Survival analysis encompasses a broad range of methods for analyzing time-to-event data, with one key objective being the comparison of survival curves across groups. Traditional approaches for identifying clusters of survival curves often rely on computationally intensive bootstrap techniques to approximate the null hypothesis distribution. While effective, these methods impose significant computational burdens. In this work, we propose a novel approach that leverages the k-means and log-rank test to efficiently identify and cluster survival curves. Our method eliminates the need for computationally expensive resampling, significantly reducing processing time while maintaining statistical reliability. By systematically evaluating survival curves and determining optimal clusters, the proposed method ensures a practical and scalable alternative for large-scale survival data analysis. Through simulation studies, we demonstrate that our approach achieves results comparable to existing bootstrap-based clustering methods while dramatically improving computational efficiency. These findings suggest that the log-rank-based clustering procedure offers a viable and time-efficient solution for researchers working with multiple survival curves in medical and epidemiological studies.

翻译：生存分析涵盖了一系列分析时间-事件数据的方法，其核心目标之一是比较不同组间的生存曲线。传统的生存曲线聚类识别方法通常依赖于计算密集的自举技术来近似零假设分布。尽管这些方法有效，但会带来显著的计算负担。本研究提出了一种新颖的方法，利用k-means算法和log-rank检验来高效识别并聚类生存曲线。我们的方法无需计算昂贵的重采样，在保持统计可靠性的同时显著减少了处理时间。通过系统评估生存曲线并确定最优聚类，所提出的方法为大规模生存数据分析提供了一种实用且可扩展的替代方案。通过模拟研究，我们证明该方法在计算效率上显著提升的同时，可获得与现有基于自举的聚类方法相当的结果。这些发现表明，基于log-rank的聚类程序为医学和流行病学研究中处理多组生存曲线的研究人员提供了一种可行且高效的时间解决方案。