Dynamic treatment regime (DTR) is the statistical study of personalized medicine in which treatment decisions are tailored to evolving, patient-level information. Individuals are followed through multiple stages of clinical intervention, and the statistical goal is to perform inferences on the sequence of individualized treatment decision rules to be applied in practice. Of interest is the identification of an optimal DTR, that is, the sequence of treatment decisions that yields the best expected outcome. Statistical methods for identifying optimal DTR from observational data are theoretically complex and not easily implementable by researchers, especially when the outcome of interest is survival time. I propose a doubly-robust, accessible method for estimating optimal DTR with survival endpoints subject to right-censoring. I will decipher the theoretical derivations, and discuss a simulation study and motivating clinical examples.