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Visualizing inconsistency in network meta-analysis by independent path decomposition.

Ulrike Krahn, Harald Binder, Jochem König1

  • 1Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center Johannes Gutenberg University Mainz, Langenbeckstr, 1, 55101 Mainz, Germany. koenigjo@uni-mainz.de.

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Summary
This summary is machine-generated.

Network meta-analysis validity is improved by assessing treatment effect consistency. A novel path decomposition method visualizes inconsistencies, enhancing reliability in treatment comparisons.

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Area of Science:

  • Biostatistics
  • Evidence Synthesis
  • Clinical Epidemiology

Background:

  • Network meta-analysis synthesizes evidence from multiple randomized controlled trials.
  • Assessing the consistency of treatment effects across the network is crucial for validity.
  • Deviations from consistency can impact the reliability of indirect treatment comparisons.

Purpose of the Study:

  • To develop and evaluate a method for assessing consistency in network meta-analysis.
  • To provide a graphical tool for visualizing treatment effect consistency.
  • To understand the influence of deviations on network estimates.

Main Methods:

  • Approximation of network estimates using decomposable subnets and independent paths.
  • Utilizing path-based estimates and residual evidence for consistency assessment.
  • Defining influence functions to quantify the impact of study effect changes.

Main Results:

  • Independent path decomposition visualizes consistency for specific treatment comparisons.
  • This method identifies inconsistencies from different evidence paths and outlier effects.
  • Contrasting findings with the net heat plot approach for consistency assessment.

Conclusions:

  • A subnet approximation and path decomposition visualization offer a practical graphical validation tool.
  • This approach extends classical meta-analysis validation techniques to network meta-analysis.
  • Enhances the interpretability and reliability of network meta-analysis findings.