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Variable selection in competing risks models based on quantile regression.
- Source :
- Statistics in Medicine; 10/15/2019, Vol. 38 Issue 23, p4670-4685, 16p
- Publication Year :
- 2019
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- QUANTILE regression
COMPETING risks
MONTE Carlo method
REGRESSION analysis
BONE marrow
ACUTE myeloid leukemia treatment
COMPUTER simulation
RESEARCH
BONE marrow transplantation
RESEARCH methodology
EVALUATION research
MEDICAL cooperation
COMPARATIVE studies
SYSTEM analysis
RESEARCH funding
PROPORTIONAL hazards models
Subjects
Details
- Language :
- English
- ISSN :
- 02776715
- Volume :
- 38
- Issue :
- 23
- Database :
- Complementary Index
- Journal :
- Statistics in Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 138540049
- Full Text :
- https://doi.org/10.1002/sim.8326