ArticleStatistical methods in medical research
Bi-level variable selection for case-cohort studies with group variables.
Article in Statistical methods in medical research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Conditional screening for ultrahigh-dimensional survival data in case-cohort studies.Lifetime data analysis · 2021Article
- Risk Factor Identification in Heterogeneous Disease Progression with L1-Regularized Multi-state Models.Journal of healthcare informatics research · 2021Article
- Feature screening for case-cohort studies with failure time outcome.Scandinavian journal of statistics, theory and applications · 2021Article
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Authors and funding
2 authors.
Funding
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.
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