Flexible parametric modelling of cause-specific hazards to estimate cumulative incidence functions.
Flexible parametric modelling of cause-specific hazards to estimate cumulative incidence functions.
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DOI：
10.1186/1471-2288-13-13
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发表时间：
2013-02-06
影响因子：
4
通讯作者：
Lambert PC
中科院分区：
文献类型：
--
作者：
Hinchliffe SR;Lambert PC
竞争风险是生存分析中常见的情况。当患者面临不止一种相互排斥的事件（例如不同原因导致的死亡）的风险时，就会出现这种情况，其中一种事件的发生可能会阻止任何其他事件的发生。有两种主要的方法来模拟竞争风险：第一种是模拟特定原因的危害，并将其转换为累积发生率函数;第二种是直接在累积发生率函数的转换上建模。本文重点介绍第一种方法。本文主张在这个竞争风险框架中使用灵活的参数生存模型。乳腺癌患者生存的一个说明性例子表明，灵活的参数比例风险模型与考克斯比例风险模型几乎完全一致。然而，这里使用的大型流行病学数据集显示了非比例危害的明确证据。灵活的参数模型是能够充分考虑到这些通过纳入随时间变化的影响。使用这种方法的一个主要优点是，可以获得原因特异性风险率和累积发病率函数的平滑估计。它也相对容易纳入流行病学研究中常见的时间依赖性效应。
Competing risks are a common occurrence in survival analysis. They arise when a patient is at risk of more than one mutually exclusive event, such as death from different causes, and the occurrence of one of these may prevent any other event from ever happening. There are two main approaches to modelling competing risks: the first is to model the cause-specific hazards and transform these to the cumulative incidence function; the second is to model directly on a transformation of the cumulative incidence function. We focus on the first approach in this paper. This paper advocates the use of the flexible parametric survival model in this competing risk framework. An illustrative example on the survival of breast cancer patients has shown that the flexible parametric proportional hazards model has almost perfect agreement with the Cox proportional hazards model. However, the large epidemiological data set used here shows clear evidence of non-proportional hazards. The flexible parametric model is able to adequately account for these through the incorporation of time-dependent effects. A key advantage of using this approach is that smooth estimates of both the cause-specific hazard rates and the cumulative incidence functions can be obtained. It is also relatively easy to incorporate time-dependent effects which are commonly seen in epidemiological studies.
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