Analysis of proportional mean residual life model with latent variables

详细信息    查看全文

• 作者：Haijin He ; Jingheng Cai ; Xinyuan Song and Liuquan Sun
• 刊名：Statistics in Medicine
• 出版年：2017
• 出版时间：28 February 2017
• 年：2017
• 卷：36
• 期：5
• 页码：813-826
• 全文大小：330K
• ISSN：1097-0258

文摘

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