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

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 corre...

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Bibliographic Details
Published inStatistical methods in medical research Vol. 28; no. 10-11; p. 3404
Main Authors Kim, Soyoung, Woo Ahn, Kwang
Format Journal Article
LanguageEnglish
Published England 01.11.2019
Subjects
Biomarkers - blood
Cohort Studies
Ferritins - blood
Humans
Models, Statistical
Research Design
Risk Assessment - methods
Risk Factors
Sample Size
Stroke - blood
Case-cohort design
efficiency
multiple diseases
variable selection
survival analysis
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Summary: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.
ISSN:1477-0334
DOI:10.1177/0962280218803654