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Related Experiment Video

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Random-effects meta-analysis models for pooling rare events data: a comparison between frequentist and bayesian

Minghong Yao1, Ke Deng1, Yuning Wang1

  • 1Institute of Neurosurgery and Chinese Evidence-Based Medicine Center and Cochrane China, West China Hospital, MAGIC China Center, Sichuan University, Chengdu, China.

BMC Medical Research Methodology
|October 2, 2025
PubMed
Summary

For rare events meta-analysis, the beta-binomial model by Kuss and a Bayesian approach show promise. These methods offer robust alternatives for synthesizing studies, especially those with double-zero events, outperforming other models in simulations.

Keywords:
Beta-binomial modelBinomial-normal hierarchical modelRandom-effects meta-analysisRare events

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Area of Science:

  • Biostatistics
  • Medical Research Methodology

Background:

  • Standard random-effects meta-analysis models struggle with rare event data, particularly double-zero events.
  • Frequentist and Bayesian methods offer alternatives, but their comparative performance is understudied.

Purpose of the Study:

  • To evaluate and compare the performance of ten meta-analysis models for binary outcomes, focusing on rare events.
  • To assess frequentist and Bayesian approaches using simulations and real-world data.

Main Methods:

  • Evaluated ten meta-analysis models (seven frequentist, three Bayesian) for binary outcomes using odds ratios.
  • Conducted simulations varying event rates, treatment effects, study numbers, and heterogeneity.
  • Assessed performance using bias, interval width, root mean square error, and coverage, and applied methods to published rare events meta-analyses.

Main Results:

  • The beta-binomial model (Kuss) generally performed well; generalized estimating equations did not.
  • Model performance varied with heterogeneity: most models performed well with low heterogeneity, but poorly with high heterogeneity.
  • A Bayesian model with Beta-Hyperprior (Hong et al.) and a binomial-normal hierarchical model (Bhaumik) also showed good performance.

Conclusions:

  • The beta-binomial model by Kuss is recommended for rare events meta-analysis.
  • Bayesian models are identified as promising for pooling rare events data effectively.