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A novel approach for visualizing local consistency in network meta-analysis.

Huw Wilson1, Anton Schönstein2, Sarah Robson1

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Network meta-analysis validity relies on consistent direct and indirect treatment effect estimates. This study introduces a novel visualization to assess consistency across multiple aspects, improving interpretation for complex networks.

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Health Research Methodology

Background:

  • Network meta-analysis (NMA) is crucial for comparing multiple treatments by pooling direct and indirect evidence from clinical trials.
  • A key assumption in NMA is the consistency between direct and indirect effect estimates, ensuring the validity of pooled results.
  • Existing visualization methods like forest plots and heat maps have limitations in comprehensively assessing NMA consistency, especially in large networks.

Purpose of the Study:

  • To develop and present a novel visualization tool for assessing the consistency assumption in network meta-analysis.
  • To address the limitations of current visualization approaches by integrating multiple aspects of direct and indirect evidence comparison.
  • To provide a clear and interpretable method for evaluating treatment effect consistency, even with an increasing number of treatments.

Main Methods:

  • The study outlines the mathematical background for a new visualization technique designed to assess NMA consistency.
  • The proposed visualization integrates three key aspects: effect sizes, their differences and uncertainty, and the direction of benefit/harm.
  • Implementation details and accompanying R code are provided for practical application of the visualization.

Main Results:

  • The developed visualization effectively combines multiple facets of direct and indirect estimate comparison into a single, interpretable graphic.
  • The approach aims to overcome the interpretability challenges faced with traditional methods as network size grows.
  • The visualization facilitates a more thorough examination of the consistency assumption in network meta-analysis.

Conclusions:

  • The novel visualization offers an improved method for assessing the critical consistency assumption in network meta-analysis.
  • This tool enhances the interpretability and validity assessment of evidence synthesized from multiple treatment comparisons.
  • The availability of R code promotes the adoption and application of this advanced visualization technique in clinical research.