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Variable selection in competing risks models based on quantile regression
Author(s) -
Li Erqian,
Tian Maozai,
Tang ManLai
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8326
Subject(s) - covariate , quantile regression , estimator , consistency (knowledge bases) , computer science , quantile , statistics , econometrics , regression analysis , model selection , proportional hazards model , mathematics , artificial intelligence
The proportional subdistribution hazard regression model has been widely used by clinical researchers for analyzing competing risks data. It is well known that quantile regression provides a more comprehensive alternative to model how covariates influence not only the location but also the entire conditional distribution. In this paper, we develop variable selection procedures based on penalized estimating equations for competing risks quantile regression. Asymptotic properties of the proposed estimators including consistency and oracle properties are established. Monte Carlo simulation studies are conducted, confirming that the proposed methods are efficient. A bone marrow transplant data set is analyzed to demonstrate our methodologies.