Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models

BACKGROUND: The standard lasso penalty and its extensions are commonly used to develop a regularized regression model while selecting candidate predictor variables on a time-to-event outcome in high-dimensional data. However, these selection methods focus on a homogeneous set of variables and do not...

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Detalles Bibliográficos
Autores principales: Belhechmi, Shaima, Bin, Riccardo De, Rotolo, Federico, Michiels, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7331150/
https://www.ncbi.nlm.nih.gov/pubmed/32615919
http://dx.doi.org/10.1186/s12859-020-03618-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7331150/
https://www.ncbi.nlm.nih.gov/pubmed/32615919
http://dx.doi.org/10.1186/s12859-020-03618-y

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