Interval-censored data frequently arise in clinical research where event times are only known to fall within specific assessment windows. Although the Cox proportional hazards model is a standard approach for such data, existing Wald-type tests often suffer from instability or poor performance in small samples. In this paper, we propose a robust spline-sieve-based likelihood ratio test for interval-censored data. We develop a computationally efficient estimation framework that ensures numerical stability. Furthermore, we rigorously establish the asymptotic distribution of the proposed likelihood ratio statistic, providing a solid theoretical foundation for statistical inference. Extensive simulation studies demonstrate that our approach achieves superior error control and higher power compared with traditional approaches. The practical utility of the method is further illustrated through the analysis of a real-world clinical dataset.

翻译：区间删失数据常见于临床研究中，其事件发生时间仅已知落在特定评估窗口内。尽管Cox比例风险模型是处理此类数据的标准方法，但现有的Wald型检验在小样本情况下往往存在不稳定性或性能不佳的问题。本文针对区间删失数据，提出了一种稳健的基于样条筛的似然比检验方法。我们开发了一个计算高效的估计框架，确保数值稳定性。同时，严格建立了所提似然比统计量的渐近分布，为统计推断提供了坚实的理论基础。大量仿真研究表明，与传统方法相比，我们的方法实现了更优的误差控制和更高的检验功效。通过分析一个真实的临床数据集，进一步验证了该方法的实际应用价值。