Proper Inference for Value Function in High-Dimensional Q-Learning for Dynamic Treatment Regimes.

ABSTRACT: Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients' responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of non-regularity problems in the presence of non-respondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high. In this paper, we propose a high-dimensional Q-learning (HQ-learning) to facilitate the inference of optimal values and parameters. The proposed method allows us to simultaneously estimate the optimal dynamic treatment regimes and select the important variables that truly contribute to the individual reward.

SUBMITTER: Zhu W 

PROVIDER: S-EPMC6953729 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature