In a relatively short period of time, therapeutic cancer vaccines have entered the landscape of cancer therapy and several large vaccine trials have been conducted. In contrast to the conventional chemotherapeutic drugs, these novel agents stimulate the patient’s own immune response to combat cancer. This indirect mechanism-of-action for vaccines poses the possibility of a delayed onset of clinical effect, due to the time required to mount an effective immune response and the time for that response to be translated into an observable clinical response. The conventional designs, however, often ignore this delayed effect and result in an underestimated sample size with insufficient power. In this talk, we propose an innovative approach for sample size and power calculation by incorporating the delayed treatment effect in the design and analysis of such trials. The properties of the proposed method are to be evaluated both empirically and theoretically.