BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses.
BMC Med Res Methodol
; 19(1): 196, 2019 10 22.
Article
em En
| MEDLINE
| ID: mdl-31640567
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.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Software
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Metanálise como Assunto
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Biologia Computacional
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Revisões Sistemáticas como Assunto
Tipo de estudo:
Guideline
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Health_technology_assessment
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Prognostic_studies
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Systematic_reviews
Limite:
Humans
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article