Model selection for component network meta-analysis in connected and disconnected networks: a simulation study
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Abstract
Background: Network meta-analysis (NMA) allows estimating and ranking the effects of several interventions for a clinical condition. Component network meta-analysis (CNMA) is an extension of NMA which considers the individual components of multicomponent interventions. CNMA allows to “reconnect” a disconnected network with common components in subnetworks. An additive CNMA assumes that component effects are additive. This assumption can be relaxed by including interaction terms in the CNMA. Methods: We evaluate a forward model selection strategy for component network meta-analysis to relax the additivity assumption that can be used in connected or disconnected networks. In addition, we describe a procedure to create disconnected networks in order to evaluate the properties of the model selection in connected and disconnected networks. We apply the methods to simulated data and a Cochrane review on interventions for postoperative nausea and vomiting in adults after general anaesthesia. Model performance is compared using average mean squared errors and coverage probabilities. Results: CNMA models provide good performance for connected networks and can be an alternative to standard NMA if additivity holds. For disconnected networks, we recommend to use additive CNMA only if strong clinical arguments for additivity exist. Conclusions: CNMA methods are feasible for connected networks but questionable for disconnected networks.
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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”
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- Authors
- Petropoulou, M., Rücker, G., Weibel, S., Kranke, P., & Schwarzer, G.
- Affiliations
- University of Freiburg (DE)
- Universitätsklinikum Würzburg (DE)
- Corresponding author
- Schwarzer, Guido
- Venue
- BMC Medical Research Methodology
- Published
- 2023-06-14
- Volume
- 23
- Issue
- 1
- Pages
- 140-140
- ISSN
- 1471-2288 (ISSN-L)
- PMC
- PMC10268445
- OpenAlex
- W4380685420
- Cited by
- 12
- References
- 36
- Citations by year
- 2026: 5 · 2025: 6 · 2023: 1
- FWCI
- 2.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Research Methodology (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Universitätsklinikum Freiburg
- 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 › Sociology and Political Science › Delphi Technique in Research
- Keywords
- component network meta-analysis, model selection, disconnected networks, additivity assumption, interaction terms, multicomponent intervention, postoperative nausea and vomiting, general anesthesia, simulation study
- MeSH terms
- Adult, Computer Simulation, Humans, Probability, Records
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