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Multivariate network meta-analysis incorporating class effects

Rhiannon Owen Orcid Logo, Sylwia Bujkiewicz, Douglas G. Tincello, Keith R. Abrams

BMC Medical Research Methodology, Volume: 20, Issue: 1

Swansea University Author: Rhiannon Owen Orcid Logo

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Abstract

BackgroundNetwork 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 mor...

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Published in: BMC Medical Research Methodology
ISSN: 1471-2288
Published: Springer Science and Business Media LLC 2020
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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.MethodsWe 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 did not 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 fitted using Bayesian Markov Chain Monte Carlo (MCMC) methods in WinBUGS.ResultsAll 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.ConclusionsMultivariate 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.</abstract><type>Journal Article</type><journal>BMC Medical Research Methodology</journal><volume>20</volume><journalNumber>1</journalNumber><paginationStart/><paginationEnd/><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>1471-2288</issnElectronic><keywords>Multivariate; Network meta-analysis; Mixed treatment comparisons; Meta-analysis; Class effect</keywords><publishedDay>8</publishedDay><publishedMonth>7</publishedMonth><publishedYear>2020</publishedYear><publishedDate>2020-07-08</publishedDate><doi>10.1186/s12874-020-01025-8</doi><url/><notes>Availability of data and materials: The dataset analysed during this study is available from the corresponding author on reasonable request.</notes><college>COLLEGE NANME</college><department>Health Data Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>HDAT</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>This article presents independent research funded by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the National Health Service, the NIHR, or the Department of Health. Rhiannon Owen was funded by an NIHR Doctoral Research Fellowship (grant no. NI-DRF-2013-06-062). Keith Abrams is partially supported by Health Data Research (HDR) UK, the UK National Institute for Health Research (NIHR) Applied Research Collaboration East Midlands (ARC EM), and as a NIHR Senior Investigator Emeritus (NF-SI-0512-10159). Sylwia Bujkiewicz was funded by the Medical Research Council (grant no. MR/L009854/1). 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spelling v2 60667 2022-07-28 Multivariate network meta-analysis incorporating class effects 0d30aa00eef6528f763a1e1589f703ec 0000-0001-5977-376X Rhiannon Owen Rhiannon Owen true false 2022-07-28 HDAT BackgroundNetwork 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.MethodsWe 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 did not 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 fitted using Bayesian Markov Chain Monte Carlo (MCMC) methods in WinBUGS.ResultsAll 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.ConclusionsMultivariate 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. Journal Article BMC Medical Research Methodology 20 1 Springer Science and Business Media LLC 1471-2288 Multivariate; Network meta-analysis; Mixed treatment comparisons; Meta-analysis; Class effect 8 7 2020 2020-07-08 10.1186/s12874-020-01025-8 Availability of data and materials: The dataset analysed during this study is available from the corresponding author on reasonable request. COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University This article presents independent research funded by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the National Health Service, the NIHR, or the Department of Health. Rhiannon Owen was funded by an NIHR Doctoral Research Fellowship (grant no. NI-DRF-2013-06-062). Keith Abrams is partially supported by Health Data Research (HDR) UK, the UK National Institute for Health Research (NIHR) Applied Research Collaboration East Midlands (ARC EM), and as a NIHR Senior Investigator Emeritus (NF-SI-0512-10159). Sylwia Bujkiewicz was funded by the Medical Research Council (grant no. MR/L009854/1). The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. 2022-08-04T12:38:13.5044590 2022-07-28T20:31:37.0560600 Swansea University Medical School Medicine Rhiannon Owen 0000-0001-5977-376X 1 Sylwia Bujkiewicz 2 Douglas G. Tincello 3 Keith R. Abrams 4 60667__24842__4b45f849c35c4e5f9530019a17e808d8.pdf 60667.pdf 2022-08-04T12:36:58.6779202 Output 6493526 application/pdf Version of Record true © The Author(s). 2020 This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
title Multivariate network meta-analysis incorporating class effects
spellingShingle Multivariate network meta-analysis incorporating class effects
Rhiannon Owen
title_short Multivariate network meta-analysis incorporating class effects
title_full Multivariate network meta-analysis incorporating class effects
title_fullStr Multivariate network meta-analysis incorporating class effects
title_full_unstemmed Multivariate network meta-analysis incorporating class effects
title_sort Multivariate network meta-analysis incorporating class effects
author_id_str_mv 0d30aa00eef6528f763a1e1589f703ec
author_id_fullname_str_mv 0d30aa00eef6528f763a1e1589f703ec_***_Rhiannon Owen
author Rhiannon Owen
author2 Rhiannon Owen
Sylwia Bujkiewicz
Douglas G. Tincello
Keith R. Abrams
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container_volume 20
container_issue 1
publishDate 2020
institution Swansea University
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doi_str_mv 10.1186/s12874-020-01025-8
publisher Springer Science and Business Media LLC
college_str Swansea University Medical School
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description BackgroundNetwork 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.MethodsWe 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 did not 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 fitted using Bayesian Markov Chain Monte Carlo (MCMC) methods in WinBUGS.ResultsAll 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.ConclusionsMultivariate 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.
published_date 2020-07-08T12:38:12Z
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