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Epidemiol Health > Accepted Articles
Epidemiology and Health 2019;e2019008.
DOI: https://doi.org/10.4178/epih.e2019008    [Accepted] Published online March 28, 2019.
Intervention Meta-analysis: Application and Practice using R software
SUNG RYUL SHIM1,2  , SEONG-JANG KIM3,4,5 
1Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea
2Urological Biomedicine Research Institute, Soonchunhyang University Hospital, Seoul, Korea
3Department of Nuclear Medicine, College of Medicine, Pusan National University, Yangsan, Korea
4BioMedical Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea
5Department of Nuclear Medicine, Pusan National University Yangsan Hospital, Yangsan, Korea
Correspondence  SUNG RYUL SHIM ,Tel: 01035843336, Email: sungryul.shim@gmail.com
Received: March 13, 2019  Accepted after revision: March 28, 2019
Abstract
The objective of this study was to describe general approaches of intervention meta-analysis that are available for quantitative synthesis of data using R software. We conducted an intervention meta-analysis using two types of data that included difference in means in continuous data and odds ratio in binary data. The package commands of R software were metacont, metabin, and metagen for overall effect size, forest for forest plot, metareg for meta-regression analysis, and funnel and metabias for publication bias. The estimated overall effect sizes, test for heterogeneity and moderator effect, and the publication bias were reported using R software. Especially authors stressed how to calculate effect sizes of target studies in intervention meta-analysis. This study focused on the practical methods of intervention meta-analysis rather than theoretical concepts for Korean researchers who were non-majored in statistics. Through this study, authors hope that many Korean researchers will use R software to perform an intervention meta-analysis more easily and that related research will be activated.
Keywords: Meta-analysis; Meta-regression; Forest plot; Heterogeneity; Publication bias; R software


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