Epidemiol Health.
2019;41:e2019013. 10.4178/epih.e2019013.
Network meta-analysis: application and practice using R software
- Affiliations
-
- 1Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea. sungryul.shim@gmail.com
- 2Urological Biomedicine Research Institute, Soonchunhyang University Hospital, Seoul, Korea.
- 3Department of Nuclear Medicine, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Korea.
- 4BioMedical Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea.
- 5Department of Internal Medicine, Jeju National University Hospital, Jeju National University School of Medicine, Jeju, Korea.
- 6Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
- KMID: 2469416
- DOI: http://doi.org/10.4178/epih.e2019013
Abstract
- 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.
