Variable selection in joint frailty models of recurrent and terminal events

Dongxiao Han, Xiaogang Su, Liuquan Sun, Zhou Zhang, Lei Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Recurrent event data are commonly encountered in biomedical studies. In many situations, they are subject to an informative terminal event, for example, death. Joint modeling of recurrent and terminal events has attracted substantial recent research interests. On the other hand, there may exist a large number of covariates in such data. How to conduct variable selection for joint frailty proportional hazards models has become a challenge in practical data analysis. We tackle this issue on the basis of the “minimum approximated information criterion” method. The proposed method can be conveniently implemented in SAS Proc NLMIXED for commonly used frailty distributions. Its finite-sample behavior is evaluated through simulation studies. We apply the proposed method to model recurrent opportunistic diseases in the presence of death in an AIDS study.

Original languageEnglish (US)
Pages (from-to)1330-1339
Number of pages10
JournalBiometrics
Volume76
Issue number4
DOIs
StatePublished - Dec 2020

Keywords

  • frailty models
  • informative censoring
  • proportional hazards models
  • recurrent event
  • survival analysis
  • variable selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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