Network meta-analysis: application and practice using R software
Network meta-analysis: application and practice using R software
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DOI：
10.4178/epih.e2019013
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发表时间：
2019-04-08
影响因子：
3.8
通讯作者：
Ruecker, Gerta
中科院分区：
文献类型：
--
作者：
Shim, Sung Ryul;Kim, Seong-Jang;Ruecker, Gerta
本研究的目的是描述网络Meta分析的一般方法，这些方法可用于使用R软件进行定量数据合成。我们使用两种方法进行了网络荟萃分析：贝叶斯方法和频率法。对于贝叶斯方法，相应的R包是“gemtc”，对于频率法，相应的R包是“netmeta”。在使用贝叶斯框架评估网络元分析模型时，“rjgs”包是一个常见的工具。“RJAGS”用图形输出实现了马尔可夫链蒙特卡罗模拟。使用R软件报告估计的总体效应大小、异质性检验、主持人效应和发表偏倚。作者着重于两个灵活的模型，贝叶斯和频率，以确定总体影响大小的网络荟萃分析。这项研究侧重于网络Meta分析的实用方法，而不是理论概念，这使得材料对于非统计专业的韩国研究人员来说更容易理解。作者希望这项研究能帮助更多的韩国研究人员利用R软件进行网络荟萃分析和相关研究。
The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequentist methods. The corresponding R packages were "gemtc" for the Bayesian approach and "netmeta" for the frequentist approach. In estimating a network meta-analysis model using a Bayesian framework, the "rjags" package is a common tool. "rjags" implements Markov chain Monte Carlo simulation with a graphical output. The estimated overall effect sizes, test for heterogeneity, moderator effects, and publication bias were reported using R software. The authors focus on two flexible models, Bayesian and frequentist, to determine overall effect sizes in network meta-analysis. This study focused on the practical methods of network meta-analysis rather than theoretical concepts, making the material easy to understand for Korean researchers who did not major in statistics. The authors hope that this study will help many Korean researchers to perform network meta-analyses and conduct related research more easily with R software.