Synthesizing cross-design evidence and cross-format data using network meta-regression.
observational studies
randomized controlled trials
real-world evidence
risk of bias
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:
28
10
2022
received:
15
03
2022
accepted:
01
12
2022
pubmed:
11
1
2023
medline:
15
3
2023
entrez:
10
1
2023
Statut:
ppublish
Résumé
In network meta-analysis (NMA), we synthesize all relevant evidence about health outcomes with competing treatments. The evidence may come from randomized clinical trials (RCT) or non-randomized studies (NRS) as individual participant data (IPD) or as aggregate data (AD). We present a suite of Bayesian NMA and network meta-regression (NMR) models allowing for cross-design and cross-format synthesis. The models integrate a three-level hierarchical model for synthesizing IPD and AD into four approaches. The four approaches account for differences in the design and risk of bias (RoB) in the RCT and NRS evidence. These four approaches variously ignoring differences in RoB, using NRS to construct penalized treatment effect priors and bias-adjustment models that control the contribution of information from high RoB studies in two different ways. We illustrate the methods in a network of three pharmacological interventions and placebo for patients with relapsing-remitting multiple sclerosis. The estimated relative treatment effects do not change much when we accounted for differences in design and RoB. Conducting network meta-regression showed that intervention efficacy decreases with increasing participant age. We also re-analysed a network of 431 RCT comparing 21 antidepressants, and we did not observe material changes in intervention efficacy when adjusting for studies' high RoB. We re-analysed both case studies accounting for different study RoB. In summary, the described suite of NMA/NMR models enables the inclusion of all relevant evidence while incorporating information on the within-study bias in both observational and experimental data and enabling estimation of individualized treatment effects through the inclusion of participant characteristics.
Substances chimiques
Antidepressive Agents
0
Types de publication
Meta-Analysis
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
283-300Subventions
Organisme : The HTx project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 825162. This dissemination reflects only the author's view and the Commission is not responsible for any use that may be made of the information it contains.
Informations de copyright
© 2023 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.
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