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This study introduces novel frequentist methods for network meta-analysis, enhancing consistency and inconsistency assessment in complex evidence synthesis. These approaches improve the analysis of multiple treatments in research syntheses.

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Area of Science:

  • Biostatistics
  • Evidence Synthesis
  • Medical Research Methodology

Background:

  • Network meta-analysis (NMA) synthesizes evidence from multiple studies comparing various treatments.
  • Assessing consistency and inconsistency within NMA is crucial for reliable conclusions.
  • Existing NMA models often have limitations, such as restricting to two-arm trials or using Bayesian frameworks.

Purpose of the Study:

  • To propose novel frequentist methods for estimating consistency and inconsistency models in network meta-analysis.
  • To offer alternatives to existing Bayesian approaches and models limited to two-arm trials.
  • To facilitate the implementation of these methods in standard statistical software.

Main Methods:

  • Developed two new frequentist approaches for NMA.
  • Expressed consistency and inconsistency models as multivariate random-effects meta-regressions.
  • Illustrated the methodology using the mvmeta package in Stata.

Main Results:

  • The proposed frequentist methods provide a viable alternative for estimating consistency and inconsistency in NMA.
  • The methods are implementable in standard statistical software, enhancing accessibility.
  • Demonstrated the practical application and utility of the new approaches.

Conclusions:

  • The new frequentist methods enhance the analysis of evidence from multiple treatments in network meta-analysis.
  • These methods offer flexibility and broader applicability compared to previous approaches.
  • Facilitates robust assessment of evidence consistency and inconsistency in complex research syntheses.