BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses
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 gu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-10-01
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| Series: | BMC Medical Research Methodology |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12874-019-0829-2 |
| _version_ | 1818047621992808448 |
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| author | Audrey Béliveau Devon J. Boyne Justin Slater Darren Brenner Paul Arora |
| author_facet | Audrey Béliveau Devon J. Boyne Justin Slater Darren Brenner Paul Arora |
| author_sort | Audrey Béliveau |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T10:08:43Z |
| format | Article |
| id | doaj.art-02389a946cad4bfd933f0f7016499d2c |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-12-10T10:08:43Z |
| publishDate | 2019-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-02389a946cad4bfd933f0f7016499d2c2022-12-22T01:53:10ZengBMCBMC Medical Research Methodology1471-22882019-10-0119111310.1186/s12874-019-0829-2BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analysesAudrey Béliveau0Devon J. Boyne1Justin Slater2Darren Brenner3Paul Arora4Department of Statistics and Actuarial Science, University of WaterlooDivision of Analytics, Lighthouse OutcomesDivision of Analytics, Lighthouse OutcomesDivision of Analytics, Lighthouse OutcomesDivision of Analytics, Lighthouse OutcomesAbstract 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.http://link.springer.com/article/10.1186/s12874-019-0829-2Network meta-analysisIndirect treatment comparisonSystematic reviewBayesian inferenceKnowledge synthesisHealth technology assessment |
| spellingShingle | Audrey Béliveau Devon J. Boyne Justin Slater Darren Brenner Paul Arora BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses BMC Medical Research Methodology Network meta-analysis Indirect treatment comparison Systematic review Bayesian inference Knowledge synthesis Health technology assessment |
| title | BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses |
| title_full | BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses |
| title_fullStr | BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses |
| title_full_unstemmed | BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses |
| title_short | BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses |
| title_sort | bugsnet an r package to facilitate the conduct and reporting of bayesian network meta analyses |
| topic | Network meta-analysis Indirect treatment comparison Systematic review Bayesian inference Knowledge synthesis Health technology assessment |
| url | http://link.springer.com/article/10.1186/s12874-019-0829-2 |
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