Exploiting relationships between outcomes in Bayesian multivariate network meta-analysis with an application to relapsing-remitting multiple sclerosis
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Published version
Author(s)
Waddingham, Ed
Matthews, Paul M
Ashby, Deborah
Type
Journal Article
Abstract
In multivariate network meta‐analysis (NMA), the piecemeal nature of the evidence base means that there may be treatment‐outcome combinations for which no data is available.
Most existing multivariate evidence synthesis models are either unable to estimate the missing treatment‐outcome combinations, or can only do so under particularly strong assumptions, such as perfect between‐study correlations between outcomes or constant effect size across outcomes. Many existing implementations are also limited to two treatments or two outcomes, or rely on model specification that is heavily tailored to the dimensions of the dataset. We present a Bayesian multivariate NMA model that estimates the missing treatment‐outcome combinations via mappings between the population mean effects, while allowing the study‐specific effects to be imperfectly correlated. The method is designed for aggregate‐level data (rather than individual patient data) and is likely to be useful when modeling multiple sparsely reported outcomes, or when varying definitions of the same underlying outcome are adopted by different studies. We implement the model via a novel decomposition of the treatment effect variance, which can be specified efficiently for an arbitrary dataset given some basic assumptions regarding the correlation structure. The method is illustrated using data concerning the efficacy and liver‐related safety of eight active treatments for relapsing‐remitting multiple sclerosis. The results indicate that fingolimod and interferon beta‐1b are the most efficacious treatments but also have some of the worst effects on liver safety. Dimethyl fumarate and glatiramer acetate perform reasonably on all of the efficacy and safety outcomes in the model.
Most existing multivariate evidence synthesis models are either unable to estimate the missing treatment‐outcome combinations, or can only do so under particularly strong assumptions, such as perfect between‐study correlations between outcomes or constant effect size across outcomes. Many existing implementations are also limited to two treatments or two outcomes, or rely on model specification that is heavily tailored to the dimensions of the dataset. We present a Bayesian multivariate NMA model that estimates the missing treatment‐outcome combinations via mappings between the population mean effects, while allowing the study‐specific effects to be imperfectly correlated. The method is designed for aggregate‐level data (rather than individual patient data) and is likely to be useful when modeling multiple sparsely reported outcomes, or when varying definitions of the same underlying outcome are adopted by different studies. We implement the model via a novel decomposition of the treatment effect variance, which can be specified efficiently for an arbitrary dataset given some basic assumptions regarding the correlation structure. The method is illustrated using data concerning the efficacy and liver‐related safety of eight active treatments for relapsing‐remitting multiple sclerosis. The results indicate that fingolimod and interferon beta‐1b are the most efficacious treatments but also have some of the worst effects on liver safety. Dimethyl fumarate and glatiramer acetate perform reasonably on all of the efficacy and safety outcomes in the model.
Date Issued
2020-07-16
Date Acceptance
2020-05-31
Citation
Statistics in Medicine, 2020, 39 (24), pp.3329-3346
ISSN
0277-6715
Publisher
John Wiley and Sons
Start Page
3329
End Page
3346
Journal / Book Title
Statistics in Medicine
Volume
39
Issue
24
Copyright Statement
© 2020 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.
This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Sponsor
Imperial College Healthcare NHS Trust- BRC Funding
Engineering & Physical Science Research Council (EPSRC)
Biogen International GmbH
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000548608400001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
RDA03-79560
EP/N014529/1
PO No: 28827
Subjects
Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Mathematical & Computational Biology
Public, Environmental & Occupational Health
Medical Informatics
Medicine, Research & Experimental
Statistics & Probability
Research & Experimental Medicine
Mathematics
correlated outcomes
evidence synthesis
multivariate network meta-analysis
relapsing-remitting multiple sclerosis
sparse data
PLACEBO-CONTROLLED PHASE-3
DOUBLE-BLIND
GLATIRAMER ACETATE
INTERFERON BETA-1A
CONTROLLED TRIAL
ORAL LAQUINIMOD
JOINT SYNTHESIS
MULTICENTER
BENEFIT
RISK
Publication Status
Published
Date Publish Online
2020-07-16