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Three-dimensional evidence network plot system: covariate imbalances and effects in network meta-analysis explored using a new software tool

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journal contribution
posted on 2017-09-04, 14:37 authored by Sarah Batson, Robert Score, Alex J. Sutton
OBJECTIVES: The aim of the study was to develop the three-dimensional (3D) evidence network plot system-a novel web-based interactive 3D tool to facilitate the visualization and exploration of covariate distributions and imbalances across evidence networks for network meta-analysis (NMA). STUDY DESIGN AND SETTING: We developed the 3D evidence network plot system within an AngularJS environment using a third party JavaScript library (Three.js) to create the 3D element of the application. Data used to enable the creation of the 3D element for a particular topic are inputted via a Microsoft Excel template spreadsheet that has been specifically formatted to hold these data. We display and discuss the findings of applying the tool to two NMA examples considering multiple covariates. These two examples have been previously identified as having potentially important covariate effects and allow us to document the various features of the tool while illustrating how it can be used. RESULTS: The 3D evidence network plot system provides an immediate, intuitive, and accessible way to assess the similarity and differences between the values of covariates for individual studies within and between each treatment contrast in an evidence network. In this way, differences between the studies, which may invalidate the usual assumptions of an NMA, can be identified for further scrutiny. Hence, the tool facilitates NMA feasibility/validity assessments and aids in the interpretation of NMA results. CONCLUSION: The 3D evidence network plot system is the first tool designed specifically to visualize covariate distributions and imbalances across evidence networks in 3D. This will be of primary interest to systematic review and meta-analysis researchers and, more generally, those assessing the validity and robustness of an NMA to inform reimbursement decisions.

History

Citation

Journal of Clinical Epidemiology, 2017, 86, pp. 182-195

Author affiliation

/Organisation/COLLEGE OF MEDICINE, BIOLOGICAL SCIENCES AND PSYCHOLOGY/School of Medicine/Department of Health Sciences

Version

  • AM (Accepted Manuscript)

Published in

Journal of Clinical Epidemiology

Publisher

Elsevier

issn

0895-4356

eissn

1878-5921

Acceptance date

2017-03-17

Copyright date

2017

Available date

2018-03-24

Publisher version

http://www.sciencedirect.com/science/article/pii/S0895435616308459?via=ihub

Notes

The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.

Language

en

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