A parsimonious personalized dose-finding model via dimension reduction.

ABSTRACT: 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 combinations of the covariates, leading to a more parsimonious model. Also, our framework does not require the inverse probability of the propensity score under observational studies due to a direct maximization of the value function. This distinguishes us from the outcome weighted learning framework, which a

SUBMITTER: Zhou W 

PROVIDER: S-EPMC8514170 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature