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Network meta-analysis (NMA) using multi-arm trials requires accounting for arm correlations. Failing to do so with contrast-level summaries can lead to incorrect results, impacting evidence synthesis.

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Health Economics

Background:

  • Multi-arm trials offer valuable evidence for network meta-analysis (NMA).
  • Trial data can be reported as arm-level or contrast-level summaries.
  • Contrast-level summaries compare treatment arms against a trial-specific control.

Purpose of the Study:

  • To highlight the importance of accounting for correlations in multi-arm trials within NMA.
  • To demonstrate how contrast-level summaries can yield incorrect results if correlations are ignored.
  • To review software and methods for handling correlations in NMA.

Main Methods:

  • Likelihood-based inference was compared between arm-level and contrast-level data formats.
  • The impact of ignoring correlations in multi-arm trials was analyzed.
  • Bayesian and frequentist NMA software capable of handling correlations were reviewed.

Main Results:

  • For two-arm trials, arm-level and contrast-level inference are identical.
  • For multi-arm trials, contrast-level inference is incorrect without accounting for correlations.
  • Ignoring correlations can introduce significant differences in NMA estimates.

Conclusions:

  • Accurate NMA with multi-arm trials necessitates incorporating correlations.
  • Trialists should report control arm standard errors to facilitate correlation imputation.
  • Software exists to correctly perform NMA with multi-arm trials, accounting for correlations.