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Joint modeling of generalized scale-change models for recurrent event and failure time data
Wang, Xiaoyu; Sun, Liuquan
2022-09-06
Source PublicationLIFETIME DATA ANALYSIS
ISSN1380-7870
Pages33
AbstractRecurrent 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.
KeywordFailure time Frailty Generalized scale-change models Informative censoring Joint modeling Recurrent events
DOI10.1007/s10985-022-09573-5
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[12171463]
WOS Research AreaMathematics
WOS SubjectMathematics, Interdisciplinary Applications ; Statistics & Probability
WOS IDWOS:000850416300002
PublisherSPRINGER
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/61041
Collection博士后
应用数学研究所
Corresponding AuthorSun, Liuquan
AffiliationChinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing 100190, Peoples R China
Recommended Citation
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.
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