Variable selection with Group LASSO approach : Application to Cox regression with frailty model
In analysis of survival outcomes supplemented with both clinical
information and high-dimensional gene expression data, use of the
traditional Cox proportional hazards model fails to meet some
emerging needs in biomedical research. First, the number of covariates
is generally much larger the sample size. Secondly, predicting
an outcome based on individual gene expression is inadequate
because multiple biological processes and functional pathways regulate
phenotypic expression. Another challenge is that the Cox model
assumes that populations are homogenous, implying that all individuals
have the same risk of death, which is rarely true due to unmeasured
risk factors among populations. In this paper we propose group
LASSO with gamma-distributed frailty for variable selection in Cox
regression by extending previous scholarship to account for heterogeneity
among group structures related to exposure and susceptibility.
The consistency property of the proposed method is established.
This method is appropriate for addressing a wide variety of research
questions from genetics to air pollution. Simulated and real world
data analysis shows promising performance by group LASSO compared
with other methods, including group SCAD and group MCP.
Future research directions include expanding the use of frailty with
adaptive group LASSO and sparse group LASSO methods.
Auteur(s) : Jean Claude Utazirubanda, Tomas Leon, Papa Ngom,
Année de publication : 2019
Revue : Communications in Statistics - Simulation and Computation
Type : Article
Mise en ligne par : NGOM Papa