Simulating competing risks data in survival analysis
Simulating competing risks data in survival analysis
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DOI：
10.1002/sim.3516
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发表时间：
2009-03-15
影响因子：
2
通讯作者：
Schumacher, Martin
中科院分区：
文献类型：
--
作者：
Beyersmann, Jan;Latouche, Aurelien;Schumacher, Martin
竞争风险分析考虑了第一个事件的发生时间(“生存时间”)和事件类型(“原因”)，可能需要进行正确的审查。原因--即特定于事件的危险--完全决定了竞争风险过程，但模拟研究往往依赖于备受批评的潜在失效时间模型。特定原因的危险驱动模拟似乎是个例外；如果这样做了，通常只考虑持续的危险，这在许多医疗情况下是不现实的。我们解释了基于可能与时间相关的特定原因的危险来模拟竞争风险数据。模拟设计与其他设计一样简单，只依赖于可识别的数量，并增加了我们对竞争风险流程的理解。此外，它还可以立即推广到更复杂的多态模型。我们将所提出的模拟设计应用于错误指定的比例次分布风险模型的最小错误参数的计算，这是一个独立于竞争风险的研究问题。模拟规范的动机是关于干细胞移植患者感染并发症的数据，在这些数据中，原因特定风险分析的结果很难用累积事件概率来解释。模拟表明，错误指定比例次分布风险分析的结果可以解释为累积事件概率尺度上的时间平均效应。版权所有(C)2009 John Wiley&Sons，Ltd.
Competing risks analysis considers time-to-first-event ('survival time') and the event type ('cause'), possibly subject to right-censoring. The cause-, i.e. event-specific hazards, completely determine the competing risk process, but simulation studies often fall back on the much criticized latent failure time model. Cause-specific hazard-driven simulation appears to be the exception; if done, usually only constant hazards are considered, which will be unrealistic in many medical situations. We explain simulating competing risks data based on possibly time-dependent cause-specific hazards. The simulation design is as easy as any other, relies on identifiable quantities only and adds to our understanding of the competing risks process. In addition, it immediately generalizes to more complex multistate models. We apply the proposed simulation design to computing the least false parameter of a misspecified proportional subdistribution hazard model, which is a research question of independent interest in competing risks. The simulation specifications have been motivated by data on infectious complications in stem-cell transplanted patients, where results from cause-specific hazards analyses were difficult to interpret in terms of cumulative event probabilities. The simulation illustrates that results from a misspecified proportional subdistribution hazard analysis can be interpreted as a tune-averaged effect on the cumulative event probability scale. Copyright (C) 2009 John Wiley & Sons, Ltd.