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<p>Meta-analyses often include multiple dependent effect sizes, yet current methods typically neglect the resulting within-study dependencies or fail to address model uncertainty and publication bias adequately. We extend robust Bayesian meta-analysis (RoBMA) to a multilevel framework, simultaneously handling within-study dependencies, model uncertainty, heterogeneity, moderators, and publication bias. Specifically, the three-level RoBMA integrates approximate Bayesian selection models with PET-PEESE adjustments within a hierarchical Bayesian setting. We illustrate the methodology through empirical examples and demonstrate its performance via simulations. The approach is implemented in the <Emphasis FontCategory="NonProportional">RoBMA</Emphasis> <Emphasis FontCategory="NonProportional">R</Emphasis> package and JASP.</p>

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Robust Bayesian multilevel meta-analysis: Adjusting for publication bias in the presence of dependent effect sizes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月12日

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  • František Bartoš,
  • Maximilian Maier,
  • Eric-Jan Wagenmakers

摘要

Meta-analyses often include multiple dependent effect sizes, yet current methods typically neglect the resulting within-study dependencies or fail to address model uncertainty and publication bias adequately. We extend robust Bayesian meta-analysis (RoBMA) to a multilevel framework, simultaneously handling within-study dependencies, model uncertainty, heterogeneity, moderators, and publication bias. Specifically, the three-level RoBMA integrates approximate Bayesian selection models with PET-PEESE adjustments within a hierarchical Bayesian setting. We illustrate the methodology through empirical examples and demonstrate its performance via simulations. The approach is implemented in the RoBMA R package and JASP.