Epidemiol Health.  2019;41:e2019008. 10.4178/epih.e2019008.

Intervention meta-analysis: application and practice using R software

  • 1Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea. sungryul.shim@gmail.com
  • 2Urological Biomedicine Research Institute, Soonchunhyang University Hospital, Seoul, Korea.
  • 3Department of Nuclear Medicine, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Korea.
  • 4BioMedical Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea.


The objective of this study was to describe general approaches for intervention meta-analysis available for quantitative data synthesis using the R software. We conducted an intervention meta-analysis using two types of data, continuous and binary, characterized by mean difference and odds ratio, respectively. The package commands for the R software were "metacont", "metabin", and "metagen" for the overall effect size, "forest" for forest plot, "metareg" for meta-regression analysis, and "funnel" and "metabias" for the publication bias. The estimated overall effect sizes, test for heterogeneity and moderator effect, and the publication bias were reported using the R software. In particular, the authors indicated methods for calculating the effect sizes of the target studies in intervention meta-analysis. This study focused on the practical methods of intervention meta-analysis, rather than the theoretical concepts, for researchers with no major in statistics. Through this study, the authors hope that many researchers will use the R software to more readily perform the intervention meta-analysis and that this will in turn generate further related research.


Meta-analysis; Meta-regression; Forest plot; Heterogeneity; Publication bias; R software

MeSH Terms

Odds Ratio
Population Characteristics
Publication Bias
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