Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
Stat Med. 2013 Aug 15;32(18):3077-88. doi: 10.1002/sim.5762. Epub 2013 Feb 24.
In the analysis of time-to-event data, the problem of competing risks occurs when an individual may experience one, and only one, of m different types of events. The presence of competing risks complicates the analysis of time-to-event data, and standard survival analysis techniques such as Kaplan-Meier estimation, log-rank test and Cox modeling are not always appropriate and should be applied with caution. Fine and Gray developed a method for regression analysis that models the hazard that corresponds to the cumulative incidence function. This model is becoming widely used by clinical researchers and is now available in all the major software environments. Although model selection methods for Cox proportional hazards models have been developed, few methods exist for competing risks data. We have developed stepwise regression procedures, both forward and backward, based on AIC, BIC, and BICcr (a newly proposed criteria that is a modified BIC for competing risks data subject to right censoring) as selection criteria for the Fine and Gray model. We evaluated the performance of these model selection procedures in a large simulation study and found them to perform well. We also applied our procedures to assess the importance of bone mineral density in predicting the absolute risk of hip fracture in the Women's Health Initiative-Observational Study, where mortality was the competing risk. We have implemented our method as a freely available R package called crrstep.
在处理时变数据的分析中，如果一个个体可能经历 m 种不同类型的事件中的一种且仅有一种，就会出现竞争风险的问题。竞争风险的存在使处理时变数据的分析变得复杂，标准的生存分析技术，如 Kaplan-Meier 估计、对数秩检验和 Cox 建模并不总是适用，并且应该谨慎应用。Fine 和 Gray 开发了一种回归分析方法，该方法对对应于累积发生率函数的风险进行建模。该模型正被临床研究人员广泛使用，现在已经在所有主要的软件环境中都可用。虽然已经为 Cox 比例风险模型开发了模型选择方法，但针对竞争风险数据的方法却很少。我们基于 AIC、BIC 和 BICcr（一种新提出的适用于右删失的竞争风险数据的修正 BIC）标准，为 Fine 和 Gray 模型开发了向前和向后的逐步回归程序作为选择标准。我们在一项大型模拟研究中评估了这些模型选择程序的性能，发现它们表现良好。我们还应用我们的程序来评估骨密度在预测妇女健康倡议观察研究中髋部骨折绝对风险中的重要性，其中死亡率是竞争风险。我们已经将我们的方法实现为一个免费的 R 包，称为 crrstep。
