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Network meta-analysis of individual and aggregate level data.

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Network meta-analysis using individual patient data (IPD) alongside aggregate data (AgD) can reduce bias. Combining IPD and AgD with non-linear models improves treatment effect estimation, especially with patient-level heterogeneity.

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Epidemiology

Background:

  • Network meta-analysis (NMA) commonly uses aggregate data (AgD).
  • AgD presents challenges in reflecting individual-level covariate effects on treatment outcomes.
  • Heterogeneity in patient-level covariates can bias treatment effect estimations in AgD-based NMA.

Purpose of the Study:

  • To present novel non-linear NMA models for integrating individual patient data (IPD) and AgD.
  • To reduce bias and uncertainty in direct and indirect treatment effects within NMA.
  • To address challenges posed by patient-level covariate heterogeneity in NMA.

Main Methods:

  • Developed two non-linear NMA models for combining IPD and AgD.
  • Model 1: Utilized identical model forms for both IPD and AgD.
  • Model 2: Derived AgD models by integrating an underlying IPD model over covariate distributions, inspired by ecological inference methods.

Main Results:

  • Simulated examples demonstrated the application of the proposed models.
  • Incorporating IPD from a subset of studies enhanced treatment effect estimation accuracy.
  • The second model showed reduced bias in scenarios with significant treatment-by-covariate interactions, potentially increasing uncertainty.

Conclusions:

  • Combining IPD and AgD in NMA offers advantages over AgD alone.
  • The second proposed model appears more robust to bias from patient-level covariate interactions.
  • Further research is needed to determine optimal model selection; IPD is recommended when available for NMA.