced specific guidelines for meta-regression practitioners that may be summarized in the key message that the causes of heterogeneity should be explored via the inclusion of covariates at both the person level and study level. Based on our comparison of bias across approaches, either fixed effects or random effects methods can be used to support this exploration. In terms of future simulation research, we need to increase the variability in sample sizes, explore correlations between study outcome rates and covariates at both the study and person level, and evaluate within-study variation for person-level covariate(s). We now have in place a simulation methodology, a common notation, and a supportive panel of national experts to enable and guide our continued work in this area. The research presented in this report has already impacted the application of meta-regression in several alternative medicine settings, and improved our ability to synthesize and understand these therapies.

Authors' methods: Systematic review

Project Status: Completed

URL for project: http://www.ahrq.gov/clinic/tp/ahrqcpgtp.htm

Year Published: 2004

English language abstract: An English language summary is available

Publication Type: Not Assigned

Country: United States

Organisation Name: Agency for Healthcare Research and Quality

Contact Address: Center for Outcomes and Evidence Technology Assessment Program, 540 Gaither Road, Rockville, MD 20850, USA. Tel: +1 301 427 1610; Fax: +1 301 427 1639;

Contact Name: martin.erlichman@ahrq.hhs.gov

Contact Email: martin.erlichman@ahrq.hhs.gov

Copyright: Agency for Healthcare Research and Quality (AHRQ)