Exploring Complex Survival Data through Frailty Modeling and Regularization

Xifen Huang, Jinfeng Xu () and Yunpeng Zhou
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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

Mathematics, 2023, vol. 11, issue 21, 1-14

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.

Keywords: complex data; multivariate survival data; inverse Gaussian; log normal; general frailty model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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