A typical challenge facing the design and analysis of immuno-oncology (IO) trials is the prevalence of nonproportional hazards (NPH) patterns manifested in Kaplan-Meier curves under time-to-event endpoints. The NPH patterns would violate the proportional hazards assumption, and yet conventional design and analysis strategies often ignore such a violation, resulting in underpowered or even falsely negative IO studies. In this article, we show, both empirically and analytically, that treating nonresponders in IO studies of inadequate size would give rise to a variety of NPH patterns; we then present a novel design and analysis strategy, P%-responder information embedded (PRIME), to properly incorporate the dichotomized response incurred from treating nonresponders. Empirical studies demonstrate that the proposed strategy can achieve desirable power, whereas the conventional alternative leads to a severe power loss. The PRIME strategy allows us to quantify the impact of treating nonresponders on study efficiency, thereby enabling a proper design of IO trials with an adequate power. More importantly, it pinpoints a solution to enhance the study efficiency and alleviates the NPH patterns by enrolling more prospective responders. An R package (Immunotherapy.Design) is developed for implementation.