A parsimonious personalized dose-finding model via dimension reduction.

@article{Zhou2021APP,
  title={A parsimonious personalized dose-finding model via dimension reduction.},
  author={Wenzhuo Zhou and Ruoqing Zhu and Donglin Zeng},
  journal={Biometrika},
  year={2021},
  volume={108 3},
  pages={
          643-659
        }
}
Learning an individualized dose rule in personalized medicine is a challenging statistical problem. Existing methods often suffer from the curse of dimensionality, especially when the decision function is estimated nonparametrically. To tackle this problem, we propose a dimension reduction framework that effectively reduces the estimation to a lower-dimensional subspace of the covariates. We exploit that the individualized dose rule can be defined in a subspace spanned by a few linear… 

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