Conditional screening for ultrahigh-dimensional survival data in case-cohort studies

Jing Zhang (), Haibo Zhou (), Yanyan Liu () and Jianwen Cai ()
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Jing Zhang: Zhongnan University of Economics and Law
Haibo Zhou: University of North Carolina at Chapel Hill
Yanyan Liu: Wuhan University
Jianwen Cai: University of North Carolina at Chapel Hill

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2021, vol. 27, issue 4, No 4, 632-661

Abstract: Abstract The case-cohort design has been widely used to reduce the cost of covariate measurements in large cohort studies. In many such studies, the number of covariates is very large, and the goal of the research is to identify active covariates which have great influence on response. Since the introduction of sure independence screening, screening procedures have achieved great success in terms of effectively reducing the dimensionality and identifying active covariates. However, commonly used screening methods are based on marginal correlation or its variants, they may fail to identify hidden active variables which are jointly important but are weakly correlated with the response. Moreover, these screening methods are mainly proposed for data under the simple random sampling and can not be directly applied to case-cohort data. In this paper, we consider the ultrahigh-dimensional survival data under the case-cohort design, and propose a conditional screening method by incorporating some important prior known information of active variables. This method can effectively detect hidden active variables. Furthermore, it possesses the sure screening property under some mild regularity conditions and does not require any complicated numerical optimization. We evaluate the finite sample performance of the proposed method via extensive simulation studies and further illustrate the new approach through a real data set from patients with breast cancer.

Keywords: Case-cohort design; Conditional screening; Sure screening property; Survival data; Ultrahigh-dimensional data; Weighted estimating equation (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10985-021-09531-7

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