Center for Biologics Evaluation and Research (CBER), U.S. Food and Drug Administration (FDA), 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
J Hematol Oncol. 2020 Mar 14;13(1):20. doi: 10.1186/s13045-020-0847-x.
Conventional trial design and analysis strategies fail to address the typical challenge of immune-oncology (IO) studies: only a limited percentage of treated patients respond to the experimental treatment. Treating non-responders, we hypothesize, would in part drive non-proportional hazards (NPH) patterns in Kaplan-Meier curves that violates the proportional hazards (PH) assumption required by conventional strategies. Ignoring such violation incurred from treating non-responders in the design and analysis strategy may result in underpowered or even falsely negative studies. Hence, designing innovative IO trials to address such pitfall becomes essential.
We empirically tested the hypothesis that treating non-responders in studies of inadequate size is sufficient to cause NPH patterns and thereby proposed a novel strategy, p-embedded, to incorporate the dichotomized response incurred from treating non-responders, as measured by the baseline proportion of responders among treated patients p%, into the design and analysis procedures, aiming to ensure an adequate study power when the PH assumption is violated.
Empirical studies confirmed the hypothetical cause contributes to the manifestation of NPH patterns. Further evaluations revealed a significant quantitative impact of p% on study efficiency. The p-embedded strategy incorporating the properly pre-specified p% ensures an adequate study power whereas the conventional design ignoring it leads to a severe power loss.
The p-embedded strategy allows us to quantify the impact of treating non-responders on study efficiency. Implicit in such strategy is the solution to mitigate the occurrence of NPH patterns and enhance the study efficiency for IO trials via enrolling more prospective responders.
传统的试验设计和分析策略无法解决免疫肿瘤学（IO）研究中典型的挑战：只有有限比例的治疗患者对实验治疗有反应。我们假设，治疗无反应者将部分导致卡普兰-迈耶（Kaplan-Meier）曲线中的非比例风险（NPH）模式，这违反了传统策略所需的比例风险（PH）假设。在设计和分析策略中忽略因治疗无反应者而导致的这种违反可能会导致研究效力不足甚至出现假阴性。因此，设计创新的 IO 试验以解决这种陷阱变得至关重要。
我们通过实证检验了一个假设，即在研究规模不足的情况下治疗无反应者足以导致 NPH 模式，并提出了一种新策略，即 p-嵌入，将治疗无反应者所导致的二分反应（由治疗患者中反应者的基线比例 p%衡量）纳入设计和分析程序，旨在确保在违反 PH 假设时具有足够的研究效力。
实证研究证实了假设的原因导致了 NPH 模式的表现。进一步的评估表明，p%对研究效率有显著的定量影响。纳入适当预先指定的 p%的 p-嵌入策略确保了足够的研究效力，而忽略它的传统设计则导致严重的效力损失。
p-嵌入策略使我们能够量化治疗无反应者对研究效率的影响。该策略隐含着通过招募更多有前途的反应者来减轻 NPH 模式的发生并提高 IO 试验的研究效率的解决方案。
