ArticleLifetime data analysis2018
Conditional screening for ultra-high dimensional covariates with survival outcomes.
Article in Lifetime data analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A Group Structure Guided Ultra-High Dimensional Feature Screening for Survival Outcome.Statistics in medicine · 2026Article
- Quantile forward regression for high-dimensional survival data.Lifetime data analysis · 2023Article
- EFFICIENT ESTIMATION OF THE MAXIMAL ASSOCIATION BETWEEN MULTIPLE PREDICTORS AND A SURVIVAL OUTCOME.Annals of statistics · 2023Article
- High-Dimensional Survival Analysis: Methods and Applications.Annual review of statistics and its application · 2023Article
- Prior Knowledge Guided Ultra-high Dimensional Variable Screening with Application to Neuroimaging Data.Statistica Sinica · 2022Article
- Conditional screening for ultrahigh-dimensional survival data in case-cohort studies.Lifetime data analysis · 2021Article
- The Lq- NORM LEARNING FOR ULTRAHIGH-DIMENSIONAL SURVIVAL DATA: AN INTEGRATIVE FRAMEWORK.Statistica Sinica · 2020Article
- A new joint screening method for right-censored time-to-event data with ultra-high dimensional covariates.Statistical methods in medical research · 2020Article
- Cox regression increases power to detect genotype-phenotype associations in genomic studies using the electronic health record.BMC genomics · 2019Article
- Marginal screening for high-dimensional predictors of survival outcomes.Statistica Sinica · 2019Article
- Forward regression for Cox models with high-dimensional covariates.Journal of multivariate analysis · 2019Article
- Integrated powered density: Screening ultrahigh dimensional covariates with survival outcomes.Biometrics · 2018Article
- Special issue dedicated to Jack Kalbfleisch.Lifetime data analysis · 2018Article
- Feature selection of ultrahigh-dimensional covariates with survival outcomes: a selective review.Applied mathematics : a journal of Chinese universities · 2017Article
- Partition-based ultrahigh-dimensional variable screening.Biometrika · 2017Article
- A selective overview of feature screening methods with applications to neuroimaging data.Wiley interdisciplinary reviews. Computational statisticsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Identifying important biomarkers that are predictive for cancer patients' prognosis is key in gaining better insights into the biological influences on the disease and has become a critical component of precision medicine. The emergence of large-scale biomedical survival studies, which typically involve excessive number of biomarkers, has brought high demand in designing efficient screening tools for selecting predictive biomarkers. The vast amount of biomarkers defies any existing variable selection methods via regularization. The recently developed variable screening methods, though powerful in many practical setting, fail to incorporate prior information on the importance of each biomarker and are less powerful in detecting marginally weak while jointly important signals. We propose a new conditional screening method for survival outcome data by computing the marginal contribution of each biomarker given priorily known biological information. This is based on the premise that some biomarkers are known to be associated with disease outcomes a priori. Our method possesses sure screening properties and a vanishing false selection rate. The utility of the proposal is further confirmed with extensive simulation studies and analysis of a diffuse large B-cell lymphoma dataset. We are pleased to dedicate this work to Jack Kalbfleisch, who has made instrumental contributions to the development of modern methods of analyzing survival data.
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