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Network Meta-Analysis: Development of a Three-Level Hierarchical Modeling Approach Incorporating Dose-Related Constraints

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

Value in Health, Volume: 18, Issue: 1, Pages: 116 - 126

Swansea University Author: Rhiannon Owen Orcid Logo

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DOI (Published version): 10.1016/j.jval.2014.10.006

Abstract

BackgroundNetwork meta-analysis (NMA) is commonly used in evidence synthesis; however, in situations in which there are a large number of treatment options, which may be subdivided into classes, and relatively few trials, NMAs produce considerable uncertainty in the estimated treatment effects, and...

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Published in: Value in Health
ISSN: 1098-3015
Published: Elsevier BV 2015
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URI: https://cronfa.swan.ac.uk/Record/cronfa60673
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spelling 2022-08-04T12:05:42.9920674 v2 60673 2022-07-28 Network Meta-Analysis: Development of a Three-Level Hierarchical Modeling Approach Incorporating Dose-Related Constraints 0d30aa00eef6528f763a1e1589f703ec 0000-0001-5977-376X Rhiannon Owen Rhiannon Owen true false 2022-07-28 MEDS BackgroundNetwork meta-analysis (NMA) is commonly used in evidence synthesis; however, in situations in which there are a large number of treatment options, which may be subdivided into classes, and relatively few trials, NMAs produce considerable uncertainty in the estimated treatment effects, and consequently, identification of the most beneficial intervention remains inconclusive.ObjectiveTo develop and demonstrate the use of evidence synthesis methods to evaluate extensive treatment networks with a limited number of trials, making use of classes.MethodsUsing Bayesian Markov chain Monte Carlo methods, we build on the existing work of a random effects NMA to develop a three-level hierarchical NMA model that accounts for the exchangeability between treatments within the same class as well as for the residual between-study heterogeneity. We demonstrate the application of these methods to a continuous and binary outcome, using a motivating example of overactive bladder. We illustrate methods for incorporating ordering constraints in increasing doses, model selection, and assessing inconsistency between the direct and indirect evidence.ResultsThe methods were applied to a data set obtained from a systematic literature review of trials for overactive bladder, evaluating the mean reduction in incontinence episodes from baseline and the number of patients reporting one or more adverse events. The data set involved 72 trials comparing 34 interventions that were categorized into nine classes of interventions, including placebo.ConclusionsBayesian three-level hierarchical NMAs have the potential to increase the precision in the effect estimates while maintaining the interpretability of the individual interventions for decision making. Journal Article Value in Health 18 1 116 126 Elsevier BV 1098-3015 network meta-analysis; statistical methods; mixed treatment comparisons; overactive bladder 1 1 2015 2015-01-01 10.1016/j.jval.2014.10.006 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University 2022-08-04T12:05:42.9920674 2022-07-28T20:33:57.2895568 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Rhiannon Owen 0000-0001-5977-376X 1 Douglas G. Tincello 2 R. Abrams Keith 3 60673__24839__aeb57adca0854e6bab93bd0952b015ba.pdf 60673.pdf 2022-08-04T12:04:28.2393126 Output 1360035 application/pdf Version of Record true Copyright 2015: This is an open access article under the CC BY-NC-ND license true eng http://creativecommons.org/licenses/by-nc-nd/3.0/
title Network Meta-Analysis: Development of a Three-Level Hierarchical Modeling Approach Incorporating Dose-Related Constraints
spellingShingle Network Meta-Analysis: Development of a Three-Level Hierarchical Modeling Approach Incorporating Dose-Related Constraints
Rhiannon Owen
title_short Network Meta-Analysis: Development of a Three-Level Hierarchical Modeling Approach Incorporating Dose-Related Constraints
title_full Network Meta-Analysis: Development of a Three-Level Hierarchical Modeling Approach Incorporating Dose-Related Constraints
title_fullStr Network Meta-Analysis: Development of a Three-Level Hierarchical Modeling Approach Incorporating Dose-Related Constraints
title_full_unstemmed Network Meta-Analysis: Development of a Three-Level Hierarchical Modeling Approach Incorporating Dose-Related Constraints
title_sort Network Meta-Analysis: Development of a Three-Level Hierarchical Modeling Approach Incorporating Dose-Related Constraints
author_id_str_mv 0d30aa00eef6528f763a1e1589f703ec
author_id_fullname_str_mv 0d30aa00eef6528f763a1e1589f703ec_***_Rhiannon Owen
author Rhiannon Owen
author2 Rhiannon Owen
Douglas G. Tincello
R. Abrams Keith
format Journal article
container_title Value in Health
container_volume 18
container_issue 1
container_start_page 116
publishDate 2015
institution Swansea University
issn 1098-3015
doi_str_mv 10.1016/j.jval.2014.10.006
publisher Elsevier BV
college_str Faculty of Medicine, Health and Life Sciences
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hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
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description BackgroundNetwork meta-analysis (NMA) is commonly used in evidence synthesis; however, in situations in which there are a large number of treatment options, which may be subdivided into classes, and relatively few trials, NMAs produce considerable uncertainty in the estimated treatment effects, and consequently, identification of the most beneficial intervention remains inconclusive.ObjectiveTo develop and demonstrate the use of evidence synthesis methods to evaluate extensive treatment networks with a limited number of trials, making use of classes.MethodsUsing Bayesian Markov chain Monte Carlo methods, we build on the existing work of a random effects NMA to develop a three-level hierarchical NMA model that accounts for the exchangeability between treatments within the same class as well as for the residual between-study heterogeneity. We demonstrate the application of these methods to a continuous and binary outcome, using a motivating example of overactive bladder. We illustrate methods for incorporating ordering constraints in increasing doses, model selection, and assessing inconsistency between the direct and indirect evidence.ResultsThe methods were applied to a data set obtained from a systematic literature review of trials for overactive bladder, evaluating the mean reduction in incontinence episodes from baseline and the number of patients reporting one or more adverse events. The data set involved 72 trials comparing 34 interventions that were categorized into nine classes of interventions, including placebo.ConclusionsBayesian three-level hierarchical NMAs have the potential to increase the precision in the effect estimates while maintaining the interpretability of the individual interventions for decision making.
published_date 2015-01-01T07:58:10Z
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