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Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies
J P T Higgins1,2, D Jackson3, J K Barrett3
1MRC Biostatistics Unit, Cambridge, UK. julian.higgins@mrc-bsu.cam.ac.uk.
Research Synthesis Methods
|June 11, 2015
Summary
Network meta-analyses assess evidence compatibility. This study introduces design-by-treatment interactions to investigate inconsistency, improving analysis of multi-arm trials and smoking cessation interventions.
Area of Science:
- Biostatistics
- Evidence Synthesis
- Health Research Methodology
Background:
- Network meta-analyses (NMA) are increasingly common for comparing multiple treatments.
- Assessing the compatibility of evidence sources is crucial in NMA.
- Simple indirect comparisons can be confounded by differences between studies.
Purpose of the Study:
- To discuss methods for addressing inconsistency in comparative evidence.
- To introduce a general framework for investigating inconsistency using design-by-treatment interactions.
- To address complications arising from multi-arm trials in evidence networks.
Main Methods:
- Defining and reviewing heterogeneity and inconsistency.
- Distinguishing between 'loop inconsistency' and 'design inconsistency'.
- Proposing design-by-treatment interactions as a framework for inconsistency investigation.
- Illustrating novel graphical methods for depicting evidence networks and potential inconsistencies.
Main Results:
- The design-by-treatment interaction framework effectively handles multi-arm trials.
- The Lu-Ades inconsistency model is a restricted case of the proposed model.
- Multiple distinct Lu-Ades models may exist for a given dataset.
- Addressing only loop inconsistency can lead to problems, as shown in smoking cessation intervention data.
Conclusions:
- Design-by-treatment interactions offer a robust framework for analyzing evidence inconsistency in NMAs.
- The proposed methods provide a more comprehensive approach than solely addressing loop inconsistency.
- Graphical methods enhance the visualization of evidence networks and potential inconsistencies.
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