Time-dependent covariates in the proportional subdistribution hazards model for competing risks
Time-dependent covariates in the proportional subdistribution hazards model for competing risks
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DOI：
10.1093/biostatistics/kxn009
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发表时间：
2008-10-01
期刊：
影响因子：
2.1
通讯作者：
Schumacher, Martin
中科院分区：
文献类型：
--
作者：
Beyersmann, Jan;Schumacher, Martin
对所有特定原因的危险进行单独的COX分析是研究竞争风险中协变量的影响的标准方法，但根据累积事件概率来概括这些结果是具有挑战性的。这一困难导致了比例次分布风险模型的发展。如果在基线时已知协变量，则该模型允许根据累积关联函数进行汇总评估。在数学上，该模型还允许包含随机的随时间变化的协变量，但由于某种风险集的特殊性，实际实施仍不清楚。我们使用离散协变量和多态模型的密切关系来自然地处理次分布风险框架内的时间依赖协变量。然后，该方法直接转化为实值的时间依赖协变量。与经典的生存分析一样，包括时间相关的协变量不再导致概率函数的模型。然而，拟议的方法提供了一个有用的综合单独的具体原因的危险分析。我们用医院感染数据来说明这一点，在这些数据中，依赖时间的协变量和竞争风险对于主题研究问题是必不可少的。
Separate Cox analyses of all cause-specific hazards are the standard technique of choice to study the effect of a covariate in competing risks, but a synopsis of these results in terms of cumulative event probabilities is challenging. This difficulty has led to the development of the proportional subdistribution hazards model. If the covariate is known at baseline, the model allows for a summarizing assessment in terms of the cumulative incidence function. black Mathematically, the model also allows for including random time-dependent covariates, but practical implementation has remained unclear due to a certain risk set peculiarity. We use the intimate relationship of discrete covariates and multistate models to naturally treat time-dependent covariates within the subdistribution hazards framework. The methodology then straightforwardly translates to real-valued time-dependent covariates. As with classical survival analysis, including time-dependent covariates does not result in a model for probability functions anymore. Nevertheless, the proposed methodology provides a useful synthesis of separate cause-specific hazards analyses. We illustrate this with hospital infection data, where time-dependent covariates and competing risks are essential to the subject research question.