Bias by censoring for competing events in survival analysis
Bias by censoring for competing events in survival analysis
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DOI：
10.1136/bmj-2022-071349
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发表时间：
2022-09-13
影响因子：
105.7
通讯作者：
Naesens, Maarten
中科院分区：
文献类型：
--
作者：
Coemans, Maarten;Verbeke, Geert;Naesens, Maarten
在生存分析中，竞争事件会排除感兴趣事件的发生。对竞争事件的审查在医学研究中很常见，但会导致累积发生率估计量出现偏差。竞争风险方法，例如非参数 Aalen-Johansen 方法或半参数 Fine 和 Gray 模型，可以减轻这种偏差，并且应分别优于 Kaplan-Meier 方法和 Cox 模型。作为一个说明性的例子，在一个大型欧洲队列中，我们报告了肾移植后移植失败累积发生率估计的差异，这是由于对受者死亡的审查造成的。
In survival analysis, competing events preclude the occurrence of the event of interest. The censoring of competing events is common in medical studies but leads to biased cumulative incidence estimators. Competing risks methods, such as the non-parametric Aalen-Johansen method or the semi -parametric Fine and Gray model, alleviate this bias and should be preferred above the Kaplan-Meier method and the Cox model, respectively. As an illustrative example, in a large European cohort, we report on the differences in the cumulative incidence estimates of graft failure after kidney transplantation, caused by censoring for recipient death.