Bias propagation in network meta-analysis models.
Bayesian methods
arm-based model
contrast-based model
network meta-analysis
Journal
Research synthesis methods
ISSN: 1759-2887
Titre abrégé: Res Synth Methods
Pays: England
ID NLM: 101543738
Informations de publication
Date de publication:
Mar 2023
Mar 2023
Historique:
revised:
25
09
2022
received:
12
11
2021
accepted:
07
12
2022
pubmed:
13
12
2022
medline:
15
3
2023
entrez:
12
12
2022
Statut:
ppublish
Résumé
Network meta-analysis combines direct and indirect evidence to compare multiple treatments. As direct evidence for one treatment contrast may be indirect evidence for other treatment contrasts, biases in the direct evidence for one treatment contrast may affect not only the estimate for this particular treatment contrast but also estimates of other treatment contrasts. Because network structure determines how direct and indirect evidence are combined and weighted, the impact of biased evidence will be determined by the network geometry. Thus, this study's aim was to investigate how the impact of biased evidence spreads across the whole network and how the propagation of bias is influenced by the network structure. In addition to the popular Lu & Ades model, we also investigate bias propagation in the baseline model and arm-based model to compare the effects of bias in the different models. We undertook extensive simulations under different scenarios to explore how the impact of bias may be affected by the location of the bias, network geometry and the statistical model. Our results showed that the structure of a network has an important impact on how the bias spreads across the network, and this is especially true for the Lu & Ades model. The impact of bias is more likely to be diluted by other unbiased evidence in a well-connected network. We also used a real network meta-analysis to demonstrate how to use the new knowledge about bias propagation to explain questionable results from the original analysis.
Types de publication
Meta-Analysis
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
247-265Subventions
Organisme : Ministry of Science and Technology, Taiwan
ID : MOST 106-2314-B-002-098-MY3
Organisme : Ministry of Science and Technology, Taiwan
ID : MOST 109-2314-B-002 -150-MY3
Organisme : Ministry of Science and Technology
Informations de copyright
© 2022 John Wiley & Sons Ltd.
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