Efficient and scalable clustering of survival curves

TL;DR

This paper introduces a scalable clustering method for survival curves that uses k-means and log-rank tests, significantly reducing computation time while maintaining accuracy, suitable for large-scale survival data analysis.

Contribution

The paper presents a novel, efficient clustering approach for survival curves that eliminates the need for bootstrap resampling, improving scalability and speed.

Findings

Achieves comparable accuracy to bootstrap methods

Dramatically reduces computational time

Effective for large-scale survival datasets

Abstract

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.