Network meta‐analysis of multicomponent interventions

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...

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Veröffentlicht in:Biometrical journal Jg. 62; H. 3; S. 808 - 821
Hauptverfasser: Rücker, Gerta, Petropoulou, Maria, Schwarzer, Guido
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.05.2020
John Wiley and Sons Inc
Schlagworte:
Bayesian analysis
combination therapies
complex interventions
disconnected networks
Information processing
Mathematical models
Meta-analysis
multiple interventions
network meta‐analysis
Parameter estimation
Regression analysis
Research Paper
Research Papers
multiple interventions
disconnected networks
network meta-analysis
combination therapies
complex interventions
ISSN:0323-3847, 1521-4036, 1521-4036
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Zusammenfassung: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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SourceType-Scholarly Journals-1
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ObjectType-Evidence Based Healthcare-1
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ISSN:0323-3847
1521-4036
1521-4036
DOI:10.1002/bimj.201800167