Arming the immune system against cancer has emerged as a powerful tool in oncology during recent years. Instead of poisoning a tumor or destroying it with radiation, therapeutic cancer vaccine, a type of cancer immunotherapy, unleashes the immune system to combat cancer. This indirect mechanism-of-action of vaccines poses the possibility of a delayed onset of clinical effect. This results in a delayed separation of survival curves between the experimental and control groups in therapeutic cancer vaccine trials with time-to-event endpoints and violates the proportional hazard assumption. As a result, the conventional study design based on the regular log-rank test ignoring the delayed effect would lead to a loss of power. In addition, the delayed duration seems to vary from subject to subject or from study to study, and thus it could be considered as random. In this talk, we propose innovative approaches for sample size and power calculation to properly and efficiently account for the delayed effect with random duration into the design and analysis of such a trial and evaluate the proposed approaches both analytically and empirically.