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

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  • Pustejovsky, James E
  • Tipton, Elizabeth

Abstract

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 nature 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 multivariate meta-analysis, this paper describes an expanded range of working models, along with accompanying estimation methods, which offer benefits in terms of better capturing the types of data structures that occur in practice and 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) and illustrate the approach in a meta-analysis of randomized trials examining the effects of brief alcohol interventions for adolescents and young adults.

Suggested Citation

  • Pustejovsky, James E & Tipton, Elizabeth, 2020. "Meta-Analysis with Robust Variance Estimation: Expanding the Range of Working Models," MetaArXiv vyfcj, Center for Open Science.
    • Handle: RePEc:osf:metaar:vyfcj
    DOI: 10.31219/osf.io/vyfcj
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    References listed on IDEAS

    as
    1. Elizabeth Tipton & James E. Pustejovsky, 2015. "Small-Sample Adjustments for Tests of Moderators and Model Fit Using Robust Variance Estimation in Meta-Regression," Journal of Educational and Behavioral Statistics, , vol. 40(6), pages 604-634, December.
    2. Viechtbauer, Wolfgang, 2010. "Conducting Meta-Analyses in R with the metafor Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i03).
    3. Eric C. Hedberg, 2011. "ROBUMETA: Stata module to perform robust variance estimation in meta-regression with dependent effect size estimates," Statistical Software Components S457219, Boston College Department of Economics, revised 23 Apr 2014.
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    Cited by:

    1. Vembye, Mikkel Helding & Pustejovsky, James E & Pigott, Terri, 2022. "Power Approximations for Meta-Analysis of Dependent Effect Sizes," MetaArXiv 6tp9y, Center for Open Science.

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