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Network meta-analysis (NMA) inconsistency sources are often outliers, not influential, in Bayesian hierarchical models. This diagnostic approach helps assess how trial data impacts NMA conclusions.

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Area of Science:

  • Biostatistics
  • Health Research Methodology

Background:

  • Network meta-analysis (NMA) integrates diverse evidence for comparing multiple treatments.
  • Inconsistency, where direct and indirect evidence diverge, is a known challenge in NMA.
  • Limited guidance exists on managing identified inconsistencies in NMA.

Purpose of the Study:

  • To develop and evaluate diagnostic methods for identifying influential and outlying observations in NMA.
  • To assess whether sources of inconsistency in NMA impact substantive conclusions.

Main Methods:

  • Formal diagnostics were developed for Bayesian hierarchical models, specifically generalized linear hierarchical NMA models.
  • Methods focused on a trial-by-arm level to detect observations affecting parameter estimates or deviating significantly.
  • The approach was validated using both published and simulated datasets.

Main Results:

  • Inconsistency sources in NMA were generally found to be non-influential on overall results.
  • However, these same sources were identified as potential outliers in the analyzed datasets.
  • This pattern mirrors findings in linear model theory where outliers with low leverage have minimal influence.

Conclusions:

  • Diagnostic tools can identify trial-by-arm level outliers in NMA, even if they are not influential.
  • Understanding the nature of inconsistency (outlier vs. influential) is crucial for interpreting NMA results.
  • Future work will extend these diagnostics to include baseline covariates and individual patient data.