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

A Nonparametric Method for Value Function Guided Subgroup Identification via Gradient Tree Boosting for Censored Survival Data