Using the bayesmeta R package for Bayesian random-effects meta-regression.

Bayes Theorem Computer Simulation
Covariables Heterogeneity Meta-analysis Moderators Subgroup analysis

Journal

Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513

Informations de publication

Date de publication:
Feb 2023
Historique:
received: 06 10 2022
revised: 25 11 2022
accepted: 07 12 2022
pubmed: 26 12 2022
medline: 8 2 2023
entrez: 25 12 2022
Statut: ppublish

Résumé

Random-effects meta-analysis within a hierarchical normal modeling framework is commonly implemented in a wide range of evidence synthesis applications. More general problems may even be tackled when considering meta-regression approaches that in addition allow for the inclusion of study-level covariables. We describe the Bayesian meta-regression implementation provided in the bayesmetaR package including the choice of priors, and we illustrate its practical use. A wide range of example applications are given, such as binary and continuous covariables, subgroup analysis, indirect comparisons, and model selection. Example R code is provided. The bayesmeta package provides a flexible implementation. Due to the avoidance of MCMC methods, computations are fast and reproducible, facilitating quick sensitivity checks or large-scale simulation studies.

Sections du résumé

BACKGROUND BACKGROUND
Random-effects meta-analysis within a hierarchical normal modeling framework is commonly implemented in a wide range of evidence synthesis applications. More general problems may even be tackled when considering meta-regression approaches that in addition allow for the inclusion of study-level covariables.
METHODS METHODS
We describe the Bayesian meta-regression implementation provided in the bayesmetaR package including the choice of priors, and we illustrate its practical use.
RESULTS RESULTS
A wide range of example applications are given, such as binary and continuous covariables, subgroup analysis, indirect comparisons, and model selection. Example R code is provided.
CONCLUSIONS CONCLUSIONS
The bayesmeta package provides a flexible implementation. Due to the avoidance of MCMC methods, computations are fast and reproducible, facilitating quick sensitivity checks or large-scale simulation studies.

Identifiants

DOI: 10.1016/j.cmpb.2022.107303 PMID: 36566650
pubmed: 36566650
pii: S0169-2607(22)00684-8
doi: 10.1016/j.cmpb.2022.107303
pii:
doi:

Types de publication

Meta-Analysis Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

107303

Informations de copyright

Copyright © 2022. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare no conflict of interest.

Auteurs

Christian Röver (C)

Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany. Electronic address: christian.roever@med.uni-goettingen.de.

Tim Friede (T)

Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany.

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Classifications MeSH

questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Probabilité › Théorème de Bayes › Using the bayesmeta R package for Bayesian...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Simulation numérique › Using the bayesmeta R package for Bayesian...