Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea.
Urological Biomedicine Research Institute, Soonchunhyang University Hospital, Seoul, Korea.
Epidemiol Health. 2019;41:e2019008. doi: 10.4178/epih.e2019008. Epub 2019 Mar 28.
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.
本研究旨在描述使用 R 软件进行定量数据分析的干预荟萃分析的一般方法。我们使用两种类型的数据进行干预荟萃分析，分别为连续型数据和二分型数据，其效应量指标分别为均数差值和优势比。R 软件的包命令包括用于总效应的“metacont”、“metabin”和“metagen”，用于森林图的“forest”，用于元回归分析的“metareg”，以及用于发表偏倚的“funnel”和“metabias”。使用 R 软件报告了估计的总效应大小、异质性和调节因素效应检验以及发表偏倚。特别是，作者指出了在干预荟萃分析中计算目标研究效应大小的方法。本研究针对的是没有统计学专业背景的研究人员，重点介绍干预荟萃分析的实用方法，而非理论概念。通过本研究，作者希望更多的研究人员能够使用 R 软件更方便地进行干预荟萃分析，从而推动进一步的相关研究。
