Analysing multicentre competing risks data with a mixed proportional hazards model for the subdistribution
Analysing multicentre competing risks data with a mixed proportional hazards model for the subdistribution
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DOI：
10.1002/sim.2684
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发表时间：
2006-12-30
影响因子：
2
通讯作者：
Porcher, Raphaeel
中科院分区：
文献类型：
--
作者：
Katsahian, Sandrine;Resche-Rigon, Matthieu;Porcher, Raphaeel
在竞争风险设置中，为了检验协变量对某一特定失效原因的概率的影响，可以使用子分布风险的Fine and Gray模型。然而，有时，由于聚类设计，例如在登记队列或多中心临床试验中，相互竞争的风险数据不能被视为独立的。在典型的生存环境中，只有一种风险作用于人群，脆弱模型已被证明对分析这种聚集数据是有用的。将随机效应纳入亚分布危害尚未得到评估。在这项工作中，我们提出了一个亚分布危害的脆弱性模型。这允许首先评估跨集群的异质性，然后在测试感兴趣的协变量的影响时合并这样的影响。在模拟研究的基础上，研究了异质性对协变量效应检验的影响。最后，该模型用一组接受骨髓移植的急性髓性白血病患者的注册队列数据进行了说明。版权所有(c) 2006约翰威利父子有限公司
In the competing-risks setting, to test the effect of a covariate on the probability of one particular cause of failure, the Fine and Gray model for the subdistribution hazard can be used. However, sometimes, competing risks data cannot be considered as independent because of a clustered design, for instance in registry cohorts or multicentre clinical trials. Frailty models have been shown useful to analyse such clustered data in a classical survival setting, where only one risk acts on the population. Inclusion of random effects in the subdistribution hazard has not been assessed yet.In this work, we propose a frailty model for the subdistribution hazard. This allows first to assess the heterogeneity across clusters, then to incorporate such an effect when testing the effect of a covariate of interest. Based on simulation study, the effect of the presence of heterogeneity on testing for covariate effects was studied. Finally, the model was illustrated on a data set from a registry cohort of patients with acute myeloid leukaemia who underwent bone marrow transplantation. Copyright (c) 2006 John Wiley & Sons, Ltd.