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Hierarchical network meta-analysis models for synthesis of evidence from randomised and non-randomised studies.pdf (2.25 MB)

Hierarchical network meta-analysis models for synthesis of evidence from randomised and non-randomised studies

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posted on 2023-05-25, 09:09 authored by H Hussein, KR Abrams, LJ Gray, S Anwer, S Dias, S Bujkiewicz

Background

With the increased interest in the inclusion of non-randomised data in network meta-analyses (NMAs) of randomised controlled trials (RCTs), analysts need to consider the implications of the differences in study designs as such data can be prone to increased bias due to the lack of randomisation and unmeasured confounding. This study aims to explore and extend a number of NMA models that account for the differences in the study designs, assessing their impact on the effect estimates and uncertainty.


Methods

Bayesian random-effects meta-analytic models, including naïve pooling and hierarchical models differentiating between the study designs, were extended to allow for the treatment class effect and accounting for bias, with further extensions allowing for bias terms to vary depending on the treatment class. Models were applied to an illustrative example in type 2 diabetes; using data from a systematic review of RCTs and non-randomised studies of two classes of glucose-lowering medications: sodium-glucose co-transporter 2 inhibitors and glucagon-like peptide-1 receptor agonists.


Results

Across all methods, the estimated mean differences in glycated haemoglobin after 24 and 52 weeks remained similar with the inclusion of observational data. The uncertainty around these estimates reduced when conducting naïve pooling, compared to NMA of RCT data alone, and remained similar when applying hierarchical model allowing for class effect. However, the uncertainty around these effect estimates increased when fitting hierarchical models allowing for the differences in study design. The impact on uncertainty varied between treatments when applying the bias adjustment models. Hierarchical models and bias adjustment models all provided a better fit in comparison to the naïve-pooling method.


Conclusions

Hierarchical and bias adjustment NMA models accounting for study design may be more appropriate when conducting a NMA of RCTs and observational studies. The degree of uncertainty around the effectiveness estimates varied depending on the method but use of hierarchical models accounting for the study design resulted in increased uncertainty. Inclusion of non-randomised data may, however, result in inferences that are more generalisable and the models accounting for the differences in the study design allow for more detailed and appropriate modelling of complex data, preventing overly optimistic conclusions.

Funding

HOD1: Inferring relative treatment effects from combined randomised and observational data

Medical Research Council

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National Institute for Health and Care Research (NIHR) Applied Research Collaboration East Midlands (ARC EM) and Leicester NIHR Biomedical Research Centre (BRC)

History

Author affiliation

Department of Population Health Sciences, University of Leicester

Version

  • VoR (Version of Record)

Published in

BMC medical research methodology

Volume

23

Issue

1

Pagination

97

Publisher

Springer Science and Business Media LLC

issn

1471-2288

eissn

1471-2288

Copyright date

2023

Available date

2023-05-25

Spatial coverage

England

Language

eng

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