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Power Approximations for Overall Average Effects in Meta-Analysis with Dependent Effect Sizes

Author

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  • Vembye, Mikkel Helding
  • Pustejovsky, James E
  • Pigott, Terri

    (Georgia State University)

Abstract

Meta-analytic models for dependent effect sizes have grown increasingly sophisticated over the last few decades, which has created challenges for a priori power calculations. We introduce power approximations for tests of average effect sizes based upon several common approaches for handling dependent effect sizes. In a Monte Carlo simulation, we show that the new power formulas can accurately approximate the true power of meta-analytic models for dependent effect sizes. Lastly, we investigate the Type I error rate and power for several common models, finding that tests using robust variance estimation provide better Type I error calibration than tests with model-based variance estimation. We consider implications for practice with respect to selecting a working model and an inferential approach.

Suggested Citation

  • Vembye, Mikkel Helding & Pustejovsky, James E & Pigott, Terri, 2022. "Power Approximations for Overall Average Effects in Meta-Analysis with Dependent Effect Sizes," MetaArXiv 6tp9y_v1, Center for Open Science.
    • Handle: RePEc:osf:metaar:6tp9y_v1
    DOI: 10.31219/osf.io/6tp9y_v1
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    References listed on IDEAS

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    1. Viechtbauer, Wolfgang, 2010. "Conducting Meta-Analyses in R with the metafor Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i03).
    2. Kenward, Michael G. & Roger, James H., 2009. "An improved approximation to the precision of fixed effects from restricted maximum likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2583-2595, May.
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