SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting.


Journal

The R journal
ISSN: 2073-4859
Titre abrégé: R J
Pays: United States
ID NLM: 101554666

Informations de publication

Date de publication:
Jun 2020
Historique:
entrez: 7 6 2021
pubmed: 8 6 2021
medline: 8 6 2021
Statut: ppublish

Résumé

High-dimensional variable selection in the proportional hazards (PH) model has many successful applications in different areas. In practice, data may involve confounding variables that do not satisfy the PH assumption, in which case the stratified proportional hazards (SPH) model can be adopted to control the confounding effects by stratification without directly modeling the confounding effects. However, there is a lack of computationally efficient statistical software for high-dimensional variable selection in the SPH model. In this work an R package,

Identifiants

pubmed: 34094592
doi: 10.32614/rj-2020-018
pmc: PMC8174798
mid: NIHMS1656432
doi:

Types de publication

Journal Article

Langues

eng

Pagination

105-117

Subventions

Organisme : NIDA NIH HHS
ID : R01 DA048993
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM124061
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH105561
Pays : United States

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Auteurs

Emily Morris (E)

Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109.

Kevin He (K)

Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109.

Yanming Li (Y)

Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109.

Yi Li (Y)

Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109.

Jian Kang (J)

Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109.

Classifications MeSH