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Network meta-analysis validity relies on consistency. This study clarifies side-splitting model assumptions for evaluating direct versus indirect evidence inconsistency in treatment comparisons.

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Area of Science:

  • Biostatistics
  • Evidence Synthesis
  • Medical Research Methodology

Background:

  • Network meta-analysis (NMA) is crucial for synthesizing evidence from multiple treatment comparisons.
  • Network inconsistency, particularly between direct and indirect evidence, can threaten NMA validity.
  • Existing methods like Bayesian node-splitting and frequentist side-splitting models address inconsistency, but parameter assignment in multi-arm trials can affect results.

Purpose of the Study:

  • To demonstrate that the side-splitting model is a specific instance of a design-by-treatment interaction model.
  • To illustrate how different parameterizations of the side-splitting model correspond to distinct design-by-treatment interactions.

Main Methods:

  • Evaluation of the side-splitting model using an arm-based generalized linear mixed model.
  • Comparison of results from arm-based models with contrast-based models using an example dataset.

Main Results:

  • The three parameterizations of the side-splitting model involve different assumptions regarding the contribution of treatments to inconsistency.
  • Symmetrical parameterization assumes both treatments contribute to inconsistency.
  • Alternative parameterizations assume only one treatment contributes to inconsistency.

Conclusions:

  • Understanding the distinct assumptions of side-splitting model parameterizations is essential for meta-analysts.
  • This knowledge aids in selecting the appropriate implementation of the side-splitting method for specific analyses.
  • Informed choices in parameterization can improve the reliability of network meta-analysis results.