The use and interpretation of competing risks regression models.
The use and interpretation of competing risks regression models.
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DOI：
10.1158/1078-0432.ccr-11-2097
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发表时间：
2012-04-15
期刊：
影响因子：
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通讯作者：
Kocherginsky M
中科院分区：
文献类型：
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作者：
Dignam JJ;Zhang Q;Kocherginsky M
竞争性风险观察，即患者遭受许多潜在的失败事件，是大多数临床癌症研究的一个特征。对于竞争风险，有几种建模方法可用于评估协变量与特定原因失效的关系。我们讨论了常用的竞争风险回归模型的使用和解释。对于竞争风险分析，可以评价协变量对特定原因危害或失效类型累积发生率的影响。我们目前的模拟研究，以说明这些方法之间的协变量效应的不同。然后，我们展示了模型选择的影响，在一个例子中，从放射治疗肿瘤组（RTOG）的前列腺癌临床试验。模拟研究表明，根据协变量与本金利息失败类型和竞争失败类型之间的关系，不同的模型可能会导致实质上不同的影响。例如，如果协变量影响竞争事件的风险，则对主要事件的风险没有直接影响的协变量仍然可以与该事件的累积概率显著相关。这是模型公式之间的根本差异的逻辑结果。来自RTOG的示例同样显示了年龄和肿瘤分级影响的差异，这取决于终点和所使用的模型类型。竞争风险回归建模需要考虑感兴趣的特定问题，并随后选择最佳模型来解决它。
Competing risks observations, where patients are subject to a number of potential failure events, are a feature of most clinical cancer studies. With competing risks, several modeling approaches are available to evaluate the relationship of covariates to cause-specific failures. We discuss the use and interpretation of commonly used competing risks regression models. For competing risks analysis, the influence of covariate can be evaluated in relation to cause-specific hazard or on the cumulative incidence of the failure types. We present simulation studies to illustrate how covariate effects differ between these approaches. We then show the implications of model choice in an example from a Radiation Therapy Oncology Group (RTOG) clinical trial for prostate cancer. The simulation studies illustrate that, depending on the relationship of a covariate to both the failure type of principal interest and the competing failure type, different models can result in substantially different effects. For example, a covariate that has no direct influence on the hazard of a primary event can still be significantly associated with the cumulative probability of that event, if the covariate influences the hazard of a competing event. This is a logical consequence of a fundamental difference between the model formulations. The example from RTOG similarly shows differences in the influence of age and tumor grade depending on the endpoint and the model type used. Competing risks regression modeling requires that one consider the specific question of interest and subsequent choice of the best model to address it.