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Exploring Complex Survival Data through Frailty Modeling and Regularization

Author

Listed:
  • Xifen Huang

    (School of Mathematics, Yunnan Normal University, Kunming 650092, China)

  • Jinfeng Xu

    (School of Mathematics, Yunnan Normal University, Kunming 650092, China)

  • Yunpeng Zhou

    (Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam, Hong Kong, China)

Abstract

This study addresses the analysis of complex multivariate survival data, where each individual may experience multiple events and a wide range of relevant covariates are available. We propose an advanced modeling approach that extends the classical shared frailty framework to account for within-subject dependence. Our model incorporates a flexible frailty distribution, encompassing well-known distributions, such as gamma, log-normal, and inverse Gaussian. To ensure accurate estimation and effective model selection, we utilize innovative regularization techniques. The proposed methodology exhibits desirable theoretical properties and has been validated through comprehensive simulation studies. Additionally, we apply the approach to real-world data from the Medical Information Mart for Intensive Care (MIMIC-III) dataset, demonstrating its practical utility in analyzing complex survival data structures.

Suggested Citation

  • Xifen Huang & Jinfeng Xu & Yunpeng Zhou, 2023. "Exploring Complex Survival Data through Frailty Modeling and Regularization," Mathematics, MDPI, vol. 11(21), pages 1-14, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:21:p:4440-:d:1268168
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    References listed on IDEAS

    as
    1. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    2. Andreas Groll & Trevor Hastie & Gerhard Tutz, 2017. "Selection of effects in Cox frailty models by regularization methods," Biometrics, The International Biometric Society, vol. 73(3), pages 846-856, September.
    3. Shujie Ma & Jian Huang, 2017. "A Concave Pairwise Fusion Approach to Subgroup Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 410-423, January.
    4. Ding, Jieli & Tian, Guo-Liang & Yuen, Kam Chuen, 2015. "A new MM algorithm for constrained estimation in the proportional hazards model," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 135-151.
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