Regression Analysis of Additive Hazards Model With Latent Variables

• 成果类型: Article
• 署名作者: Pan, Deng; He, Haijin; Song, Xinyuan; Sun, Liuquan
• 署名单位: Huazhong University of Science & Technology; Chinese University of Hong Kong; Chinese Academy of Sciences
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.950083
• 发表日期: 2015
• 页码: 1148-1159
• 关键词: estimators

摘要

We propose an additive hazards model with latent variables to investigate the observed and latent risk factors of the failure time of interest. Each latent risk factor is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a hybrid procedure that combines the expectation maximization (EM) algorithm and the borrow-strength estimation approach to estimate the model parameters. We establish the consistency and asymptotic normality of the parameter estimators. Various nice features, including finite sample performance of the proposed methodology, are demonstrated by simulation studies. Our model is applied to a study concerning the risk factors of chronic kidney disease for Type 2 diabetic patients. Supplementary materials for this article are available online.