Network Meta-Analysis
Abstract
Network meta-analysis allows to jointly estimate the relative effectiveness of various treatments or interventions. To achieve this, network meta-analyses combine both direct (i.e. observed) and indirect evidence in a network. This chapter provides a brief introduction into network meta-analysis, starting with a discussion of indirect and direct evidence, and how both can be combined to obtain more precise effect estimates. The concept of transitivity and consistency is also discussed. The chapter covers both a frequentist and Bayesian network meta-analysis approach, and provides a hands-on tutorial on how these methods can be implemented using R. Furthermore, techniques to assess publication bias mechanisms in network meta-analyses are described, and how network meta-regression can be conducted using R.
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Sensitivity analyses included excluding potential outliers and studies rated as unclear or high-bias in any domain.
“Sensitivity analyses included excluding potential outliers and studies rated as unclear or high-bias in any domain.21”
From Assessing the performance of artificial intelligence models in evaluating inflammatory skin disease severity: a systematic review and meta-analysis (2025) · cites this paper as [21]
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Cai, Z. R., Kim, J., Rezaei, S. J., Chen, M. L., Touma, F., Zhu, C., Onyeka, S., Fonjungo, F., Omiye, J. A., Krakowski, I., et al. (2025) · British Journal of Dermatology · doi:10.1093/bjd/ljaf250 · Cited by 8 · see citing works
- Authors
- Harrer, M., Cuijpers, P., Furukawa, T. A., & Ebert, D. D.
- Venue
- Doing Meta-Analysis with R
- Publisher
- Chapman and Hall/CRC
- Published
- 2021-07-09
- Pages
- 329-380
- OpenAlex
- W3178432325
- Cited by
- 6
- References
- 0
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 2 · 2021: 2
- FWCI
- 4.84
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Chapman & Hall/CRC eBooks (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- network meta-analysis, circumstantial evidence, direct evidence, transitivity, consistency, Bayesian network meta-analysis, publication bias, network meta-regression, R implementation
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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.
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