In early-phase clinical trials, a predictive biomarker may identify subgroups that benefit from an experimental therapy, even when the overall average treatment effect is negligible. Recently proposed nonparametric interaction tests such as the Average Kolmogorov-Smirnov Approach (AKSA) avoid prespecified biomarker cutting points and model assumptions, but their power degrades when the biomarker distribution is zero-inflated. We propose a two-step test that partitions the analysis into a spike test for biomarker-negative patients and a tail test for biomarker-positive patients, then combines the resulting p-values using Fisher's or Brown's method. This design isolates distinct sources of predictive effects, mitigates dilution, and preserves exact type I error control through permutation calibration. We derive theoretical properties showing that the proposed test retains nominal size and improves power over AKSA when predictive effects are concentrated in either the spike or tail subpopulation. Extensive simulations confirm robust type I error control under various zero-inflation rates, sample sizes, and skewed biomarker distributions. We also demonstrate consistent power gains across spike-only, tail-only, and mixed-effect scenarios. Our method provides a practical and flexible tool for early-phase trials with sparse biomarker distributions, enabling more reliable identification of predictive biomarkers to guide later-phase development.

翻译：在早期临床试验中，即使总体平均治疗效果可忽略不计，预测性生物标志物仍可能识别出能从实验性治疗中获益的亚组。最近提出的非参数交互作用检验（如平均Kolmogorov-Smirnov方法，AKSA）避免了预设的生物标志物截断点和模型假设，但当生物标志物分布呈零膨胀时，其检验效能会下降。我们提出一种两步检验法：首先将分析划分为针对生物标志物阴性患者的尖峰检验和针对生物标志物阳性患者的尾部检验，随后使用Fisher法或Brown法合并所得p值。该设计能分离预测效应的不同来源，减轻稀释效应，并通过置换校准保持精确的第一类错误控制。我们推导的理论性质表明，当预测效应集中在尖峰或尾部亚群时，所提检验能维持名义检验水平，并较AKSA获得更高的检验效能。大量模拟实验证实，该方法在不同零膨胀率、样本量和偏态生物标志物分布下均能实现稳健的第一类错误控制。我们还在仅尖峰效应、仅尾部效应及混合效应场景中验证了其持续的效能提升。本方法为具有稀疏生物标志物分布的早期临床试验提供了实用灵活的工具，能够更可靠地识别预测性生物标志物以指导后期研发。