Actual and actuarial probabilities of competing risks: Apples and lemons
Actual and actuarial probabilities of competing risks: Apples and lemons
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DOI：
10.1016/j.athoracsur.2006.11.044
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发表时间：
2007-05-01
影响因子：
4.6
通讯作者：
Takkenberg, Johanna J. M.
中科院分区：
文献类型：
--
作者：
Grunkemeier, Gary L.;Jin, Ruyun;Takkenberg, Johanna J. M.
某种类型的故障并非不可避免，但可以通过其他事件（例如死亡）来排除，其概率由累积发生率函数给出。在心脏研究文章中，它被称为实际概率，与通常通过 Kaplan-Meier (KM) 估计实现的精算估计方法相反。与累积发生率不同，知识管理试图预测如果死亡被消除的话潜在的失败概率是多少。为此，KM 方法假设死亡风险和失败风险是独立的。但这种假设对于许多心脏应用来说并不成立，其中衰竭风险和死亡呈负相关（即，死亡风险较高的患者失败风险较低，死亡风险较低的患者失败风险较高，这种情况称为信息审查）。最近两本心脏杂志的社论提倡将 KM 方法（精算估计）用于竞争风险事件（特别是心脏瓣膜性能），并批评累积发生率的使用（实际）估计。本报告有两个目的：解释这两种估计之间的差异，并说明为什么知识管理通常不合适。在此过程中，我们将依靠 KM 估计器的替代表示（使用向右重新分配和逆概率加权）来解释两个估计之间的差异，并展示如何调整 KM 来克服信息审查。
The probability of a type of failure that is not inevitable, but can be precluded by other events such as death, is given by the cumulative incidence function. In cardiac research articles, it has become known as the actual probability, in contrast to the actuarial methods of estimation, usually implemented by the Kaplan-Meier (KM) estimate. Unlike cumulative incidence, KM attempts to predict what the latent failure probability would be if death were eliminated. To do this, the KM method assumes that the risk of dying and the risk of failure are independent. But this assumption is not true for many cardiac applications in which the risks of failure and death are negatively correlated (ie, patients with a higher risk of dying have a lower risk of failure, and patients with a lower risk of death have a higher risk of failure, which is a condition called informative censoring).Recent editorials in two cardiac journals have promoted the use of the KM method (actuarial estimate) for competing risk events (specifically for heart valve performance) and criticized the use of the cumulative incidence (actual) estimates. This report has two aims: to explain the difference between these two estimates and to show why the KM is generally not appropriate. In the process we will rely on alternative representations of the KM estimator (using redistribution to the right and inverse probability weighting) to explain the difference between the two estimates and to show how it may be possible to adjust KM to overcome the informative censoring.