Parametric G‐computation for compatible indirect treatment comparisons with limited individual patient data

@article{RemiroAzocar2021ParametricGF,
  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={2021},
  volume={13},
  pages={716 - 744}
}
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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Disagreement remains on what the target estimand should be for population-adjusted indirect treatment comparisons. This debate is of central importance for policy-makers and applied practitioners in

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