Nonparametric Association Analysis of Exchangeable Clustered Competing Risks Data
Nonparametric Association Analysis of Exchangeable Clustered Competing Risks Data
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DOI：
10.1111/j.1541-0420.2008.01072.x
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发表时间：
2009-06-01
期刊：
影响因子：
1.9
通讯作者：
Kosorok, Michael R.
中科院分区：
文献类型：
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作者：
Cheng, Yu;Fine, Jason P.;Kosorok, Michael R.
这项工作的动机是卡什县老龄化研究，犹他州的一项以人口为基础的研究，其中兄弟姐妹与痴呆症发病的关系很有趣。并发症的出现是因为只有一小部分人会患上痴呆症，而大多数人死于无痴呆症。对独立权利审查数据应用标准依赖分析可能不适用于这种多变量竞争风险数据，其中死亡可能违反独立审查假设。在Cache County研究中，根据需要，二元累积风险函数和二元累积关联函数的非参数估计从简单的不可交换的二元设置调整为可交换的聚类数据。使用这些估计器评估依赖于时间的关联度量。使用经验处理技术严格研究大样本推论。该方法的实际效用是通过模拟和应用于卡什县研究的实际样本来证明的，在该研究中，兄弟姐妹之间的痴呆症发病集群因年龄而有很大差异。
The work is motivated by the Cache County Study of Aging, a population-based study in Utah, in which sibship associations in dementia onset are of interest. Complications arise because only a fraction of the population ever develops dementia, with the majority dying without dementia. The application of standard dependence analyses for independently right-censored data may not be appropriate with such multivariate competing risks data, where death may violate the independent censoring assumption. Nonparametric estimators of the bivariate cumulative hazard function and the bivariate cumulative incidence function are adapted from the simple nonexchangeable bivariate setup to exchangeable clustered data, as needed with the large sibships in the Cache County Study. Time-dependent association measures are evaluated using these estimators. Large sample inferences are studied rigorously using empirical process techniques. The practical utility of the methodology is demonstrated with realistic samples both via simulations and via an application to the Cache County Study, where dementia onset clustering among siblings varies strongly by age.