CSpace  > 应用数学研究所
 Analysis of proportional mean residual life model with latent variables He, Haijin1; Cai, Jingheng2; Song, Xinyuan3,4; Sun, Liuquan5 2017-02-01 Source Publication STATISTICS IN MEDICINE ISSN 0277-6715 Volume 36Issue:5Pages:813-826 Abstract End-stage renal disease (ESRD) is one of the most serious diabetes complications. Numerous studies have been devoted to revealing the risk factors of the onset time of ESRD. In this article, we propose a proportional mean residual life (MRL) model with latent variables to assess the effects of observed and latent risk factors on the MRL function of ESRD in a cohort of Chinese type 2 diabetic patients. The proposed model generalizes the conventional proportional MRL model to accommodate the latent risk factor that cannot be measured by a single observed variable. We employ a factor analysis model to characterize the latent risk factors via multiple observed variables. We develop a borrow-strength estimation procedure, which incorporates the expectation-maximization algorithm and an extended estimating equation approach. The asymptotic properties of the proposed estimators are established. Simulation shows that the performance of the proposed methodology is satisfactory. The application to the study of type 2 diabetes reveals insights into the prevention of ESRD. Copyright (C) 2016 John Wiley & Sons, Ltd. Keyword borrow-strength estimation extended estimating equations factor analysis latent variables mean residual life function proportional model DOI 10.1002/sim.7174 Language 英语 Funding Project Research Grant Council of the Hong Kong Special Administration Region[14601115] ; Research Grant Council of the Hong Kong Special Administration Region[14305014] ; Chinese University of Hong Kong ; National Natural Science Foundation of China[11471277] ; National Natural Science Foundation of China[11231010] ; National Natural Science Foundation of China[11171330] ; Key Laboratory of RCSDS, CAS[2008DP173182] WOS Research Area Mathematical & Computational Biology ; Public, Environmental & Occupational Health ; Medical Informatics ; Research & Experimental Medicine ; Mathematics WOS Subject Mathematical & Computational Biology ; Public, Environmental & Occupational Health ; Medical Informatics ; Medicine, Research & Experimental ; Statistics & Probability WOS ID WOS:000393303200007 Publisher WILEY-BLACKWELL Citation statistics Document Type 期刊论文 Identifier http://ir.amss.ac.cn/handle/2S8OKBNM/24689 Collection 应用数学研究所 Corresponding Author Song, Xinyuan Affiliation 1.Shenzhen Univ, Coll Math & Comp Sci, Shenzhen, Peoples R China2.Sun Yat Sen Univ, Dept Stat, Guangzhou, Guangdong, Peoples R China3.Chinese Univ Hong Kong, Shenzhen Res Inst, Hong Kong, Hong Kong, Peoples R China4.Chinese Univ Hong Kong, Dept Stat, Hong Kong, Hong Kong, Peoples R China5.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing, Peoples R China Recommended CitationGB/T 7714 He, Haijin,Cai, Jingheng,Song, Xinyuan,et al. Analysis of proportional mean residual life model with latent variables[J]. STATISTICS IN MEDICINE,2017,36(5):813-826. APA He, Haijin,Cai, Jingheng,Song, Xinyuan,&Sun, Liuquan.(2017).Analysis of proportional mean residual life model with latent variables.STATISTICS IN MEDICINE,36(5),813-826. MLA He, Haijin,et al."Analysis of proportional mean residual life model with latent variables".STATISTICS IN MEDICINE 36.5(2017):813-826.
 Files in This Item: There are no files associated with this item.
 Related Services Recommend this item Bookmark Usage statistics Export to Endnote Google Scholar Similar articles in Google Scholar [He, Haijin]'s Articles [Cai, Jingheng]'s Articles [Song, Xinyuan]'s Articles Baidu academic Similar articles in Baidu academic [He, Haijin]'s Articles [Cai, Jingheng]'s Articles [Song, Xinyuan]'s Articles Bing Scholar Similar articles in Bing Scholar [He, Haijin]'s Articles [Cai, Jingheng]'s Articles [Song, Xinyuan]'s Articles Terms of Use No data! Social Bookmark/Share