🤖 AI Summary
This study addresses the low power and poor stability of conventional Wald-type tests for interval-censored data in small-sample settings by proposing the first likelihood ratio test framework based on spline sieves. The method integrates spline sieve modeling with likelihood ratio testing and rigorously derives its asymptotic distribution, ensuring both theoretical validity and practical robustness. Simulation studies demonstrate that the proposed approach substantially improves statistical power while maintaining proper control of Type I error rates. Its applicability and advantages are further confirmed through analysis of real clinical data.
📝 Abstract
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.