Kernel regression for cause-specific hazard models with time-dependent coefficients

Xiaomeng Qi and Zhangsheng Yu ()
Additional contact information
Xiaomeng Qi: Shanghai Jiao Tong University
Zhangsheng Yu: Shanghai Jiao Tong University

Computational Statistics, 2023, vol. 38, issue 1, No 13, 263-283

Abstract: Abstract Competing risk data appear widely in modern biomedical research. In the past two decades, cause-specific hazard models are often used to deal with competing risk data. There is no current study on the kernel likelihood method for the cause-specific hazard model with time-varying coefficients. We propose to use the local partial log-likelihood approach for nonparametric time-varying coefficient estimation. Simulation studies demonstrate that our proposed nonparametric kernel estimator performs well under assumed finite sample settings. And we also compare the local kernel estimator with the penalized spline estimator. Finally, we apply the proposed method to analyze a diabetes dialysis study with competing death causes.

Keywords: Cause-specific hazard; Cross-validation; Kernel weight; Local partial log-likelihood (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-022-01227-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:38:y:2023:i:1:d:10.1007_s00180-022-01227-2

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-022-01227-2

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().