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Related Experiment Video

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An R-Based Landscape Validation of a Competing Risk Model
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Meta-analysis methods for risk difference: A comparison of different models.

Juanru Guo1, Mengli Xiao2, Haitao Chu3

  • 1Division of Biology and Biomedical Science, 12275Washington University School of Medicine, Saint Louis, MO, USA.

Statistical Methods in Medical Research
|November 2, 2022
PubMed
Summary
This summary is machine-generated.

For meta-analyses, bivariate random-effects models are recommended for synthesizing risk differences, especially when studies report zero events. These one-step methods avoid bias common in two-step approaches with zero-event data.

Keywords:
Bivariate generalized linear mixed-effects modelbivariate beta-binomial modelmeta-analysisrisk differencezero-event study

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Area of Science:

  • Biostatistics
  • Epidemiology

Background:

  • Risk difference is a key measure for binary outcomes in meta-analysis.
  • Conventional two-step methods may introduce bias with zero-event studies.
  • One-step bivariate random-effects models offer an alternative approach.

Approach:

  • Compared conventional two-step methods with bivariate random-effects models.
  • Utilized two case studies and three simulation studies.
  • Evaluated performance with and without zero-event studies.

Key Points:

  • Bivariate random-effects models can incorporate zero-event studies directly.
  • Two-step methods may exclude zero-event studies or require continuity corrections, potentially causing bias.
  • Performance differences are most pronounced when zero events are present.

Conclusions:

  • Bivariate random-effects models are recommended for estimating risk differences in meta-analyses.
  • This approach is particularly advantageous when dealing with studies reporting zero events.
  • Researchers should consider these models to improve the accuracy of meta-analysis synthesis.