Network meta‐analysis of multicomponent interventions
Open accessAbstract
In network meta‐analysis (NMA), treatments can be complex interventions, for example, some treatments may be combinations of others or of common components. In standard NMA, all existing (single or combined) treatments are different nodes in the network. However, sometimes an alternative model is of interest that utilizes the information that some treatments are combinations of common components, called component network meta‐analysis (CNMA) model. The additive CNMA model assumes that the effect of a treatment combined of two components A and B is the sum of the effects of A and B, which is easily extended to treatments composed of more than two components. This implies that in comparisons equal components cancel out. Interaction CNMA models also allow interactions between the components. Bayesian analyses have been suggested. We report an implementation of CNMA models in the frequentist R package netmeta. All parameters are estimated using weighted least squares regression. We illustrate the application of CNMA models using an NMA of treatments for depression in primary care. Moreover, we show that these models can even be applied to disconnected networks, if the composite treatments in the subnetworks contain common components.
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The additivity assumption was tested by comparing Q-statistics of the additive component network meta-analysis model and the treatment-level network meta-analysis model.
“We tested the additivity assumption by comparing the difference in Cochrane’s Q-statistics of the additive component network meta-analysis model and the treatment level (ie, full-interaction) network meta-analysis model at a cut-off determined by the difference in the models’ degrees of freedom.20 49”
Network meta-analysis and component network meta-analysis allow estimation of separate effects for specific combinations of components, and individual components, respectively, through direct and indirect comparisons.
“Network meta-analysis and component network meta-analysis allow estimation of separate effects for specific combinations of components, and individual components, respectively, through direct and indirect comparisons.20”
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<b>netmeta</b> : An <i>R</i> Package for Network Meta-Analysis Using Frequentist Methods
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“Component network meta-analysis should also be applied to larger studies where various components of different therapies can be isolated and compared (Rücker et al., 2020).”
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- Authors
- Rücker, G., Petropoulou, M., & Schwarzer, G.
- Affiliations
- University of Freiburg (DE)
- Department of Primary Education School of Education, University of Ioannina Ioannina Greece
- Corresponding author
- Rücker, Gerta
- Venue
- Biometrical Journal
- Publisher
- Wiley
- Published
- 2019-04-25
- Volume
- 62
- Issue
- 3
- Pages
- 808-821
- ISSN
- 0323-3847 (ISSN-L)
- 1521-4036
- PMC
- PMC7217213
- OpenAlex
- W2941722846
- Cited by
- 195
- References
- 41
- Citations by year
- 2026: 25 · 2025: 32 · 2024: 25 · 2023: 37 · 2022: 38 · 2021: 26 · 2020: 12
- FWCI
- 12.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Biometrical Journal (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (RU1747/1‐2)
- 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
- component network meta-analysis, multicomponent intervention, complex interventions, weighted least squares, disconnected networks, depression treatment, primary care
- MeSH terms
- Biometry, Depression, Humans, Primary Health Care, Models, Statistical
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