Estimating survival and association in a semicompeting risks model
Estimating survival and association in a semicompeting risks model
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DOI：
10.1111/j.1541-0420.2007.00872.x
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发表时间：
2008-03-01
期刊：
影响因子：
1.9
通讯作者：
Abdous, Belkacem
中科院分区：
文献类型：
--
作者：
Lakhal, Lajmi;Rivest, Louis-Paul;Abdous, Belkacem
在许多随访研究中，患者易发生并发事件。在本文中，我们考虑由Fine， Jiang和Chappell （2001, Biometrika 88, 907-919）定义的半竞争风险数据，其中一个事件被另一个事件审查，反之亦然。所提出的模型包括两个事件的边际生存函数和它们的依赖关系的参数族。本文提出了用阿基米德copula建模时估计依赖参数的一般方法。它使用Zheng和Klein （1995, Biometrika 82, 127-138）的copula-graphic estimator来估计非终止事件的生存函数，并进行依赖审查。给出了这些估计量的渐近性质。仿真结果表明，在有限样本情况下，新方法能很好地解决问题。与Fine et al.（2001）提出的估计器相比，copula-graphic估计器被证明更准确；其性能与Jiang， Fine, Kosorok, and Chappell （2005, Scandinavian Journal of Statistics, 33, 1-20）的自洽估计量相似。一个数据集的分析，强调可观测区域的特征估计，作为一个说明。
In many follow-up studies, patients are subject to concurrent events. In this article, we consider semicompeting risks data as defined by Fine, Jiang, and Chappell (2001, Biometrika 88, 907-919) where one event is censored by the other but not vice versa. The proposed model involves marginal survival functions for the two events and a parametric family of copulas for their dependency. This article suggests a general method for estimating the dependence parameter when the dependency is modeled with an Archimedean copula. It uses the copula-graphic estimator of Zheng and Klein (1995, Biometrika 82, 127-138) for estimating the survival function of the nonterminal event, subject to dependent censoring. Asymptotic properties of these estimators are derived. Simulations show that the new methods work well with finite samples. The copula-graphic estimator is shown to be more accurate than the estimator proposed by Fine et al. (2001); its performances are similar to those of the self-consistent estimator of Jiang, Fine, Kosorok, and Chappell (2005, Scandinavian Journal of Statistics 33, 1-20). The analysis of a data set, emphasizing the estimation of characteristics of the observable region, is presented as an illustration.