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Network meta-analysis: application and practice using R software
Open accessAbstract
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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“The network meta-analysis was performed following the current Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline items (26,27): 1) a network geometry was created to explore the comparisons between resistance training loads; 2) transitivity was tested by fitting a network inconsistency assumption along with Q test and side-splitting analyses between indirect and direct”
From Resistance training load effects on muscle hypertrophy and strength gain: systematic review and network meta-analysis. (2021) · cites this paper as [27]
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Metadata
- Authors
- Shim, S. R., Kim, S.-J., Lee, J., & Rücker, G.
- Affiliations
- Korea University (KR)
- Pusan National University Yangsan Hospital (KR)
- University of Freiburg (DE)
- Corresponding author
- Shim, Sung Ryul
- Venue
- Epidemiology and Health
- Publisher
- Korean Society of Epidemiology
- Published
- 2019-04-08
- Volume
- 41
- Pages
- e2019013
- ISSN
- 1225-3596 (ISSN-L)
- 2092-7193
- PMC
- PMC6635665
- OpenAlex
- W2939443580
- Cited by
- 483
- References
- 6
- Citations by year
- 2026: 58 · 2025: 97 · 2024: 104 · 2023: 69 · 2022: 85 · 2021: 49 · 2020: 20 · 2019: 1
- FWCI
- 25.93
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Epidemiology and Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- network meta-analysis, R software, Bayesian network meta-analysis, Markov chain Monte Carlo, heterogeneity, publication bias, moderator effects
- MeSH terms
- Humans, Research Design, Software, Meta-Analysis as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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