Intervention meta-analysis: application and practice using R software

被引:70
|
作者
Shim, Sung Ryul [1 ,2 ]
Kim, Seong-Jang [3 ,4 ]
机构
[1] Korea Univ, Dept Prevent Med, Coll Med, 145 Anam Ro, Seoul 02841, South Korea
[2] Soonchunhyang Univ Hosp, Urol Biomed Res Inst, Seoul, South Korea
[3] Pusan Natl Univ, Yangsan Hosp, Dept Nucl Med, Sch Med, Yangsan, South Korea
[4] Pusan Natl Univ, BioMed Res Inst Convergence Biomed Sci & Technol, Yangsan Hosp, Yangsan, South Korea
来源
EPIDEMIOLOGY AND HEALTH | 2019年 / 41卷
关键词
Meta-analysis; Meta-regression; Forest plot; Heterogeneity; Publication bias; R software;
D O I
10.4178/epih.e2019008
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
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 "mctagen" for the overall effect size, "forest" for forest plot, "metaree 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.
引用
收藏
页数:8
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