  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.
