Constructing dynamic treatment regimes with shared parameters for censored data.

ABSTRACT: Dynamic treatment regimes are sequential decision rules that adapt throughout disease progression according to a patient's evolving characteristics. In many clinical applications, it is desirable that the format of the decision rules remains consistent over time. Unlike the estimation of dynamic treatment regimes in regular settings, where decision rules are formed without shared parameters, the derivation of the shared decision rules requires estimating shared parameters indexing the decision rules across different decision points. Estimation of such rules becomes more complicated when the clinical outcome of interest is a survival time subject to censoring. To address these challenges, we propose two novel methods: censored shared-Q-learning and censored shared-O-learning. Both methods i

SUBMITTER: Zhao YQ 

PROVIDER: S-EPMC7305816 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature