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. 2019:41:e2019008.
doi: 10.4178/epih.e2019008. Epub 2019 Mar 28.

Intervention meta-analysis: application and practice using R software

Sung Ryul Shim  1   2 , Seong-Jang Kim  3   4
Affiliations

Intervention meta-analysis: application and practice using R software

Sung Ryul Shim et al. Epidemiol Health. 2019.

Abstract

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.

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

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Conflict of interest statement

The authors have no conflicts of interest to declare for this study.

Figures

Figure 1.
Figure 1.
Effect size of standardized mean difference (SMD).
Figure 2.
Figure 2.
Flow chart of intervention meta-analysis using R "meta" package. 1 Recommend.
Figure 3.
Figure 3.
Overall effect size of continuous example. SMD, standardized mean difference; CI, confidence interval; g, subgroup.
Figure 4.
Figure 4.
Forest plot of continuous example. SD, standard deviation; SMD, standardized mean difference; CI, confidence interval; g. subgroup .
Figure 5.
Figure 5.
Meta-regression bubble plot of continuous example.
Figure 6.
Figure 6.
Funnel plot of continuous example.
See this image and copyright information in PMC

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References

    1. Hwang SD, Shim SR. Meta-analysis: from forest plot to network meta-analysis. Seoul: Hannarae; 2018. pp. 12–178. (Korean)
    1. Shim SR. Intervention meta-analysis using R software. Gwacheon: SDB Lab; 2019. (Korean)
    1. Shim SR, Shin IS, Bae JM. Intervention meta-analysis using STATA software. J Health Info Stat. 2016;41:123–134. (Korean)
    1. Higgins JP, Green S. Cochrane handbook for systematic reviews of interventions. Version 5.1.0. 2011 [cited 2019 May 1]. Available from: http://www.cochrane-handbook.org.
    1. Borensein M, Hedges LV, Higgins JP, Rothstein HR. Introduction to meta-analysis. West Sussex: John Wiley & Sons; 2019. pp. 20–28.

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