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Bi-level variable selection for case-cohort studies with group variables.

ABSTRACT: The case-cohort design is an economical approach to estimate the effect of risk factors on the survival outcome when collecting exposure information or covariates on all patients is expensive in a large cohort study. Variables often have group structure such as categorical variables and highly correlated continuous variables. The existing literature for case-cohort data is limited to identifying non-zero variables at individual level only. In this article, we propose a bi-level variable selection method to select non-zero group and within-group variables for case-cohort data when variables have group structure. The proposed method allows the number of variables to diverge as the sample size increases. The asymptotic properties of the estimator including bi-level variable selection consistency and the asymptotic normality are shown. We also conduct simulations to compare our proposed method with some existing method and apply them to the Busselton Health data.

SUBMITTER: Kim S

PROVIDER: S-EPMC6748310 | biostudies-literature | 2019 Oct-Nov

REPOSITORIES: biostudies-literature

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Bi-level variable selection for case-cohort studies with group variables.

Kim Soyoung S Woo Ahn Kwang K

Statistical methods in medical research 20181011 10-11

The case-cohort design is an economical approach to estimate the effect of risk factors on the survival outcome when collecting exposure information or covariates on all patients is expensive in a large cohort study. Variables often have group structure such as categorical variables and highly correlated continuous variables. The existing literature for case-cohort data is limited to identifying non-zero variables at individual level only. In this article, we propose a bi-level variable selectio ...[more]

PMID: 30306838

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