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Problematic meta-analyses: Bayesian and frequentist perspectives on combining randomized controlled trials and

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Summary

A Bayesian meta-analysis approach supports including non-randomized studies (NRS) alongside randomized controlled trials (RCT) for treatment-effect estimation. Frequentist methods may require caution when pooling both RCT and NRS data due to potential bias.

Keywords:
Bayes factorsBayesianFrequentistMeta-analysisPosterior probability

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Area of Science:

  • Biostatistics
  • Evidence Synthesis
  • Critical Care Medicine

Background:

  • Combining randomized controlled trials (RCT) and non-randomized studies (NRS) in meta-analysis is debated due to potential bias from NRS.
  • Assessing treatment effects requires robust methods that can account for inherent differences between study designs.

Purpose of the Study:

  • To compare a Bayesian meta-analytic approach with a frequentist approach for pooling RCT and NRS data.
  • To evaluate how each method handles potential bias introduced by non-randomized studies.

Main Methods:

  • A systematic search identified binary outcome meta-analyses in Critical-Care combining RCT and NRS.
  • Bayesian pooled treatment-effects and credible intervals were estimated using a half-Cauchy prior, with Bayes-factors (BF) determining model preference.
  • Frequentist pooled treatment-effects and confidence intervals were re-estimated using the DerSimonian-Laird (DSL) random effects model.

Main Results:

  • Out of 50 identified meta-analyses, 44 reported pooled estimates.
  • Frequentist confidence intervals excluded the null in 86% of cases, while Bayesian credible intervals embraced the null in 23 instances where frequentist intervals did not.
  • Bayes-factors supported a pooled model in 27 meta-analyses, indicating preference for integrating RCT and NRS data.

Conclusions:

  • An integrated Bayesian approach provides support for including non-randomized studies in pooled meta-analytic treatment-effect models.
  • Caution is advised when reporting frequentist pooled treatment effects that combine randomized controlled trials and non-randomized studies without careful consideration of potential bias.