Methods for estimating subgroup effects in cost-effectiveness analyses that use observational data.

@article{Kreif2012MethodsFE,
  title={Methods for estimating subgroup effects in cost-effectiveness analyses that use observational data.},
  author={No{\'e}mi Kreif and Richard D Grieve and Rosalba Radice and Zia Sadique and Roland Ramsahai and Jasjeet Singh Sekhon},
  journal={Medical decision making : an international journal of the Society for Medical Decision Making},
  year={2012},
  volume={32 6},
  pages={750-63}
}
Decision makers require cost-effectiveness estimates for patient subgroups. In nonrandomized studies, propensity score (PS) matching and inverse probability of treatment weighting (IPTW) can address overt selection bias, but only if they balance observed covariates between treatment groups. Genetic matching (GM) matches on the PS and individual covariates using an automated search algorithm to directly balance baseline covariates. This article compares these methods for estimating subgroup… CONTINUE READING
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