Model selection for component network meta-analysis in connected and disconnected networks: a simulation study
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
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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BMC medical research methodology. - 23, 1 (2023) , 140, ISSN: 1471-2288
- Ereignis
-
Veröffentlichung
- (wo)
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Freiburg
- (wer)
-
Universität
- (wann)
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2023
- Urheber
- DOI
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10.1186/s12874-023-01959-9
- URN
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urn:nbn:de:bsz:25-freidok-2373270
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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25.03.2025, 13:43 MEZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Petropoulou, Maria
- Rücker, Gerta
- Weibel, Stephanie
- Kranke, Peter
- Schwarzer, Guido
- Universität
Entstanden
- 2023
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