Simultaneous variable selection in regression analysis of multivariate interval-censored data

doi:10.1111/biom.13548

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 应用数学研究所

	Simultaneous variable selection in regression analysis of multivariate interval-censored data
	Sun, Liuquan 1,4; Li, Shuwei 4; Wang, Lianming 5; Song, Xinyuan 2; Sui, Xuemei 3
刊名	BIOMETRICS
	2021-09-07
页码	12
关键词	EM algorithm interval censoring minimum information criterion multivariate analysis transformation models
ISSN号	0006-341X
DOI	10.1111/biom.13548
英文摘要	Multivariate interval-censored data arise when each subject under study can potentially experience multiple events and the onset time of each event is not observed exactly but is known to lie in a certain time interval formed by adjacent examination times with changed statuses of the event. This type of incomplete and complex data structure poses a substantial challenge in practical data analysis. In addition, many potential risk factors exist in numerous studies. Thus, conducting variable selection for event-specific covariates simultaneously becomes useful in identifying important variables and assessing their effects on the events of interest. In this paper, we develop a variable selection technique for multivariate interval-censored data under a general class of semiparametric transformation frailty models. The minimum information criterion (MIC) method is embedded in the optimization step of the proposed expectation-maximization (EM) algorithm to obtain the parameter estimator. The proposed EM algorithm greatly reduces the computational burden in maximizing the observed likelihood function, and the MIC naturally avoids selecting the optimal tuning parameter as needed in many other popular penalties, making the proposed algorithm promising and reliable. The proposed method is evaluated through extensive simulation studies and illustrated by an analysis of patient data from the Aerobics Center Longitudinal Study.
资助项目	Key Laboratory of RCSDS, CAS[2008DP173182] ; Natural Science Foundation of Guangdong Province[2021A1515010044] ; Science and Technology Programof Guangzhou of China[202102010512] ; Research Grant Council of the Hong Kong SpecialAdministrativeRegion[14301918] ; Research Grant Council of the Hong Kong SpecialAdministrativeRegion[14302519] ; National Institutes of Health[R01CA218578] ; NationalNatural Science Foundation of China[11771431] ; NationalNatural Science Foundation of China[11690015] ; NationalNatural Science Foundation of China[11901128]
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics
语种	英语
出版者	WILEY
WOS记录号	WOS:000695315100001
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/59275]
专题	应用数学研究所
通讯作者	Li, Shuwei
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing, Peoples R China 2.Chinese Univ Hong Kong, Dept Stat, Hong Kong, Peoples R China 3.Univ South Carolina, Arnold Sch Publ Hlth, Dept Exercise Sci, Columbia, SC 29208 USA 4.Guangzhou Univ, Sch Econ & Stat, Guangzhou, Peoples R China 5.Univ South Carolina, Dept Stat, Columbia, SC 29208 USA
推荐引用方式 GB/T 7714	Sun, Liuquan,Li, Shuwei,Wang, Lianming,et al. Simultaneous variable selection in regression analysis of multivariate interval-censored data[J]. BIOMETRICS,2021:12.
APA	Sun, Liuquan,Li, Shuwei,Wang, Lianming,Song, Xinyuan,&Sui, Xuemei.(2021).Simultaneous variable selection in regression analysis of multivariate interval-censored data.BIOMETRICS,12.
MLA	Sun, Liuquan,et al."Simultaneous variable selection in regression analysis of multivariate interval-censored data".BIOMETRICS (2021):12.