Dependence modeling for recurrent event times subject to right-censoring with D-vine copulas
Dependence modeling for recurrent event times subject to right-censoring with D-vine copulas
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DOI：
10.1111/biom.13014
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发表时间：
2019-06-01
期刊：
影响因子：
1.9
通讯作者：
Janssen, Paul
中科院分区：
文献类型：
--
作者：
Barthel, Nicole;Geerdens, Candida;Janssen, Paul
在许多时间对事件的研究中，感兴趣的事件是反复出现的。这里，每个样本单元的数据对应于后续事件之间的一系列间隔时间。鉴于后续时间有限，最后的间隔时间可能会受到严格审查。与经典分析相反，不能假设间隙时间和审查时间是独立的，即，数据的顺序性质导致依赖审查。此外，递归的次数通常在样本单位之间变化，导致数据不平衡。为了对间隙时间之间的关联模式进行建模，迄今为止只考虑了参数边界与阿基米德copulas的限制类相结合。在这里，考虑到特定的数据特征，我们在几个方向上扩展了现有的工作：我们允许非参数边界并考虑灵活的D-vine copula类。提出了一种全局和顺序（一阶段和两阶段）似然方法。讨论了每种估计策略的计算效率。大量的仿真结果表明，该方法具有良好的有限样本性能。用于分析儿童哮喘复发的相关性。分析表明，d -藤的联结体可以检测到相关的见解，即依赖性的强度和类型如何随着时间的推移而变化。
In many time-to-event studies, the event of interest is recurrent. Here, the data for each sample unit correspond to a series of gap times between the subsequent events. Given a limited follow-up period, the last gap time might be right-censored. In contrast to classical analysis, gap times and censoring times cannot be assumed independent, i.e., the sequential nature of the data induces dependent censoring. Also, the number of recurrences typically varies among sample units leading to unbalanced data. To model the association pattern between gap times, so far only parametric margins combined with the restrictive class of Archimedean copulas have been considered. Here, taking the specific data features into account, we extend existing work in several directions: we allow for nonparametric margins and consider the flexible class of D-vine copulas. A global and sequential (one- and two-stage) likelihood approach are suggested. We discuss the computational efficiency of each estimation strategy. Extensive simulations show good finite sample performance of the proposed methodology. It is used to analyze the association of recurrent asthma attacks in children. The analysis reveals that a D-vine copula detects relevant insights, on how dependence changes in strength and type over time.