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引用次数: 2
摘要
分析重复事件数据的时间尺度的自然选择是连续事件之间的“间隙”(或逗留)时间。在许多情况下,假设给定主体所经历的连续事件之间存在相关性是合理的。本文研究了通过强度建模将加速失效时间(AFT)模型扩展到相关循环事件数据的问题。具体而言,Strawderman(2005)的加速间隙时间模型(独立间隙时间数据的半参数强度模型)被扩展到乘法伽马脆弱的情况。正如Aalen & Husebye(1991)所指出的那样,纳入脆弱性捕获了受试者之间的异质性,而强度模型的“危险”部分捕获了受试者内部的间隙时间变化。估计器使用半参数效率理论进行激励,并导致了Strawderman(2005)中考虑的秩统计的有用推广。与Cox-Andersen-Gill脆弱性模型相比,出现了几个有趣的区别(例如,Nielsen et al, 1992;克莱恩,1992)。通过仿真和数据分析说明了所提出的方法。
A natural choice of time scale for analyzing recurrent event data is the ``gap" (or soujourn) time between successive events. In many situations it is reasonable to assume correlation exists between the successive events experienced by a given subject. This paper looks at the problem of extending the accelerated failure time (AFT) model to the case of dependent recurrent event data via intensity modeling. Specifically, the accelerated gap times model of Strawderman (2005), a semiparametric intensity model for independent gap time data, is extended to the case of multiplicative gamma frailty. As argued in Aalen & Husebye (1991), incorporating frailty captures the heterogeneity between subjects and the ``hazard" portion of the intensity model captures gap time variation within a subject. Estimators are motivated using semiparametric efficiency theory and lead to useful generalizations of the rank statistics considered in Strawderman (2005). Several interesting distinctions arise in comparison to the Cox-Andersen-Gill frailty model (e.g., Nielsen et al, 1992; Klein, 1992). The proposed methodology is illustrated by simulation and data analysis.
期刊介绍:
The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.