Fine-Gray subdistribution hazard models to simultaneously estimate the absolute risk of different event types: Cumulative total failure probability may exceed 1.
Fine-Gray subdistribution hazard models to simultaneously estimate the absolute risk of different event types: Cumulative total failure probability may exceed 1.
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DOI：
10.1002/sim.9023
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发表时间：
2021-08-30
影响因子：
2
通讯作者：
Putter H
中科院分区：
文献类型：
--
作者：
Austin PC;Steyerberg EW;Putter H
细灰色子分布风险模型已成为在存在竞争风险的情况下估计随时间推移的结局发生率的默认方法。该模型很有吸引力，因为它直接将协变量与关注事件的累积发生率函数（CIF）联系起来。另一种方法是将不同的特定原因危险函数组合起来，以获得不同的CIF。子分布风险方法的局限性在于某些协变量模式的原因特异性CIF总和可能超过1（100%）。使用9479例急性心肌梗死住院患者的数据，我们估计了每例患者心血管死亡和非心血管死亡的累积发生率。我们发现，当使用子分布风险模型时，约5%的受试者5年全因死亡的估计风险（通过合并从子分布风险模型获得的两个原因特异性CIF获得）超过1。通过使用两个特定原因危害模型避免了这种现象。我们证明了预测之和超过1是细灰色子分布风险模型的一个基本问题。我们使用基于两种不同类型的数据生成过程的模拟进一步探索了这个问题，一种基于子分布风险模型，另一种基于特定原因风险模型。我们的结论是，在风险分布较宽或累积发生率较高的情况下，以及如果对每种不同事件类型的失效风险感兴趣，使用细灰色子分布风险模型时应谨慎。
The Fine‐Gray subdistribution hazard model has become the default method to estimate the incidence of outcomes over time in the presence of competing risks. This model is attractive because it directly relates covariates to the cumulative incidence function (CIF) of the event of interest. An alternative is to combine the different cause‐specific hazard functions to obtain the different CIFs. A limitation of the subdistribution hazard approach is that the sum of the cause‐specific CIFs can exceed 1 (100%) for some covariate patterns. Using data on 9479 patients hospitalized with acute myocardial infarction, we estimated the cumulative incidence of both cardiovascular death and non‐cardiovascular death for each patient. We found that when using subdistribution hazard models, approximately 5% of subjects had an estimated risk of 5‐year all‐cause death (obtained by combining the two cause‐specific CIFs obtained from subdistribution hazard models) that exceeded 1. This phenomenon was avoided by using the two cause‐specific hazard models. We provide a proof that the sum of predictions exceeds 1 is a fundamental problem with the Fine‐Gray subdistribution hazard model. We further explored this issue using simulations based on two different types of data‐generating process, one based on subdistribution hazard models and other based on cause‐specific hazard models. We conclude that care should be taken when using the Fine‐Gray subdistribution hazard model in situations with wide risk distributions or a high cumulative incidence, and if one is interested in the risk of failure from each of the different event types.
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影响因子：
1.9
作者：
Klein, JP;Andersen, PK
通讯作者：
Andersen, PK
影响因子：
2
作者：
Beyersmann, Jan;Dettenkofer, Markus;Schumacher, Martin
通讯作者：
Schumacher, Martin
影响因子：
2
作者：
Austin PC;Fine JP
通讯作者：
Fine JP
影响因子：
2
作者：
Austin, Peter C.
通讯作者：
Austin, Peter C.
影响因子：
2
作者：
Putter, H.;Fiocco, M.;Geskus, R. B.
通讯作者：
Geskus, R. B.