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Network meta-analysis: application and practice using R software.

Sung Ryul Shim1,2, Seong-Jang Kim3,4, Jonghoo Lee5

  • 1Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea.

Epidemiology and Health
|April 20, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces practical network meta-analysis methods using R software, detailing Bayesian and frequentist approaches for quantitative data synthesis. It aims to simplify complex statistical concepts for researchers, enhancing their ability to conduct meta-analyses.

Keywords:
Bayes’ theoremConsistencyMixed treatment comparisonMultiple treatments meta-analysisNetwork meta-analysisTransitivity

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Quantitative Synthesis

Background:

  • Network meta-analysis (NMA) is crucial for synthesizing evidence from multiple studies.
  • R software offers powerful tools for complex statistical analyses, including NMA.
  • Understanding practical NMA methods is essential for researchers, especially those without extensive statistical backgrounds.

Purpose of the Study:

  • To describe general approaches for network meta-analysis using R software for quantitative data synthesis.
  • To provide a practical guide to Bayesian and frequentist NMA methods.
  • To facilitate NMA implementation for Korean researchers using R.

Main Methods:

  • Conducted network meta-analysis using both Bayesian and frequentist approaches.
  • Utilized R packages 'gemtc' (Bayesian) and 'netmeta' (frequentist).
  • Employed 'rjags' package for Bayesian NMA, including Markov chain Monte Carlo simulation.

Main Results:

  • Demonstrated estimation of overall effect sizes, heterogeneity testing, moderator effects, and publication bias using R.
  • Provided practical examples of Bayesian and frequentist NMA models.
  • Successfully applied R software for comprehensive NMA reporting.

Conclusions:

  • Network meta-analysis can be effectively performed using R software with both Bayesian and frequentist methods.
  • The study offers a practical, non-theoretical approach to NMA, accessible to a broader research audience.
  • This guide empowers Korean researchers to conduct NMA more efficiently and confidently.