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MetaInsight: An interactive web‐based tool for analyzing, interrogating, and visualizing network meta‐analyses using R‐shiny and netmeta

Rhiannon Owen Orcid Logo, Naomi Bradbury, Yiqiao Xin, Nicola Cooper, Alex Sutton

Research Synthesis Methods, Volume: 10, Issue: 4, Pages: 569 - 581

Swansea University Author: Rhiannon Owen Orcid Logo

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DOI (Published version): 10.1002/jrsm.1373

Abstract

BackgroundNetwork meta-analysis (NMA) is a powerful analysis method used to identify the best treatments for a condition and is used extensively by health care decision makers. Although software routines exist for conducting NMA, they require considerable statistical programming expertise to use, wh...

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Published in: Research Synthesis Methods
ISSN: 1759-2879 1759-2887
Published: Wiley 2019
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa60669
Abstract: BackgroundNetwork meta-analysis (NMA) is a powerful analysis method used to identify the best treatments for a condition and is used extensively by health care decision makers. Although software routines exist for conducting NMA, they require considerable statistical programming expertise to use, which limits the number of researchers able to conduct such analyses.ObjectivesTo develop a web-based tool allowing users with only standard internet browser software to be able to conduct NMAs using an intuitive “point and click” interface and present the results using visual plots.MethodsUsing the existing netmeta and Shiny packages for R to conduct the analyses, and to develop the user interface, we created the MetaInsight tool which is freely available to use via the web.ResultsA package was created for conducting NMA which satisfied our objectives, and this is described, and its application demonstrated, using an illustrative example.ConclusionsWe believe that many researchers will find our package helpful for facilitating NMA as well as allowing decision makers to scrutinize presented results visually and in real time. This will impact on the relevance of statistical analyses for health care decision making and sustainably increase capacity by empowering informed nonspecialists to be able to conduct more clinically relevant reviews. It is also hoped that others will be inspired to create similar tools for other advanced specialist analyses methods using the freely available technologies we have adopted.
College: Faculty of Medicine, Health and Life Sciences
Funders: Midlands Integrative Biosciences Training Partnership studentship. Grant Number: BB/M01116X/1; National Institute for Health Research. Grant Number: 14/178/29
Issue: 4
Start Page: 569
End Page: 581

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