Using multivariate regression model with least absolute shrinkage and selection operator (LASSO) to predict the incidence of Xerostomia after intensity-modulated radiotherapy for head and neck cancer.

Using multivariate regression model with least absolute shrinkage and selection operator (LASSO) to predict the incidence of Xerostomia after intensity-modulated radiotherapy for head and neck cancer.

<h4>Purpose</h4>The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patient...

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Bibliographic Details
Main Authors: Tsair-Fwu Lee, Pei-Ju Chao, Hui-Min Ting, Liyun Chang, Yu-Jie Huang, Jia-Ming Wu, Hung-Yu Wang, Mong-Fong Horng, Chun-Ming Chang, Jen-Hong Lan, Ya-Yu Huang, Fu-Min Fang, Stephen Wan Leung
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2014-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24586971/pdf/?tool=EBI

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24586971/pdf/?tool=EBI

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