Variable selection for case-cohort studies with failure time outcome

• 成果类型: Article
• 署名作者: Ni, Ai; Cai, Jianwen; Zeng, Donglin
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asw027
• 发表日期: 2016
• 页码: 547562
• 关键词: tuning parameter selection; diverging number; model-selection; PENALIZED LIKELIHOOD; oracle properties; hazards model; disease; Lasso; estimators; regression

摘要

Case-cohort designs are widely used in large cohort studies to reduce the cost associated with covariate measurement. In many such studies the number of covariates is very large, so an efficient variable selection method is necessary. In this paper, we study the properties of a variable selection procedure using the smoothly clipped absolute deviation penalty in a case-cohort design with a diverging number of parameters. We establish the consistency and asymptotic normality of the maximum penalized pseudo-partial-likelihood estimator, and show that the proposed variable selection method is consistent and has an asymptotic oracle property. Simulation studies compare the finite-sample performance of the procedure with tuning parameter selection methods based on the Akaike information criterion and the Bayesian information criterion. We make recommendations for use of the proposed procedures in case-cohort studies, and apply them to the Busselton Health Study.