Meta-analysis with Robust Variance Estimation: Expanding the Range of Working Models

James E. Pustejovsky*, Elizabeth Tipton

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

149 Scopus citations


In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to include all dependent effect sizes in a single meta-regression model, even when the exact form of the dependence is unknown. RVE uses a working model of the dependence structure, but the two currently available working models are limited to each describing a single type of dependence. Drawing on flexible tools from multilevel and multivariate meta-analysis, this paper describes an expanded range of working models, along with accompanying estimation methods, which offer potential benefits in terms of better capturing the types of data structures that occur in practice and, under some circumstances, improving the efficiency of meta-regression estimates. We describe how the methods can be implemented using existing software (the “metafor” and “clubSandwich” packages for R), illustrate the proposed approach in a meta-analysis of randomized trials on the effects of brief alcohol interventions for adolescents and young adults, and report findings from a simulation study evaluating the performance of the new methods.

Original languageEnglish (US)
Pages (from-to)425-438
Number of pages14
JournalPrevention Science
Issue number3
StatePublished - Apr 2022


  • Dependent effect sizes
  • Meta-analysis
  • Meta-regression
  • Robust standard errors

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health


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