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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Abstract	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.
AbstractList	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. To better facilitate the conduct and reporting of NMAs, we have created an R package called "BUGSnet" (Bayesian inference Using Gibbs Sampling to conduct a Network 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. 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. 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.BACKGROUNDSeveral 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.To better facilitate the conduct and reporting of NMAs, we have created an R package called "BUGSnet" (Bayesian inference Using Gibbs Sampling to conduct a Network 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.RESULTSTo better facilitate the conduct and reporting of NMAs, we have created an R package called "BUGSnet" (Bayesian inference Using Gibbs Sampling to conduct a Network 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.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.CONCLUSIONBUGSnet 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. 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. To better facilitate the conduct and reporting of NMAs, we have created an R package called "BUGSnet" (Bayesian inference Using Gibbs Sampling to conduct a Network 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. 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. Abstract 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” (Bayesian inference Using Gibbs Sampling to conduct a Network 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. 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" (Bayesian inference Using Gibbs Sampling to conduct a Network 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. Keywords: Network meta-analysis, Indirect treatment comparison, Systematic review, Bayesian inference, Knowledge synthesis, Health technology assessment, Clinical efficacy, R package, Reporting guidelines 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.
ArticleNumber	196
Audience	Academic
Author	Brenner, Darren Boyne, Devon J. Béliveau, Audrey Arora, Paul Slater, Justin
Author_xml	– sequence: 1 givenname: Audrey orcidid: 0000-0003-4124-2498 surname: Béliveau fullname: Béliveau, Audrey email: audrey.beliveau@waterloo.ca organization: Department of Statistics and Actuarial Science, University of Waterloo – sequence: 2 givenname: Devon J. surname: Boyne fullname: Boyne, Devon J. organization: Division of Analytics, Lighthouse Outcomes, Department of Community Health Sciences, University of Calgary – sequence: 3 givenname: Justin surname: Slater fullname: Slater, Justin organization: Division of Analytics, Lighthouse Outcomes – sequence: 4 givenname: Darren surname: Brenner fullname: Brenner, Darren organization: Division of Analytics, Lighthouse Outcomes, Department of Community Health Sciences, University of Calgary, Department of Oncology, University of Calgary – sequence: 5 givenname: Paul surname: Arora fullname: Arora, Paul organization: Division of Analytics, Lighthouse Outcomes, Dalla Lana School of Public Health, University of Toronto
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31640567$$D View this record in MEDLINE/PubMed
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Keywords	Network meta-analysis Health technology assessment Knowledge synthesis Clinical efficacy Systematic review Bayesian inference Reporting guidelines Indirect treatment comparison R package
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References	B Hutton (829_CR11) 2015; 162 C Xu (829_CR14) 2018; 11 JP Jansen (829_CR1) 2011; 14 829_CR12 G van Valkenhoef (829_CR15) 2012; 3 HC Bucher (829_CR27) 1997; 50 829_CR18 AW Lee (829_CR3) 2014; 67 829_CR19 S Dias (829_CR26) 2011 S Donegan (829_CR7) 2010; 5 JP Higgins (829_CR21) 2002; 21 J Jaime Caro (829_CR29) 2014; 17 B Hutton (829_CR5) 2014; 9 F Song (829_CR6) 2009; 338 S Dias (829_CR17) 2011 WJ Elliott (829_CR31) 2007; 369 AA Veroniki (829_CR24) 2013; 42 A Nikolakopoulou (829_CR32) 2018; 360 O Efthimiou (829_CR2) 2016; 7 A Bafeta (829_CR4) 2014; 348 B Kovic (829_CR8) 2017; 88 B Neupane (829_CR13) 2014; 9 S Donegan (829_CR25) 2013; 4 S Brown (829_CR16) 2014; 3 DE Warn (829_CR23) 2002; 21 S Dias (829_CR28) 2011 G Schwarzer (829_CR30) 2007; 7 W Zarin (829_CR9) 2017; 15 JP Jansen (829_CR20) 2013; 11 Sofia Dias (829_CR22) 2018 JP Jansen (829_CR10) 2014; 17
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Snippet	Background Several reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs). We suspect that this issue may be... Several reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs). We suspect that this issue may be partially... Background Several reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs). We suspect that this issue may be... Abstract Background Several reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs). We suspect that this issue may...
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