Benefit-risk assessment is a crucial step in the medical decision process. In many biomedical studies and clinical trials, a failure event is typically considered as the primary endpoint while other longitudinal markers also possess important implications for the evaluation of treatment effect. Thus, questions arise on how to evaluate treatment effects based on the two types of measurements for the purpose of deciding on which treatment is most likely to benefit the patients. In this talk, we present a unified framework for benefit-risk assessment using the observed longitudinal markers and time to he primary endpoint. We propose a composite marker process to synthesize information from these two measurements. We consider nonparametric and semiparametric approaches under two scenarios: (i) the longitudinal marker is measured intermittently during the study period, and (ii) the values of the longitudinal marker are observed throughout the entire follow-up period. A real data example of prostate cancer is used to illustrate the application of the proposed methods.