Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/38457
Title: Exploratory Network Meta Regression Analysis of Stroke Prevention in Atrial Fibrillation Fails to Identify Any Interactions with Treatment Effect.
Authors: Batson, S.
Sutton, Alex
Abrams, Keith
First Published: 25-Aug-2016
Publisher: Public Library of Science
Citation: PLoS ONE 11 (8): e0161864. 2016
Abstract: BACKGROUND: Patients with atrial fibrillation are at a greater risk of stroke and therefore the main goal for treatment of patients with atrial fibrillation is to prevent stroke from occurring. There are a number of different stroke prevention treatments available to include warfarin and novel oral anticoagulants. Previous network meta-analyses of novel oral anticoagulants for stroke prevention in atrial fibrillation acknowledge the limitation of heterogeneity across the included trials but have not explored the impact of potentially important treatment modifying covariates. OBJECTIVES: To explore potentially important treatment modifying covariates using network meta-regression analyses for stroke prevention in atrial fibrillation. METHODS: We performed a network meta-analysis for the outcome of ischaemic stroke and conducted an exploratory regression analysis considering potentially important treatment modifying covariates. These covariates included the proportion of patients with a previous stroke, proportion of males, mean age, the duration of study follow-up and the patients underlying risk of ischaemic stroke. RESULTS: None of the covariates explored impacted relative treatment effects relative to placebo. Notably, the exploration of 'study follow-up' as a covariate supported the assumption that difference in trial durations is unimportant in this indication despite the variation across trials in the network. CONCLUSION: This study is limited by the quantity of data available. Further investigation is warranted, and, as justifying further trials may be difficult, it would be desirable to obtain individual patient level data (IPD) to facilitate an effort to relate treatment effects to IPD covariates in order to investigate heterogeneity. Observational data could also be examined to establish if there are potential trends elsewhere. The approach and methods presented have potentially wide applications within any indication as to highlight the potential benefit of extending decision problems to include additional comparators outside of those of primary interest to allow for the exploration of heterogeneity.
DOI Link: 10.1371/journal.pone.0161864
eISSN: 1932-6203
Links: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0161864
http://hdl.handle.net/2381/38457
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: © 2016 Batson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Description: S1 File. Supporting Information. doi:10.1371/journal.pone.0161864.s001 (DOCX)
Appears in Collections:Published Articles, Dept. of Health Sciences

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