  In randomized clinical trials with survival outcome, there has been an
increasing interest in subgroup identification based on baseline genomic,
proteomic markers or clinical characteristics. Some of the existing methods
identify subgroups that benefit substantially from the experimental treatment
by directly modeling outcomes or treatment effect. When the goal is to find an
optimal treatment for a given patient rather than finding the right patient for
a given treatment, methods under the individualized treatment regime framework
estimate an individualized treatment rule that would lead to the best expected
clinical outcome as measured by a value function. Connecting the concept of
value function to subgroup identification, we propose a nonparametric method
that searches for subgroup membership scores by maximizing a value function
that directly reflects the subgroup-treatment interaction effect based on
restricted mean survival time. A gradient tree boosting algorithm is proposed
to search for the individual subgroup membership scores. We conduct simulation
studies to evaluate the performance of the proposed method and an application
to an AIDS clinical trial is performed for illustration.
