The hierarchical metaregression approach and learning from clinical evidence.
Bayesian hierarchical models
comparative effectiveness
conflict of evidence
cross-design synthesis
individual participant data
meta-analysis
personalized medicine
Journal
Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048
Informations de publication
Date de publication:
05 2019
05 2019
Historique:
received:
15
11
2017
revised:
15
10
2018
accepted:
16
10
2018
pubmed:
3
1
2019
medline:
6
2
2020
entrez:
3
1
2019
Statut:
ppublish
Résumé
The hierarchical metaregression (HMR) approach is a multiparameter Bayesian approach for meta-analysis, which generalizes the standard mixed effects models by explicitly modeling the data collection process in the meta-analysis. The HMR allows to investigate the potential external validity of experimental results as well as to assess the internal validity of the studies included in a systematic review. The HMR automatically identifies studies presenting conflicting evidence and it downweights their influence in the meta-analysis. In addition, the HMR allows to perform cross-evidence synthesis, which combines aggregated results from randomized controlled trials to predict effectiveness in a single-arm observational study with individual participant data (IPD). In this paper, we evaluate the HMR approach using simulated data examples. We present a new real case study in diabetes research, along with a new R package called jarbes (just a rather Bayesian evidence synthesis), which automatizes the complex computations involved in the HMR.
Identifiants
pubmed: 30600534
doi: 10.1002/bimj.201700266
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
535-557Informations de copyright
© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.