Parametric G-computation for Compatible Indirect Treatment Comparisons with Limited Individual Patient Data.

@article{RemiroAzocar2022ParametricGF,
  title={Parametric G-computation for Compatible Indirect Treatment Comparisons with Limited Individual Patient Data.},
  author={Antonio Remiro-Az'ocar and Anna Heath and Gianluca Baio},
  journal={Research synthesis methods},
  year={2022}
}
Population adjustment methods such as matching-adjusted indirect comparison (MAIC) are increasingly used to compare marginal treatment effects when there are cross-trial differences in effect modifiers and limited patient-level data. MAIC is based on propensity score weighting, which is sensitive to poor covariate overlap and cannot extrapolate beyond the observed covariate space. Current outcome regression-based alternatives can extrapolate but target a conditional treatment effect that is… 

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