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Research article
Multivariate Network Meta-Analysis Incorporating Class Effects
Rhiannon Kate Owen
University of Leicester Department of Health Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5977-376X
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Medical Research Methodology.
Background: Network meta-analysis synthesises data from a number of clinical trials in order to assess the comparative efficacy of multiple healthcare interventions in similar patient populations. In situations where clinical trial data are heterogeneously reported i.e. data are missing for one or more outcomes of interest, synthesising such data can lead to disconnected networks of evidence, increased uncertainty, and potentially biased estimates which can have severe implications for decision-making. To overcome this issue, strength can be borrowed between outcomes of interest in multivariate network meta-analyses. Furthermore, in situations where there are relatively few trials informing each treatment comparison, there is a potential issue with the sparsity of data in the treatment networks, which can lead to substantial parameter uncertainty. A multivariate network meta-analysis approach can be further extended to borrow strength between interventions of the same class using hierarchical models.
Methods: We extend the trivariate network meta-analysis model to incorporate the exchangeability between treatment effects belonging to the same class of intervention to increase precision in treatment effect estimates. We further incorporate a missing data framework to estimate uncertainty in trials that fail to report measures of variability in order to maximise the use of all available information for healthcare decision-making. The methods are applied to a motivating dataset in overactive bladder syndrome. The outcomes of interest were mean change from baseline in incontinence, voiding and urgency episodes. All models were tted using Bayesian Markov Chain Monte Carlo (MCMC) methods in WinBUGS.
Results: All models (univariate, multivariate, and multivariate models incorporating class effects) produced similar point estimates for all treatment effects. Incorporating class effects in multivariate models often increased precision in treatment effect estimates.
Conclusions: Multivariate network meta-analysis incorporating class effects allowed for the comparison of all interventions across all outcome measures to ameliorate the potential impact of outcome reporting bias, and further borrowed strength between interventions belonging to the same class of treatment to increase the precision in treatment effect estimates for healthcare policy and decision-making. Keywords: multivariate; network meta-analysis; mixed treatment comparisons; meta-analysis; class effect
Keywords
multivariate, network meta-analysis, mixed treatment comparisons, meta-analysis, class effect
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CURRENT STATUS: Published
Version 2
Posted 01 Jun, 2020
Published
On 08 Jul, 2020
No community comments so far
Editorial decision: Accept
On 20 May, 2020
Editor assigned
On 19 May, 2020
Submission checks complete
On 18 May, 2020
Editor invited
On 18 May, 2020
No comments provided
Editorial decision: Major revision
On 17 Apr, 2020
Review #2 received
Received 06 Apr, 2020
Review #1 received
Received 12 Feb, 2020
Reviewer #2 agreed
On 05 Feb, 2020
Reviewer #3 agreed
On 05 Feb, 2020
Reviewer #1 agreed
On 22 Jan, 2020
Reviewers invited
Invitations sent on 18 Jan, 2020
Editor assigned
On 30 Dec, 2019
Submission checks complete
On 29 Dec, 2019
Editor invited
On 29 Dec, 2019
First submitted
On 27 Dec, 2019
Metrics
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HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
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This preprint is under consideration at BMC Medical Research Methodology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Multivariate Network Meta-Analysis Incorporating Class Effects
Rhiannon Kate Owen
University of Leicester Department of Health Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0001-5977-376X
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 2
Posted 01 Jun, 2020
Published
On 08 Jul, 2020
No community comments so far
Editorial decision: Accept
On 20 May, 2020
Editor assigned
On 19 May, 2020
Submission checks complete
On 18 May, 2020
Editor invited
On 18 May, 2020
No comments provided
Editorial decision: Major revision
On 17 Apr, 2020
Review #2 received
Received 06 Apr, 2020
Review #1 received
Received 12 Feb, 2020
Reviewer #2 agreed
On 05 Feb, 2020
Reviewer #3 agreed
On 05 Feb, 2020
Reviewer #1 agreed
On 22 Jan, 2020
Reviewers invited
Invitations sent on 18 Jan, 2020
Editor assigned
On 30 Dec, 2019
Submission checks complete
On 29 Dec, 2019
Editor invited
On 29 Dec, 2019
First submitted
On 27 Dec, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Medical Research Methodology.
Background: Network meta-analysis synthesises data from a number of clinical trials in order to assess the comparative efficacy of multiple healthcare interventions in similar patient populations. In situations where clinical trial data are heterogeneously reported i.e. data are missing for one or more outcomes of interest, synthesising such data can lead to disconnected networks of evidence, increased uncertainty, and potentially biased estimates which can have severe implications for decision-making. To overcome this issue, strength can be borrowed between outcomes of interest in multivariate network meta-analyses. Furthermore, in situations where there are relatively few trials informing each treatment comparison, there is a potential issue with the sparsity of data in the treatment networks, which can lead to substantial parameter uncertainty. A multivariate network meta-analysis approach can be further extended to borrow strength between interventions of the same class using hierarchical models.
Methods: We extend the trivariate network meta-analysis model to incorporate the exchangeability between treatment effects belonging to the same class of intervention to increase precision in treatment effect estimates. We further incorporate a missing data framework to estimate uncertainty in trials that fail to report measures of variability in order to maximise the use of all available information for healthcare decision-making. The methods are applied to a motivating dataset in overactive bladder syndrome. The outcomes of interest were mean change from baseline in incontinence, voiding and urgency episodes. All models were tted using Bayesian Markov Chain Monte Carlo (MCMC) methods in WinBUGS.
Results: All models (univariate, multivariate, and multivariate models incorporating class effects) produced similar point estimates for all treatment effects. Incorporating class effects in multivariate models often increased precision in treatment effect estimates.
Conclusions: Multivariate network meta-analysis incorporating class effects allowed for the comparison of all interventions across all outcome measures to ameliorate the potential impact of outcome reporting bias, and further borrowed strength between interventions belonging to the same class of treatment to increase the precision in treatment effect estimates for healthcare policy and decision-making. Keywords: multivariate; network meta-analysis; mixed treatment comparisons; meta-analysis; class effect
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.