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Selecting relevant moderators with Bayesian regularized meta-regression.

Caspar J Van Lissa1, Sara van Erp2, Eli-Boaz Clapper2

  • 1Dept. Methodology & Statistics, Tilburg University, The Netherlands.

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Summary
This summary is machine-generated.

Bayesian Regularized Meta-Analysis (BRMA) effectively selects relevant moderators in meta-regression, outperforming traditional methods by improving generalizability and reducing spurious findings. This approach is particularly useful for complex literature reviews with many potential variables.

Keywords:
bayesianhorseshoelassomachine learningmeta-analysisregularization

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Area of Science:

  • Statistics
  • Biostatistics
  • Meta-analysis

Background:

  • Meta-regression is crucial for analyzing heterogeneity in diverse literature.
  • A common challenge is the high number of potential moderators relative to studies, risking overfitting and non-convergence.
  • Existing methods struggle with selecting relevant moderators from large candidate pools.

Approach:

  • Introduced Bayesian Regularized Meta-Analysis (BRMA) using regularizing priors (LASSO, horseshoe) to shrink small coefficients, effectively selecting moderators.
  • Compared BRMA against restricted maximum likelihood (RMA) random effects meta-regression via simulation.
  • Developed open-source software implementations in R (pema package) and JASP.

Key Points:

  • BRMA demonstrated superior predictive performance and better rejection of irrelevant moderators compared to RMA.
  • While BRMA coefficients were slightly biased towards zero, residual heterogeneity estimates were less biased than RMA.
  • BRMA performed well even with small sample sizes (as few as 20 studies).

Conclusions:

  • BRMA offers a robust solution for meta-regression with numerous candidate moderators, especially in small sample settings.
  • The method enhances model generalizability and reduces the risk of spurious results in meta-analysis.
  • BRMA provides a valuable tool for researchers dealing with complex heterogeneous literature.