频数推理
马尔科夫蒙特卡洛
荟萃分析
计算机科学
软件
贝叶斯概率
贝叶斯网络
适度
数据挖掘
机器学习
人工智能
贝叶斯推理
医学
内科学
程序设计语言
作者
Sung Ryul Shim,Seong Jang Kim,Jong Hoo Lee,Gerta Rücker
出处
期刊:Epidemiology and Health
[Korean Society of Epidemiology (KAMJE)]
日期:2019-04-08
卷期号:41: e2019013-e2019013
被引量:159
标识
DOI:10.4178/epih.e2019013
摘要
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.
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