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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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7cc338c7e96c40adb68d93ff2f34ced3
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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.

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