Variable selection in competing risks models based on quantile regression.
Stat Med
; 38(23): 4670-4685, 2019 10 15.
Article
en En
| MEDLINE
| ID: mdl-31359443
ABSTRACT
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Modelos de Riesgos Proporcionales
Tipo de estudio:
Etiology_studies
/
Health_economic_evaluation
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Stat Med
Año:
2019
Tipo del documento:
Article
País de afiliación:
China