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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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| Gepubliceerd in: | BMC Bioinformatics |
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| Hoofdauteurs: | , , , |
| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
BioMed Central
2020
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| Onderwerpen: | |
| Online toegang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7331150/ https://ncbi.nlm.nih.gov/pubmed/32615919 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12859-020-03618-y |
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