Nonparametric Bayesian Q-learning for optimization of dynamic treatment regimes in the presence of partial compliance.

ABSTRACT: Existing methods for estimation of dynamic treatment regimes are mostly limited to intention-to-treat analyses-which estimate the effect of randomization to a particular treatment regime without considering the compliance behavior of patients. In this article, we propose a novel nonparametric Bayesian Q-learning approach to construct optimal sequential treatment regimes that adjust for partial compliance. We consider the popular potential compliance framework, where some potential compliances are latent and need to be imputed. The key challenge is learning the joint distribution of the potential compliances, which we accomplish using a Dirichlet process mixture model. Our approach provides two kinds of treatment regimes: (1) conditional regimes that depend on the potential compliance value

SUBMITTER: Bhattacharya I 

PROVIDER: S-EPMC11625440 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature