Variable selection for high-dimensional partly linear additive Cox model with application to Alzheimer's disease.
Variable selection has been discussed under many contexts and especially, a large literature has been established for the analysis of right-censored failure time data. In this article, we discuss an interval-censored failure time situation where there exist two sets of covariates with one being low-...
| Publicado en: | Statistics in Medicine Vol. 39; no. 23; pp. 3120 - 3135 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
10/15/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145697786&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145697786 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: 10/15/2020 vid: 39 iid: 23 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 145697786 145697786 145856196 NLM32652699 145697786 10.1002/sim.8594 NLM32652699 145697786 ppf: 3120 ppct: 15 formats: tig: atl: Variable selection for high-dimensional partly linear additive Cox model with application to Alzheimer's disease. aug: au: Wu, Qiwei Zhao, Hui Zhu, Liang Sun, Jianguo affil: Eli Lilly and Company, Indianapolis, Indiana, USA sug: subj: Alzheimer's Disease Drug Therapy Linear Regression Human Algorithms Cox Proportional Hazards Model Computer Simulation Comparative Studies Multicenter Studies Evaluation Research Validation Studies ab: Variable selection has been discussed under many contexts and especially, a large literature has been established for the analysis of right-censored failure time data. In this article, we discuss an interval-censored failure time situation where there exist two sets of covariates with one being low-dimensional and having possible nonlinear effects and the other being high-dimensional. For the problem, we present a penalized estimation procedure for simultaneous variable selection and estimation, and in the method, Bernstein polynomials are used to approximate the involved nonlinear functions. Furthermore, for implementation, a coordinate-wise optimization algorithm, which can accommodate most commonly used penalty functions, is developed. A numerical study is performed for the evaluation of the proposed approach and suggests that it works well in practical situations. Finally the method is applied to an Alzheimer's disease study that motivated this investigation. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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