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Stat Med

Title:		Structured sparse logistic regression with application to lung cancer prediction using breath volatile biomarkers
Author(s):		Zhang X; Zhang Q; Wang X; Ma S; Fang K;
Address:		"Department of Statistics, School of Economics, Xiamen University, China. The Wang Yanan Institute for Studies in Economics, Xiamen University, China. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio. Department of Biostatistics, Yale University, New Haven, Connecticut"
Journal Title:		Stat Med
Year:		2020
Volume:		20191227
Issue:		7
Page Number:		955 - 967
DOI:		10.1002/sim.8454
ISSN/ISBN:		1097-0258 (Electronic) 0277-6715 (Linking)
Abstract:		"This article is motivated by a study of lung cancer prediction using breath volatile organic compound (VOC) biomarkers, where the challenge is that the predictors include not only high-dimensional time-dependent or functional VOC features but also the time-independent clinical variables. We consider a high-dimensional logistic regression and propose two different penalties: group spline-penalty or group smooth-penalty to handle the group structures of the time-dependent variables in the model. The new methods have the advantage for the situation where the model coefficients are sparse but change smoothly within the group, compared with other existing methods such as the group lasso and the group bridge approaches. Our methods are easy to implement since they can be turned into a group minimax concave penalty problem after certain transformations. We show that our fitting algorithm possesses the descent property and leads to attractive convergence properties. The simulation studies and the lung cancer application are performed to demonstrate the accuracy and stability of the proposed approaches"
Keywords:		*Algorithms Biomarkers Computer Simulation Humans Logistic Models *Lung Neoplasms/diagnosis group smooth-penalty group spline-penalty high-dimensional data time-dependent variables variable selection;
Notes:		"MedlineZhang, Xiaochen Zhang, Qingzhao Wang, Xiaofeng Ma, Shuangge Fang, Kuangnan eng Research Support, Non-U.S. Gov't England 2019/12/28 Stat Med. 2020 Mar 30; 39(7):955-967. doi: 10.1002/sim.8454. Epub 2019 Dec 27"