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Exploring the Transitivity Assumption in Network Meta-Analysis: A Novel Approach and Its Implications.

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Evaluating network meta-analysis feasibility requires assessing transitivity. This study introduces a novel method using hierarchical clustering to identify potential intransitivity, improving systematic review validity.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Research Methodology

Background:

  • Network meta-analysis (NMA) feasibility hinges on the transitivity assumption, which is difficult to evaluate empirically.
  • Transitivity requires no systematic differences in effect modifiers across treatment comparisons within a network.
  • Existing methods for evaluating transitivity are complex and rely heavily on epidemiological interpretation.

Purpose of the Study:

  • To propose a novel methodological framework for evaluating the transitivity assumption in network meta-analysis.
  • To develop an approach for detecting potential intransitivity using study-level characteristics.
  • To provide a semi-objective method for assessing the validity of network meta-analysis.

Main Methods:

  • Calculating dissimilarities between treatment comparisons based on aggregate participant and methodological characteristics.
  • Applying hierarchical clustering to group similar treatment comparisons.
  • Quantifying clinical and methodological heterogeneity within and between comparisons.

Main Results:

  • The proposed approach identified varying levels of between-comparison dissimilarities in investigated networks.
  • Several treatment comparisons showed "likely concerning" non-statistical heterogeneity, indicating potential intransitivity.
  • Hierarchical clustering revealed clusters of studies, suggesting areas requiring closer examination for transitivity violations.

Conclusions:

  • The novel approach facilitates empirical evaluation of transitivity in network meta-analysis.
  • Assessing clinical and methodological heterogeneity is crucial for NMA feasibility, similar to statistical heterogeneity.
  • This method aids in scrutinizing evidence bases and justifying the use of network meta-analysis.