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Accounting for correlation in network meta-analysis with multi-arm trials
A J Franchini1, S Dias2, A E Ades2
1School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, UK. angelo.franchini@bristol.ac.uk.
Research Synthesis Methods
|June 11, 2015
Summary
Network meta-analysis (NMA) using multi-arm trials requires accounting for arm correlations. Failing to do so with contrast-level summaries can lead to incorrect results, impacting evidence synthesis.
Area of Science:
- Biostatistics
- Clinical Epidemiology
- Health Economics
Background:
- Multi-arm trials offer valuable evidence for network meta-analysis (NMA).
- Trial data can be reported as arm-level or contrast-level summaries.
- Contrast-level summaries compare treatment arms against a trial-specific control.
Purpose of the Study:
- To highlight the importance of accounting for correlations in multi-arm trials within NMA.
- To demonstrate how contrast-level summaries can yield incorrect results if correlations are ignored.
- To review software and methods for handling correlations in NMA.
Main Methods:
- Likelihood-based inference was compared between arm-level and contrast-level data formats.
- The impact of ignoring correlations in multi-arm trials was analyzed.
- Bayesian and frequentist NMA software capable of handling correlations were reviewed.
Main Results:
- For two-arm trials, arm-level and contrast-level inference are identical.
- For multi-arm trials, contrast-level inference is incorrect without accounting for correlations.
- Ignoring correlations can introduce significant differences in NMA estimates.
Conclusions:
- Accurate NMA with multi-arm trials necessitates incorporating correlations.
- Trialists should report control arm standard errors to facilitate correlation imputation.
- Software exists to correctly perform NMA with multi-arm trials, accounting for correlations.
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