Proportional subdistribution hazards modeling offers a summary analysis, even if misspecified
Proportional subdistribution hazards modeling offers a summary analysis, even if misspecified
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DOI：
10.1002/sim.3786
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发表时间：
2010-03-01
影响因子：
2
通讯作者：
Beyersmann, Jan
中科院分区：
文献类型：
--
作者：
Grambauer, Nadine;Schumacher, Martin;Beyersmann, Jan
竞争风险模型的时间到第一个事件和事件类型。我们的激励性数据示例是ONKO-KISS研究，该研究涉及干细胞移植后血小板减少症患者感染的发生，首次事件类型为“感染”和“血小板减少症结束”。研究协变量在竞争风险中的影响的标准方法是假设每个事件特定风险（ESH）遵循比例风险模型。然而，一个协变量的不同事件特异性效应的总结概率解释可能具有挑战性。这一困难导致了利益竞争事件的比例子分布风险模型的发展。然而，一个模型规范通常排除了另一个。假设比例ESHS，我们发现，子分布的对数风险比可能会显示出明显的时间依赖性，甚至改变符号。尽管如此，子分布分析通过估计最小假参数（LFP）（对累积事件概率的时间平均效应）是有用的。在例子中，我们发现，LFP提供了一个强大的总结的影响ESH的不同的观察期，从重删失到没有删失。特别是，如果对竞争性ESH没有影响，则子分布对数风险比接近关注的事件特异性对数风险比。我们重新分析了一个解释上具有挑战性的例子，从ONKO-KISS研究和进行模拟研究，在那里我们发现，LFP是可靠的估计子分布分析，即使是中等样本量。版权所有（C）2010约翰威利父子有限公司
Competing risks model time-to-first-event and the event type. Our motivating data example is the ONKO-KISS study on the occurrence of infections in neutropenic patients after stem-cell transplantation with first-event-types 'infection' and 'end of neutropenia'. The standard approach to study the effects of covariates in competing risks is to assume each event-specific hazard (ESH) to follow a proportional hazards model. However, a summarizing probability interpretation of the different event-specific effects of one covariate can be challenging. This difficulty has led to the development of the proportional subdistribution hazards model of a competing event of interest. However, one model specification usually precludes the other. Assuming proportional ESHs, we find that the subdistribution log-hazard ratio may show a pronounced time-dependency, even changing sign. Still, the subdistribution analysis is useful by estimating the least false parameter (LFP), a time-averaged effect on the cumulative event probabilities. In examples, we find that the LFP offers a robust summary of the effects on the ESHs for different observation periods, ranging from heavy censoring to no censoring at all. In particular, if there is no effect on the competing ESH, the subdistribution log-hazard ratio is close to the event-specific log-hazard ratio of interest. We reanalyze an interpretationally challenging example from the ONKO-KISS study and conduct a simulation study, where we find that the LFP is reliably estimated by the subdistribution analysis even for moderate sample sizes. Copyright (C) 2010 John Wiley & Sons, Ltd.