Variable selection in joint frailty models of recurrent and terminal events

• Published in: Biometrics Vol. 76; no. 4; pp. 1330 - 1339
• Main Authors: Han, Dongxiao, Su, Xiaogang, Sun, Liuquan, Zhang, Zhou, Liu, Lei
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.12.2020
• Subjects: Approximation; biometry; Data analysis; death; Frailty; frailty models; informative censoring; proportional hazards models; recurrent event; Statistical models; survival analysis; variable selection; recurrent event; variable selection; informative censoring; survival analysis; frailty models; proportional hazards models
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.13242

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.