Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/32974
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dc.contributor.authorOwen, Rhiannon K.-
dc.contributor.authorTincello, Douglas G.-
dc.contributor.authorKeith, R. Abrams-
dc.date.accessioned2015-08-24T14:12:10Z-
dc.date.available2015-08-24T14:12:10Z-
dc.date.issued2015-01-13-
dc.identifier.citationValue in Health, 2015, 18 (1), pp. 116-126en
dc.identifier.issn1098-3015-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S1098301514047317#en
dc.identifier.urihttp://hdl.handle.net/2381/32974-
dc.description.abstractBACKGROUND: Network 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. OBJECTIVE: To develop and demonstrate the use of evidence synthesis methods to evaluate extensive treatment networks with a limited number of trials, making use of classes. METHODS: Using 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. RESULTS: The 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. CONCLUSIONS: Bayesian 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.en
dc.language.isoenen
dc.publisherElsevier for International Society for Pharmacoeconomics and Outcomes Researchen
dc.relation.urihttp://www.ncbi.nlm.nih.gov/pubmed/25595242-
dc.rightsCopyright © the authors, 2014. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.en
dc.subjectmixed treatment comparisonsen
dc.subjectnetwork meta-analysisen
dc.subjectoveractive bladderen
dc.subjectstatistical methodsen
dc.subjectBayes Theoremen
dc.subjectCholinergic Antagonistsen
dc.subjectClinical Trials as Topicen
dc.subjectDose-Response Relationship, Drugen
dc.subjectHumansen
dc.subjectMarkov Chainsen
dc.subjectModels, Biologicalen
dc.subjectMonte Carlo Methoden
dc.subjectUrinary Bladder, Overactiveen
dc.titleNetwork meta-analysis: development of a three-level hierarchical modeling approach incorporating dose-related constraintsen
dc.typeJournal Articleen
dc.identifier.doi10.1016/j.jval.2014.10.006-
dc.identifier.eissn1524-4733-
dc.identifier.piiS1098-3015(14)04731-7-
dc.description.statusPeer-revieweden
dc.description.versionPublisher Versionen
dc.type.subtypeJournal Article;Meta-Analysis;Research Support, Non-U.S. Gov't-
pubs.organisational-group/Organisationen
pubs.organisational-group/Organisation/COLLEGE OF MEDICINE, BIOLOGICAL SCIENCES AND PSYCHOLOGYen
pubs.organisational-group/Organisation/COLLEGE OF MEDICINE, BIOLOGICAL SCIENCES AND PSYCHOLOGY/School of Medicineen
pubs.organisational-group/Organisation/COLLEGE OF MEDICINE, BIOLOGICAL SCIENCES AND PSYCHOLOGY/School of Medicine/Department of Cancer Studies and Molecular Medicineen
pubs.organisational-group/Organisation/COLLEGE OF MEDICINE, BIOLOGICAL SCIENCES AND PSYCHOLOGY/School of Medicine/Department of Health Sciencesen
pubs.organisational-group/Organisation/COLLEGE OF MEDICINE, BIOLOGICAL SCIENCES AND PSYCHOLOGY/Themesen
pubs.organisational-group/Organisation/COLLEGE OF MEDICINE, BIOLOGICAL SCIENCES AND PSYCHOLOGY/Themes/Microbial Scienceen
pubs.organisational-group/Organisation/COLLEGE OF MEDICINE, BIOLOGICAL SCIENCES AND PSYCHOLOGY/Themes/Molecular & Cellular Bioscienceen
Appears in Collections:Published Articles, Dept. of Health Sciences

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