Feature screening in ultrahigh-dimensional varying-coefficient Cox model.

Guangren Yang, Ling Zhang, Runze Li, Yuan Huang

Abstract read

In one paragraph

Article in Journal of multivariate analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

4 authors.

Guangren YangDepartment of Statistics, School of Economics, Jinan University, Guangzhou, China 510632.

Ling ZhangDepartment of Statistics, The Pennsylvania State University, University Park, PA 16802, USA.

Runze LiDepartment of Statistics and the Methodology Center, The Pennsylvania State University, University Park, PA 16802, USA.

Yuan HuangDepartment of Biostatistics, University of Iowa, Iowa City, IA 52242, USA.

Funding

Pilot, Mentoring, and Professional Development CoreP50DA039838 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI LI, RUNZE · 2015 to 2019

$13.9M

Disease-Modifying Genes in Huntington's DiseaseR01NS091161 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI JAMES F GUSELLA · 2015 to 2026

$7.2M

NIDA NIH HHS P50 DA039838NINDS NIH HHS R01 NS091161

6 · The paper itself

Abstract

The varying-coefficient Cox model is flexible and useful for modeling the dynamic changes of regression coefficients in survival analysis. In this paper, we study feature screening for varying-coefficient Cox models in ultrahigh-dimensional covariates. The proposed screening procedure is based on the joint partial likelihood of all predictors, thus different from marginal screening procedures available in the literature. In order to carry out the new procedure, we propose an effective algorithm and establish its ascent property. We further prove that the proposed procedure possesses the sure screening property. That is, with probability tending to 1, the selected variable set includes the actual active predictors. We conducted simulations to evaluate the finite-sample performance of the proposed procedure and compared it with marginal screening procedures. A genomic data set is used for illustration purposes.

Indexed as

Cox modelPartial likelihoodPenalized likelihoodUltrahigh-dimensional survival data

Identifiers

PMID31866699

DOI10.1016/j.jmva.2018.12.009

PMCPMC6924954

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.