Joint modeling of generalized scale-change models for recurrent event and failure time data

doi:10.1007/s10985-022-09573-5

	Joint modeling of generalized scale-change models for recurrent event and failure time data
	Wang, Xiaoyu; Sun, Liuquan
刊名	LIFETIME DATA ANALYSIS
	2022-09-06
页码	33
关键词	Failure time Frailty Generalized scale-change models Informative censoring Joint modeling Recurrent events
ISSN号	1380-7870
DOI	10.1007/s10985-022-09573-5
英文摘要	Recurrent event and failure time data arise frequently in many clinical and observational studies. In this article, we propose a joint modeling of generalized scale-change models for the recurrent event process and the failure time, and allow the two processes to be correlated through a shared frailty. The proposed joint model is flexible in that it requires neither the Poisson assumption for the recurrent event process nor a parametric assumption on the frailty distribution. Estimating equation approaches are developed for parameter estimation, and the asymptotic properties of the resulting estimators are established. Simulation studies are conducted to evaluate the finite sample performances of the proposed method. An application to a medical cost study of chronic heart failure patients is provided.
资助项目	National Natural Science Foundation of China[12171463]
WOS研究方向	Mathematics
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000850416300002
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/61040]
专题	中国科学院数学与系统科学研究院
通讯作者	Sun, Liuquan
作者单位	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, Xiaoyu,Sun, Liuquan. Joint modeling of generalized scale-change models for recurrent event and failure time data[J]. LIFETIME DATA ANALYSIS,2022:33.
APA	Wang, Xiaoyu,&Sun, Liuquan.(2022).Joint modeling of generalized scale-change models for recurrent event and failure time data.LIFETIME DATA ANALYSIS,33.
MLA	Wang, Xiaoyu,et al."Joint modeling of generalized scale-change models for recurrent event and failure time data".LIFETIME DATA ANALYSIS (2022):33.