Using individual participant data to improve network meta-analysis projects.
evidence-based practice
policy
Journal
BMJ evidence-based medicine
ISSN: 2515-4478
Titre abrégé: BMJ Evid Based Med
Pays: England
ID NLM: 101719009
Informations de publication
Date de publication:
06 2023
06 2023
Historique:
accepted:
01
07
2022
medline:
24
5
2023
pubmed:
11
8
2022
entrez:
10
8
2022
Statut:
ppublish
Résumé
A network meta-analysis combines the evidence from existing randomised trials about the comparative efficacy of multiple treatments. It allows direct and indirect evidence about each comparison to be included in the same analysis, and provides a coherent framework to compare and rank treatments. A traditional network meta-analysis uses aggregate data (eg, treatment effect estimates and standard errors) obtained from publications or trial investigators. An alternative approach is to obtain, check, harmonise and meta-analyse the individual participant data (IPD) from each trial. In this article, we describe potential advantages of IPD for network meta-analysis projects, emphasising five key benefits: (1) improving the quality and scope of information available for inclusion in the meta-analysis, (2) examining and plotting distributions of covariates across trials (eg, for potential effect modifiers), (3) standardising and improving the analysis of each trial, (4) adjusting for prognostic factors to allow a network meta-analysis of conditional treatment effects and (5) including treatment-covariate interactions (effect modifiers) to allow relative treatment effects to vary by participant-level covariate values (eg, age, baseline depression score). A running theme of all these benefits is that they help examine and reduce heterogeneity (differences in the true treatment effect between trials) and inconsistency (differences in the true treatment effect between direct and indirect evidence) in the network. As a consequence, an IPD network meta-analysis has the potential for more precise, reliable and informative results for clinical practice and even allows treatment comparisons to be made for individual patients and targeted populations conditional on their particular characteristics.
Identifiants
pubmed: 35948411
pii: bmjebm-2022-111931
doi: 10.1136/bmjebm-2022-111931
pmc: PMC10313959
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
197-203Subventions
Organisme : Medical Research Council
ID : MC_UU_00004/06
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/P015298/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/R025223/1
Pays : United Kingdom
Informations de copyright
© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: None declared.
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