TY - JOUR
T1 - Weighted competing risks quantile regression models and variable selection
AU - Li, Erqian
AU - Pan, Jianxin
AU - Tang, Man Lai
AU - Yu, Keming
AU - Wolfgang Karl, Hardle
AU - Dai, Xiaowen
AU - Tian, Maozai
N1 - © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
PY - 2023/3/8
Y1 - 2023/3/8
N2 - The proportional subdistribution hazards (PSH) model is popularly used to deal with competing risks data. Censored quantile regression provides an important supplement as well as variable selection methods due to large numbers of irrelevant covariates in practice. In this paper, we study variable selection procedures based on penalized weighted quantile regression for competing risks models, which is conveniently applied by researchers. Asymptotic properties of the proposed estimators, including consistency and asymptotic normality of non-penalized estimator and consistency of variable selection, are established. Monte Carlo simulation studies are conducted, showing that the proposed methods are considerably stable and efficient. Real data about bone marrow transplant (BMT) are also analyzed to illustrate the application of the proposed procedure.
AB - The proportional subdistribution hazards (PSH) model is popularly used to deal with competing risks data. Censored quantile regression provides an important supplement as well as variable selection methods due to large numbers of irrelevant covariates in practice. In this paper, we study variable selection procedures based on penalized weighted quantile regression for competing risks models, which is conveniently applied by researchers. Asymptotic properties of the proposed estimators, including consistency and asymptotic normality of non-penalized estimator and consistency of variable selection, are established. Monte Carlo simulation studies are conducted, showing that the proposed methods are considerably stable and efficient. Real data about bone marrow transplant (BMT) are also analyzed to illustrate the application of the proposed procedure.
KW - bone marrow transplant
KW - competing risks
KW - cumulative incidence function
KW - re-distribution method
UR - http://www.scopus.com/inward/record.url?scp=85151562110&partnerID=8YFLogxK
U2 - 10.3390/math11061295
DO - 10.3390/math11061295
M3 - Article
SN - 2227-7390
VL - 11
SP - 1
EP - 23
JO - Mathematics
JF - Mathematics
IS - 6
M1 - 1295
ER -