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Regression analysis of restricted mean survival time based on pseudo-observations for competing risks data

文献类型：期刊论文

作者Wang, Xin1; Xue, Xiaoming2; Sun, Liuquan2
刊名COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
出版日期2018
卷号47期号:22页码:5614-5625
关键词Competing risks Generalized estimating equation Pseudo-observations Regression models Restricted mean
ISSN号0361-0926
DOI10.1080/03610926.2017.1397174
英文摘要Competing risks data often occur in many medical follow-up studies. When the survival time is the outcome variable, the restricted mean survival time has heuristic and clinically meaningful interpretation. In this article, we propose a class of regression models for the restricted mean survival time in the competing risks setting. We adopt a technique of pseudo-observations to develop estimating equation approaches for the model parameters and establish asymptotic properties of the resulting estimators. The finite-sample behavior of the proposed method is evaluated through simulation studies, and an application to the Women's Interagency HIV Study is provided.
WOS研究方向Mathematics
语种英语
WOS记录号WOS:000443430800018
出版者TAYLOR & FRANCIS INC
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/31182]  
专题应用数学研究所
通讯作者Sun, Liuquan
作者单位1.Beijing Informat Sci & Technol Univ, Sch Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Xin,Xue, Xiaoming,Sun, Liuquan. Regression analysis of restricted mean survival time based on pseudo-observations for competing risks data[J]. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS,2018,47(22):5614-5625.
APA Wang, Xin,Xue, Xiaoming,&Sun, Liuquan.(2018).Regression analysis of restricted mean survival time based on pseudo-observations for competing risks data.COMMUNICATIONS IN STATISTICS-THEORY AND METHODS,47(22),5614-5625.
MLA Wang, Xin,et al."Regression analysis of restricted mean survival time based on pseudo-observations for competing risks data".COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 47.22(2018):5614-5625.

入库方式： OAI收割

来源：数学与系统科学研究院

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