Non-asymptotic Properties of Individualized Treatment Rules from Sequentially Rule-Adaptive Trials.

ABSTRACT: Learning optimal individualized treatment rules (ITRs) has become increasingly important in the modern era of precision medicine. Many statistical and machine learning methods for learning optimal ITRs have been developed in the literature. However, most existing methods are based on data collected from traditional randomized controlled trials and thus cannot take advantage of the accumulative evidence when patients enter the trials sequentially. It is also ethically important that future patients should have a high probability to be treated optimally based on the updated knowledge so far. In this work, we propose a new design called sequentially rule-adaptive trials to learn optimal ITRs based on the contextual bandit framework, in contrast to the response-adaptive design in traditional a

SUBMITTER: Gao D 

PROVIDER: S-EPMC10419117 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature