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Testing moderation in network meta-analysis with individual participant data.

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Network meta-analysis (NMA) can now test moderator effects, determining if interventions vary by population or context. Individual participant data significantly enhances NMA

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Meta-analytic methods are crucial for estimating intervention effectiveness and comparative effectiveness.
  • Network meta-analysis (NMA) integrates direct and indirect treatment comparisons.
  • Existing NMA methods primarily focus on main effects.

Purpose of the Study:

  • To extend network meta-analysis (NMA) methods for examining moderator effects.
  • To investigate the impact of individual participant data (IPD) versus aggregate data on detecting moderator effects.
  • To develop a generalized multilevel model for NMA.

Main Methods:

  • Developed a generalized multilevel model for NMA incorporating within-trial and between-trial heterogeneity.
  • Included participant-level covariates in the NMA framework.
  • Proposed a new NMA diagram and defined homogeneity and consistency across trials.
  • Conducted a simulation study to assess power for detecting main and moderator effects.

Main Results:

  • Power to detect moderator effects is substantially greater with individual participant data (IPD) compared to aggregate data NMA.
  • The generalized multilevel model effectively accounts for heterogeneity and participant-level covariates.
  • Simulation results demonstrated the model's utility in assessing power for main and moderator effects.

Conclusions:

  • Extending NMA to investigate moderator effects is feasible and valuable.
  • Individual participant data (IPD) offers superior sensitivity for detecting moderator effects in NMA.
  • The proposed generalized multilevel model provides a robust framework for advanced NMA.