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

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

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

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|April 20, 2019
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Summary
This summary is machine-generated.

This study provides practical guidance on performing intervention meta-analysis using R software for continuous and binary data. It details commands for effect size calculation, heterogeneity testing, and publication bias assessment.

Keywords:
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Area of Science:

  • Biostatistics
  • Quantitative Synthesis
  • Medical Research Methodology

Background:

  • Meta-analysis is crucial for synthesizing quantitative research findings.
  • Performing complex meta-analyses, especially intervention studies, can be challenging for non-statisticians.
  • The R software offers powerful tools for statistical analysis, including meta-analysis.

Purpose of the Study:

  • To describe practical approaches for intervention meta-analysis using R software.
  • To guide researchers, particularly those without a statistics background, through quantitative data synthesis.
  • To demonstrate the application of specific R package commands for various meta-analysis components.

Main Methods:

  • Conducted intervention meta-analysis using continuous (mean difference) and binary (odds ratio) data.
  • Utilized R software commands: metacont, metabin, metagen for effect sizes; forest for forest plots; metareg for meta-regression; funnel and metabias for publication bias.
  • Focused on practical implementation rather than theoretical underpinnings.

Main Results:

  • Demonstrated the calculation of overall effect sizes for intervention studies.
  • Showcased methods for testing heterogeneity and moderator effects.
  • Illustrated the assessment of publication bias using R software.

Conclusions:

  • The R software provides accessible tools for conducting intervention meta-analyses.
  • This practical guide aims to empower researchers to perform meta-analyses more readily.
  • Facilitating the use of R for meta-analysis is expected to stimulate further research in the field.