Rosthøj Susanne, Andersen Per K, Abildstrom Steen Z
Department of Biostatistics, University of Copenhagen, Blegdamsvej 3, DK 2200 Copenhagen N, Denmark.
Comput Methods Programs Biomed. 2004 Apr;74(1):69-75. doi: 10.1016/S0169-2607(03)00069-5.
When considering competing risks survival data, the cause specific hazard functions are often modelled by the proportional hazards Cox regression model. First, we present how to estimate the parameters in this model when some of the covariates are allowed to have exactly the same effect on several causes of failure. In many cases, the focus is not on the parameter estimates, but rather on the probability of observing a failure from a specific cause for individuals with specified covariate values. These probabilities, the cumulative incidences, are not simple functions of the parameters and they are, so far, not provided by the standard statistical software packages. We present two SAS macros: a SAS macro named CumInc for estimation of the cumulative incidences and a SAS macro named CumIncV for estimation of the cumulative incidences and the variances of the estimated cumulative incidences. The use of the macros is demonstrated through an example.
在考虑竞争风险生存数据时，特定病因风险函数通常采用比例风险Cox回归模型进行建模。首先，我们介绍当某些协变量对多种失败原因具有完全相同的影响时，如何估计该模型中的参数。在许多情况下，重点不在于参数估计，而是具有特定协变量值的个体因特定原因出现失败的概率。这些概率，即累积发病率，并非参数的简单函数，并且到目前为止，标准统计软件包并未提供这些概率。我们给出两个SAS宏：一个名为CumInc的SAS宏用于估计累积发病率，另一个名为CumIncV的SAS宏用于估计累积发病率以及估计的累积发病率的方差。通过一个示例展示了这些宏的用法。
