• Corpus ID: 235303705

Marginalization of Regression-Adjusted Treatment Effects in Indirect Comparisons with Limited Patient-Level Data

@inproceedings{RemiroAzocar2020MarginalizationOR,
  title={Marginalization of Regression-Adjusted Treatment Effects in Indirect Comparisons with Limited Patient-Level Data},
  author={Antonio Remiro-Az'ocar and Anna Heath and Gianluca Baio},
  year={2020}
}
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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