Practical recommendations for reporting Fine-Gray model analyses for competing risk data.
Practical recommendations for reporting Fine-Gray model analyses for competing risk data.
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DOI：
10.1002/sim.7501
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发表时间：
2017-11-30
影响因子：
2
通讯作者：
Fine JP
中科院分区：
文献类型：
--
作者：
Austin PC;Fine JP
在生存分析中，竞争风险是指其发生排除了主要关注事件发生的事件。医学研究的结果经常受到相互竞争的风险的影响。在生存分析中，有两个关键问题可以使用竞争风险回归模型来解决：第一，哪些协变量影响事件发生的速率，第二，哪些协变量影响事件随时间发生的概率。原因特异性风险模型估计了协变量对目前无事件受试者中事件发生率的影响。从Fine‐Gray模型获得的子分布风险比描述了协变量对子分布风险函数的相对影响。因此，该模型中的协变量也可以解释为对累积发生率函数或随时间推移发生的事件的概率具有影响。我们对2015年医学文献中发表的文章中细灰色子分布风险模型的使用和解释进行了综述。我们发现，许多作者提供了一个不清楚或不正确的解释与此模型相关的回归系数。当比较不同研究的结果时，对回归系数的不正确和不一致的解释可能会导致混淆。此外，对估计回归系数的不正确解释可能导致对暴露与结局发生率之间关联程度的不正确理解。本文的目的是澄清这些回归系数应如何报告，并提出解释这些系数的建议。
In survival analysis, a competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. Outcomes in medical research are frequently subject to competing risks. In survival analysis, there are 2 key questions that can be addressed using competing risk regression models: first, which covariates affect the rate at which events occur, and second, which covariates affect the probability of an event occurring over time. The cause‐specific hazard model estimates the effect of covariates on the rate at which events occur in subjects who are currently event‐free. Subdistribution hazard ratios obtained from the Fine‐Gray model describe the relative effect of covariates on the subdistribution hazard function. Hence, the covariates in this model can also be interpreted as having an effect on the cumulative incidence function or on the probability of events occurring over time. We conducted a review of the use and interpretation of the Fine‐Gray subdistribution hazard model in articles published in the medical literature in 2015. We found that many authors provided an unclear or incorrect interpretation of the regression coefficients associated with this model. An incorrect and inconsistent interpretation of regression coefficients may lead to confusion when comparing results across different studies. Furthermore, an incorrect interpretation of estimated regression coefficients can result in an incorrect understanding about the magnitude of the association between exposure and the incidence of the outcome. The objective of this article is to clarify how these regression coefficients should be reported and to propose suggestions for interpreting these coefficients.
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影响因子：
2
作者：
Austin, Peter C.;Lee, Douglas S.;D'Agostino, Ralph B.;Fine, Jason P.
通讯作者：
Fine, Jason P.
影响因子：
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影响因子：
2
作者：
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通讯作者：
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影响因子：
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通讯作者：
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