Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Dec 20;40(29):6541-6557.
doi: 10.1002/sim.9197. Epub 2021 Sep 20.

Penalized variable selection for cause-specific hazard frailty models with clustered competing-risks data

Trias W Rakhmawati  1 , Il Do Ha  2 , Hangbin Lee  1 , Youngjo Lee  1
Affiliations

Penalized variable selection for cause-specific hazard frailty models with clustered competing-risks data

Trias W Rakhmawati et al. Stat Med. .

Abstract

Competing risks data usually arise when an occurrence of an event precludes other types of events from being observed. Such data are often encountered in a clustered clinical study such as a multi-center clinical trial. For the clustered competing-risks data which are correlated within a cluster, competing-risks models allowing for frailty terms have been recently studied. To the best of our knowledge, however, there is no literature on variable selection methods for cause-specific hazard frailty models. In this article, we propose a variable selection procedure for fixed effects in cause-specific competing risks frailty models using a penalized h-likelihood (HL). Here, we study three penalty functions, LASSO, SCAD, and HL. Simulation studies demonstrate that the proposed procedure using the HL penalty works well, providing a higher probability of choosing the true model than LASSO and SCAD methods without losing prediction accuracy. The proposed method is illustrated by using two kinds of clustered competing-risks cancer data sets.

Keywords: competing risks; frailty models; h-likelihood; penalized likelihood; variable selection.

PubMed Disclaimer

References

REFERENCES

    1. Prentice R, Kalbfleisch J-D, Peterson A-V, Flournoy N, Farewell V-T, Breslow N-E. The analysis of failure times in the presence of competing risks. Biometrics. 1978;34(4):541-554.
    1. Fine J-P, Gray R-J. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc. 1999;94(446):496-509.
    1. Katsahian S, Boudreau C. Estimating and testing for center effects in competing risks. Stat Med. 2011;30(13):1608-1617.
    1. Gorfine M, Hsu L. Frailty-based competing risks model for multivariate survival data. Biometrics. 2011;67(2):415-426.
    1. Christian N-J, Ha I-D, Jeong J-H. Hierarchical likelihood inference on clustered competing risks data. Stat Med. 2016;35(2):251-267.

Publication types