淡江大學機構典藏:Item 987654321/122889
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    题名: Analyzing recurrent and nonrecurrent terminal events data in discrete time
    作者: Wen, Chi-Chung;Chen, Yi-Hau
    关键词: Poisson process;frailty model;informative dropout;semiparametric transformation model
    日期: 2022-10-26
    上传时间: 2023-04-28 16:23:07 (UTC+8)
    出版者: Wiley-VCH GmbH.
    摘要: Joint analysis of recurrent and nonrecurrent terminal events has attracted substantial attention in literature. However, there lacks formal methodology for such analysis when the event time data are on discrete scales, even though some modeling and inference strategies have been developed for discrete-time survival analysis. We propose a discrete-time joint modeling approach for the analysis of recurrent and terminal events where the two types of events may be correlated with each other. The proposed joint modeling assumes a shared frailty to account for the dependence among recurrent events and between the recurrent and the terminal terminal events. Also, the joint modeling allows for time-dependent covariates and rich families of transformation models for the recurrent and terminal events. A major advantage of our approach is that it does not assume a distribution for the frailty, nor does it assume a Poisson process for the analysis of the recurrent event. The utility of the proposed analysis is illustrated by simulation studies and two real applications, where the application to the biochemists' rank promotion data jointly analyzes the biochemists' citation numbers and times to rank promotion, and the application to the scleroderma lung study data jointly analyzes the adverse events and off-drug time among patients with the symptomatic scleroderma-related interstitial lung disease.
    關聯: Biometrical Journal
    DOI: 10.1002/bimj.202100361
    显示于类别:[應用數學與數據科學學系] 期刊論文

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