Constructing Stabilized Dynamic Treatment Regimes for Censored Data
Stabilized dynamic treatment regimes are sequential decision rules for individual patients that not only adaptive throughout the disease progression but also remain consistent over time in format. The estimation of stabilized dynamic treatment regimes becomes more complicated when the clinical outcome of interest is a survival time subject to censoring. To address this challenge, we propose two novel methods, censored shared-Q-learning and censored shared-O-learning. Both methods incorporate clinical preferences into a qualitative rule, where the parameters indexing the decision rules are shared across different stages and estimated simultaneously. We use extensive simulation studies to demonstrate the superior performance of the proposed methods. The methods are further applied to the Framingham Study to derive treatment rules for cardiovascular disease.