Component network meta-analysis methods for combining individual participant data and aggregate data.

Authors

Ellesha A Smith, Stephanie J Hubbard, Suzanne C Freeman, Nicola J Cooper, Laura J Gray

Published in

Research synthesis methods. Pages 1-18. Sep 17, 2026. Epub Sep 17, 2026.

Abstract

Component network meta-analysis (CNMA) is an evidence synthesis method that can estimate the effectiveness of components and component combinations of complex interventions. Individual participant data (IPD) meta-analysis is considered the gold standard for evaluating medical interventions. In this article, we propose CNMA models that simultaneously synthesise IPD and aggregate data to evaluate the effectiveness of individual components and combinations of components. We extend the models to adjust for individual-level covariates, by separating the within-study and between-study interaction effects. The methods are applied to a Cochrane systematic review dataset of 11 studies, of which eight have IPD available, to evaluate the effectiveness of complex home safety practices for promoting the safe storage of cleaning products to prevent childhood poisonings. Our findings demonstrate the benefits of combining IPD with aggregate data beyond a standard network meta-analysis or CNMA based solely on aggregate data, such as improved precision, changed conclusions, and addressing aggregation bias. For models without covariate adjustment, inclusion of IPD reduced the between-study heterogeneity and reduced the width of credible intervals (CrIs). However, across analyses, many CrIs are wide, particularly in models that separate the within- and between-study interactions. The models proposed in this article maximise the use of available evidence and provide valuable insights into which combinations of components are potentially the most effective and in which subgroups of the population. Further research is required to assess the generalisability of CNMA that combine IPD and aggregate data and their potential to enhance healthcare decision making.

PMID:
42750471
Bibliographic data and abstract were imported from PubMed on 17 Sep 2026.

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