Variable Selection in Additive Hazards Model with Case-Cohort Design

• Activity Number: 174
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Andy (Ai) Ni* and Jianwen Cai
• Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
• Keywords: Variable Selection ; Diverging Dimension ; SCAD ; Survival Analysis ; Case-Cohort Design ; Additive Hazards Model

Abstract

Case-cohort design is widely used in large epidemiological studies to reduce the cost associated with covariate measurement. In many of those studies the number of covariates is large, especially with the increasing availability of Big Data. Therefore, an efficient variable selection method is needed for case-cohort design. Motivated by the Atherosclerosis Risk in Communities (ARIC) study, in this paper we investigate the properties of the Smoothly Clipped Absolute Deviation (SCAD) penalty based variable selection procedure in case-cohort design under additive hazards model. We establish the consistency and asymptotic normality of the penalized estimator. We also show that the proposed model selection procedure can identify the true model with probability one asymptotically, and estimates the nonzero parameters as efficiently as if the true model is known a priori. Simulation studies are conducted to assess and compare the finite sample performance of the proposed variable selection procedure with different tuning parameter selection methods. We make recommendations for practical use of the variable selection procedures. The proposed procedure is used to analyze the ARIC Study.