Exploring Complex Survival Data through Frailty Modeling and Regularization

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-subj...

Full description

Saved in:
Bibliographic Details
Published inMathematics (Basel) Vol. 11; no. 21; p. 4440
Main Authors Huang, Xifen, Xu, Jinfeng, Zhou, Yunpeng
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2023
Subjects
Algorithms
Analysis
Analysis of covariance
complex data
Complex systems
Data structures
Feature selection
Fines & penalties
Frailty
general frailty model
Integrals
inverse Gaussian
log normal
Methods
Modelling
Multivariate analysis
multivariate survival data
Parameter estimation
Regularization
Survival
Survival after airplane accidents, shipwrecks, etc
China
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2227-7390
2227-7390
DOI:10.3390/math11214440