BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses

Background Several reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs). We suspect that this issue may be partially attributable to limitations in current NMA software which do not readily produce all of the output needed to satisfy current guidelines....

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Published in:BMC medical research methodology Vol. 19; no. 1; pp. 196 - 13
Main Authors: Béliveau, Audrey, Boyne, Devon J., Slater, Justin, Brenner, Darren, Arora, Paul
Format: Journal Article
Language:English
Published: London BioMed Central 22.10.2019
BioMed Central Ltd
BMC
Subjects:
Bayes Theorem
Bayesian analysis
Bayesian inference
Computational Biology - methods
Data analysis
Health Sciences
Health technology assessment
Humans
Indirect treatment comparison
Knowledge synthesis
Medicine
Medicine & Public Health
Meta-analysis
Methods
Network meta-analysis
Network Meta-Analysis as Topic
R (Programming language)
Software
Statistical software
Statistical Theory and Methods
statistics and modelling
Statistics for Life Sciences
Systematic review
Systematic Reviews as Topic
Technology
Theory of Medicine/Bioethics
Canada
Network meta-analysis
Health technology assessment
Knowledge synthesis
Clinical efficacy
Systematic review
Bayesian inference
Reporting guidelines
Indirect treatment comparison
R package
ISSN:1471-2288, 1471-2288
Online Access:Get full text
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Summary:Background Several reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs). We suspect that this issue may be partially attributable to limitations in current NMA software which do not readily produce all of the output needed to satisfy current guidelines. Results To better facilitate the conduct and reporting of NMAs, we have created an R package called “BUGSnet” ( B ayesian inference U sing G ibbs S ampling to conduct a Net work meta-analysis). This R package relies upon Just Another Gibbs Sampler (JAGS) to conduct Bayesian NMA using a generalized linear model. BUGSnet contains a suite of functions that can be used to describe the evidence network, estimate a model and assess the model fit and convergence, assess the presence of heterogeneity and inconsistency, and output the results in a variety of formats including league tables and surface under the cumulative rank curve (SUCRA) plots. We provide a demonstration of the functions contained within BUGSnet by recreating a Bayesian NMA found in the second technical support document composed by the National Institute for Health and Care Excellence Decision Support Unit (NICE-DSU). We have also mapped these functions to checklist items within current reporting and best practice guidelines. Conclusion BUGSnet is a new R package that can be used to conduct a Bayesian NMA and produce all of the necessary output needed to satisfy current scientific and regulatory standards. We hope that this software will help to improve the conduct and reporting of NMAs.
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ISSN:1471-2288
1471-2288
DOI:10.1186/s12874-019-0829-2