title: Analysis of proportional mean residual life model with latent variables.
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 © 2016 John Wiley & Sons, Ltd.
authors.affiliation: College of Mathematics and Computer Science, Shenzhen University, Shenzhen, China.
authors.forename: Haijin
authors.initials: H
authors.lastname: He
authors.affiliation: Department of Statistics, Sun Yat-sen University, Guangzhou, China.
authors.forename: Jingheng
authors.initials: J
authors.lastname: Cai
authors.affiliation: Shenzhen Research Institute and Department of Statistics, The Chinese University of Hong Kong, Hong Kong.
authors.forename: Xinyuan
authors.initials: X
authors.lastname: Song
authors.affiliation: Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
authors.forename: Liuquan
authors.initials: L
authors.lastname: Sun
mesh_terms: D000465:Algorithms; D003924:Diabetes Mellitus, Type 2 / Q000150:complications; D005163:Factor Analysis, Statistical; D006801:Humans; D007676:Kidney Failure, Chronic / Q000453:epidemiology / Q000209:etiology*; D015233:Models, Statistical; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors
keywords: borrow-strength estimation; extended estimating equations; factor analysis; latent variables; mean residual life function; proportional model