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.

中文翻译：

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设计有反应者和无反应者的免疫肿瘤学临床试验。

免疫肿瘤学 (IO) 试验的设计和分析面临的一个典型挑战是在事件发生时间终点下的 Kaplan-Meier 曲线中显示的非比例风险 (NPH) 模式的普遍性。NPH 模式会违反比例风险假设，但传统的设计和分析策略通常会忽略这种违反，导致 IO 研究的功效不足甚至假阴性。在本文中，我们通过经验和分析表明，在规模不足的 IO 研究中治疗无反应者会导致各种 NPH 模式；然后我们提出了一种新颖的设计和分析策略，P %‐ r esponder info e m b edded（PRIME），以正确合并因治疗无反应者而产生的二分反应。实证研究表明，所提出的策略可以实现理想的功率，而传统的替代方案会导致严重的功率损失。PRIME 策略使我们能够量化治疗无反应者对研究效率的影响，从而能够以足够的能力正确设计 IO 试验。更重要的是，它通过招募更多的潜在反应者找到了提高研究效率和缓解 NPH 模式的解决方案。为实施而开发了一个 R 包 (Immunotherapy.Design)。