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Network meta-analysis (NMA) helps rank treatments. Including treatment-covariate interactions (TCIs) in NMA requires creating hierarchies for specific patient profiles to ensure accurate treatment rankings.

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Health Services Research

Background:

  • Network meta-analysis (NMA) is a valuable tool for synthesizing evidence and establishing treatment hierarchies.
  • Treatment-covariate interactions (TCIs) allow for the examination of how relative treatment effects differ across patient characteristics.

Purpose of the Study:

  • To demonstrate how to construct treatment hierarchies from NMA models incorporating TCIs.
  • To emphasize the importance of considering specific covariate profiles when creating treatment hierarchies in the presence of TCIs.

Main Methods:

  • Outlining the standard Bayesian NMA approach for creating treatment hierarchies.
  • Detailing the methodology for deriving a covariate-specific treatment hierarchy from an NMA model that estimates TCIs.

Main Results:

  • Treatment hierarchies derived from NMA models with TCIs are dependent on the chosen covariate profile.
  • The study provides a practical framework for generating covariate-specific treatment hierarchies.

Conclusions:

  • When using NMA with TCIs, treatment hierarchies must be developed with a specific covariate profile in mind.
  • The presented methods facilitate more nuanced and clinically relevant treatment comparisons in evidence synthesis.