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Data visualisation approaches for component network meta-analysis: visualising the data structure.

Suzanne C Freeman1,2, Elnaz Saeedi3,4, José M Ordóñez-Mena5

  • 1Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, UK. suzanne.freeman@leicester.ac.uk.

BMC Medical Research Methodology
|September 15, 2023
PubMed
Summary
This summary is machine-generated.

New visualization tools, including CNMA-UpSet plots, heat maps, and circle plots, improve the understanding of complex evidence networks for component network meta-analysis (CNMA). These methods aid in synthesizing multicomponent interventions more effectively.

Keywords:
Complex interventionsComponent network meta-analysisData visualisationGraphical displaysMeta-analysisMulticomponent interventionsPresentational tools

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Medical Informatics

Background:

  • Health and social care interventions are frequently complex, comprising multiple components.
  • Multicomponent interventions are often evaluated using randomized controlled trials, with common components across trials alongside unique ones.
  • Component network meta-analysis (CNMA) synthesizes evidence from such interventions but faces challenges in visualizing complex evidence networks.

Purpose of the Study:

  • To develop novel tools for visualizing the structure of complex evidence networks to support component network meta-analysis (CNMA).

Main Methods:

  • A citation review identified 34 articles reporting CNMAs and analyzed their graphical representations of intervention complexity.
  • Existing visualization methods were found to be limited in expressing complex data structures.
  • Three new visualization approaches were developed: CNMA-UpSet plot, CNMA heat map, and CNMA-circle plot.

Main Results:

  • Network diagrams, the most common visualization, were insufficient for complex CNMA data.
  • The CNMA-UpSet plot effectively presents arm-level data for networks with numerous components.
  • Heat maps assist in selecting pairwise interactions for CNMA models, and CNMA-circle plots visualize differing component combinations between trial arms.

Conclusions:

  • Novel CNMA-specific visualizations enhance the understanding of complex data structures in multicomponent interventions.
  • These tools facilitate the interpretation of CNMA results as the method gains wider adoption.
  • Improved visualization is crucial for the effective evaluation and synthesis of complex health interventions.