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Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods.

František Bartoš1,2, Maximilian Maier1,3, Eric-Jan Wagenmakers1

  • 1Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands.

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Summary
This summary is machine-generated.

Publication bias threatens meta-analysis validity. This study introduces Bayesian model-averaging to combine publication bias adjustment methods, providing more robust estimates by weighting approaches based on data support.

Keywords:
Bayesian model-averagingPET-PEESEmeta-analysispublication biasselection models

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Area of Science:

  • Statistical methodology
  • Meta-analysis
  • Scientific integrity

Background:

  • Publication bias undermines the reliability of meta-analyses and scientific evidence accumulation.
  • Existing methods for adjusting publication bias have inconsistent performance across different data-generating processes.
  • Conflicting results from competing methods can lead to selective application, compromising objectivity.

Approach:

  • Extended robust Bayesian meta-analysis to incorporate model-averaging.
  • Integrated two prominent publication bias adjustment approaches: selection models of p-values and small-study effect models.
  • Developed a model ensemble that weights estimates and evidence based on data support.

Key Points:

  • Bayesian model-averaging offers a robust solution to the condition-dependent limitations of individual publication bias adjustment methods.
  • The proposed ensemble approach provides data-driven weighting of complementary methods, mitigating conflicting conclusions.
  • Demonstrated benefits through applications, simulations, and comparisons with multi-lab replications.

Conclusions:

  • Bayesian model-averaging enhances the reliability of meta-analytic findings in the presence of publication bias.
  • This approach facilitates more objective and consistent adjustments for publication bias.
  • The method provides a unified framework for integrating diverse strategies to address publication bias.