  Individualized treatment rules can lead to better health outcomes when
patients have heterogeneous responses to treatment. Very few individualized
treatment rule estimation methods are compatible with a multi-treatment
observational study with right censored survival outcomes. In this paper we
extend policy evaluation methods to the right censored data setting. Existing
approaches either make restrictive assumptions about the structure of the data,
or use inverse weighting methods that increase the variance of the estimator
resulting in decreased performance. We propose a method which uses balanced
policy evaluation combined with an imputation approach to remove right
censoring. We show that the proposed imputation approach is compatible with a
large number of existing survival models and can be used to extend any
individualized treatment rule estimation method to the right censored data
setting. We establish the rate at which the imputed values converge to the
conditional expected survival times, as well as consistency guarantees and
regret bounds for the combined balanced policy with imputation approach. In
simulation studies, we demonstrate the improved performance of our approach
compared to existing methods. We also apply our method to data from the
University of North Carolina Center for AIDS Research HIV Clinical Cohort.
