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Lifetime Data Analysis

, Volume 24, Issue 3, pp 443–463 | Cite as

A regularized variable selection procedure in additive hazards model with stratified case-cohort design

Article
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Abstract

Case-cohort designs are commonly used in large epidemiological studies to reduce the cost associated with covariate measurement. In many such studies the number of covariates is very large. An efficient variable selection method is needed for case-cohort studies where the covariates are only observed in a subset of the sample. Current literature on this topic has been focused on the proportional hazards model. However, in many studies the additive hazards model is preferred over the proportional hazards model either because the proportional hazards assumption is violated or the additive hazards model provides more relevent information to the research question. Motivated by one such study, the Atherosclerosis Risk in Communities (ARIC) study, we investigate the properties of a regularized variable selection procedure in stratified case-cohort design under an additive hazards model with a diverging number of parameters. We establish the consistency and asymptotic normality of the penalized estimator and prove its oracle property. Simulation studies are conducted to assess the finite sample performance of the proposed method with a modified cross-validation tuning parameter selection methods. We apply the variable selection procedure to the ARIC study to demonstrate its practical use.

Keywords

Additive hazards model Diverging number of parameters SCAD Stratified case-cohort design Survival analysis Variable selection 

Notes

Acknowledgements

This work was partially supported by National Institutes of Health Grants (P01 CA 142538, R01 ES 021900). The authors thank the staff and participants of the ARIC study for their important contributions. The ARIC Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01-HC-55022).

Supplementary material

10985_2017_9402_MOESM1_ESM.pdf (134 kb)
Supplementary material 1 (pdf 133 KB)

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Epidemiology and BiostatisticsMemorial Sloan Kettering Cancer CenterNew YorkUSA
  2. 2.Department of BiostatisticsUniversity of North Carolina at Chapel HillChapel HillUSA

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