A Theoretical Investigation of How Evidence Flows in Bayesian Network Meta-Analysis of Disconnected Networks

Béliveau, Audrey; Gustafson, Paul

doi:10.1214/20-ba1224

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…Several alternative NMA methods have been proposed to deal with disconnected networks [ 7 ]. A theoretical framework of the Bayesian contrast-based model has recently been provided for disconnected networks [ 8 ]. Arm-based approaches have also been developed to analyse connected and disconnected networks [ 9 – 11 ].…”

Section: Introductionmentioning

confidence: 99%

Model selection for component network meta-analysis in connected and disconnected networks: a simulation study

Πετροπούλου

Rücker

Weibel

et al. 2023

BMC Med Res Methodol

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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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Section: Introductionmentioning

confidence: 99%

Model selection for component network meta-analysis in connected and disconnected networks: a simulation study

Πετροπούλου

Rücker

Weibel

et al. 2023

BMC Med Res Methodol

View full text Add to dashboard Cite

show abstract

“…4 A theoretical framework of the Bayesian contrast-based model has recently been provided for disconnected networks. 5 Arm-based approaches have also been developed to analyse connected and disconnected networks. 6,7,8 Goring et al 9 proposed the random baseline treatment effects NMA model in the Bayesian framework to accommodate disconnected networks, while Béliveau et al 10 conducted a case study to evaluate the performance of the random baseline treatment effects in disconnected networks.…”

Section: Introductionmentioning

confidence: 99%

Model selection for component network meta-analysis in connected and disconnected networks: a simulation study

Πετροπούλου¹,

Rücker²,

Weibel³

et al. 2022

Preprint

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Network meta-analysis (NMA) is widely used in evidence synthesis to estimate the effects of several competing interventions for a given clinical condition. One of the challenges is that it is not possible in disconnected networks.Component network meta-analysis (CNMA) allows technically "reconnecting" a disconnected network with multicomponent interventions. The additive CNMA model assumes that the effect of any multicomponent intervention is the additive sum of its components. This assumption can be relaxed by adding interaction component terms, which improves the goodness of fit but decreases the network connectivity. Model selection aims at finding the model with a reasonable balance between the goodness of fit and connectivity (selected CNMA model).We aim to introduce a forward model selection strategy for CNMA models and to investigate the performance of CNMA models for connected and disconnected networks. We applied the methods to a real Cochrane review dataset and simulated data with additive, mildly, or strongly violated intervention effects. We started with connected networks, and we artificially constructed disconnected networks. We compared the results of the additive and the selected CNMAs from each connected and disconnected network with the NMA using the mean squared error and coverage probability.CNMA models provide good performance for connected networks and can be an alternative to standard NMA if additivity holds. On the contrary, model selection does not perform well for disconnected networks, and we recommend conducting separate analyses of subnetworks.

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“…Several alternative NMA methods have been proposed to deal with disconnected networks [5]. A theoretical framework of the Bayesian contrast-based model has recently been provided for disconnected networks [6]. Arm-based approaches have also been de-veloped to analyse connected and disconnected networks [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Model selection for component network meta-analysis in connected and disconnected networks: a simulation study

Πετροπούλου

Rücker

Weibel

et al. 2022

Preprint

View full text Add to dashboard Cite

Background Network meta-analysis (NMA) allows to estimate and rank 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.

show abstract

A Theoretical Investigation of How Evidence Flows in Bayesian Network Meta-Analysis of Disconnected Networks

Cited by 6 publications

References 36 publications

Model selection for component network meta-analysis in connected and disconnected networks: a simulation study

Model selection for component network meta-analysis in connected and disconnected networks: a simulation study

Model selection for component network meta-analysis in connected and disconnected networks: a simulation study

Model selection for component network meta-analysis in connected and disconnected networks: a simulation study

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