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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Evidence-Based Medicine

Background:

  • Network meta-analysis (NMA) integrates direct and indirect trial evidence for comparing multiple interventions.
  • NMA is valuable for ranking treatments but often criticized for complexity and requiring advanced statistical skills.
  • Challenges include evaluating model assumptions, statistical intricacies, and presenting results clearly.

Purpose of the Study:

  • To demystify network meta-analysis for researchers without extensive statistical expertise.
  • To provide practical tools for understanding and applying NMA methodology.
  • To enhance the accessibility of treatment comparison and ranking through clear explanations and examples.

Main Methods:

  • Development and presentation of graphical tools for NMA.
  • Provision of STATA routines for practical application.
  • Worked examples to illustrate the methodology and interpretation of results.

Main Results:

  • The study offers accessible methods for presenting evidence bases in NMA.
  • Graphical tools facilitate the evaluation of underlying model assumptions.
  • STATA routines enable straightforward fitting and interpretation of NMA models.

Conclusions:

  • The developed graphical tools and STATA routines significantly enhance the accessibility of network meta-analysis.
  • Non-statisticians can more effectively utilize NMA for evidence synthesis and treatment ranking.
  • This work promotes broader application of NMA in research and clinical practice.