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Area of Science:

  • Biostatistics
  • Health Economics
  • Pharmacometrics

Background:

  • Standard network meta-analysis (NMA) assumes balanced effect modifiers across populations.
  • Existing population adjustment methods are limited to pairwise comparisons and cannot predict into target populations.
  • Meta-regression methods can introduce aggregation bias.

Purpose of the Study:

  • To develop a new method extending NMA to incorporate individual patient data for population adjustment.
  • To enable treatment effect comparisons in any specified target population.
  • To improve the accuracy and interpretability of treatment effect estimates.

Main Methods:

  • An individual-level regression model is defined and fitted using aggregate data via integration over covariate distributions.
  • Quasi-Monte-Carlo integration is employed to handle complex integration.
  • Copulas are used to account for covariate correlation structures.

Main Results:

  • The proposed method provides population-average treatment effects similar to standard NMA when effect modifier distributions are similar.
  • The new method achieves a better fit than random-effects NMA and substantially reduces uncertainty.
  • Estimates are more interpretable by explaining within- and between-study variation.

Conclusions:

  • The novel NMA framework with population adjustment offers a more robust and interpretable approach for treatment comparisons.
  • This method allows for predictions in specific target populations, aiding clinical decision-making.
  • The approach effectively addresses limitations of existing meta-analysis techniques.