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<p>We study the variable selection problem in survival analysis to identify the most important factors affecting survival time. Our method incorporates prior knowledge of mutual correlations among variables, represented through a graph. We utilize the Cox proportional hazard model with a graph-based regularizer for variable selection. We present a computationally efficient algorithm which is developed to solve the graph regularized maximum likelihood problem by establishing connections with the group lasso and provide theoretical guarantees about the recovery error and asymptotic distribution of the proposed estimators. The improved performance of the proposed approach compared with existing methods is demonstrated in both synthetic and real organ transplantation datasets.</p>

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Survival Analysis with Graph-Based Regularization for Predictors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月12日

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  • Liyan Xie,
  • Xi He,
  • Pinar Keskinocak,
  • Yao Xie

摘要

We study the variable selection problem in survival analysis to identify the most important factors affecting survival time. Our method incorporates prior knowledge of mutual correlations among variables, represented through a graph. We utilize the Cox proportional hazard model with a graph-based regularizer for variable selection. We present a computationally efficient algorithm which is developed to solve the graph regularized maximum likelihood problem by establishing connections with the group lasso and provide theoretical guarantees about the recovery error and asymptotic distribution of the proposed estimators. The improved performance of the proposed approach compared with existing methods is demonstrated in both synthetic and real organ transplantation datasets.