PSHREG: a SAS macro for proportional and nonproportional subdistribution hazards regression.
PSHREG: a SAS macro for proportional and nonproportional subdistribution hazards regression.
复制标题
DOI：
10.1016/j.cmpb.2014.11.009
复制
发表时间：
2015-02
影响因子：
6.1
通讯作者：
Heinze G
中科院分区：
文献类型：
--
作者：
Kohl M;Plischke M;Leffondré K;Heinze G
%pshreg SAS 宏适合竞争风险的精细灰色模型。该宏首先修改给定的数据集，然后使用 PROC PHREG 进行分析。 PROC PHREG 的许多有用功能现在可以应用于 Fine-Gray 模型。时间依赖性效应可以通过时间与协变量的相互作用来调节。对于小数据集，可以使用 Firth 校正。我们提出了一个新的 SAS 宏 %pshreg，可用于拟合受竞争风险影响的生存数据的比例次分布风险模型。我们的宏首先适当地修改输入数据集，然后应用 SAS 的标准 Cox 回归程序 PROC PHREG，使用权重和指定修改数据集生存时间的计数过程样式。修改后的数据集还可用于估计感兴趣事件的累积发生率曲线。 PROC PHREG 的应用有几个优点，例如，它可以直接使用户能够应用 Firth 校正，该校正已被提议作为 Cox 回归中未定义（无限）最大似然估计问题的解决方案，该问题在小样本分析中经常遇到。通过检查舍恩菲尔德型残差并测试这些残差与时间的相关性，或者通过包含协变量与时间函数的相互作用，可以检测比例次分布风险的偏差。我们通过对真实慢性肾脏疾病研究的分析，说明了使用我们的宏来应用这些扩展方法进行竞争风险回归，该宏可免费获得：http://cemsiis.meduniwien.ac.at/en/kb/science-research/software/statistical-software/pshreg。我们讨论 %pshreg 和最近（2014 年 1 月）比例次分布风险建模的 SAS PROC PHREG 实现的特性和功能之间的差异。
The %pshreg SAS macro fits Fine-Gray models for competing risks. The macro first modifies a given data set and then uses PROC PHREG for analysis. Many useful features of PROC PHREG can now be applied to a Fine-Gray model. Time-dependent effects can be accommodated by time-by-covariate interactions. For small data sets, the Firth correction is available. We present a new SAS macro %pshreg that can be used to fit a proportional subdistribution hazards model for survival data subject to competing risks. Our macro first modifies the input data set appropriately and then applies SAS's standard Cox regression procedure, PROC PHREG, using weights and counting-process style of specifying survival times to the modified data set. The modified data set can also be used to estimate cumulative incidence curves for the event of interest. The application of PROC PHREG has several advantages, e.g., it directly enables the user to apply the Firth correction, which has been proposed as a solution to the problem of undefined (infinite) maximum likelihood estimates in Cox regression, frequently encountered in small sample analyses. Deviation from proportional subdistribution hazards can be detected by both inspecting Schoenfeld-type residuals and testing correlation of these residuals with time, or by including interactions of covariates with functions of time. We illustrate application of these extended methods for competing risk regression using our macro, which is freely available at: http://cemsiis.meduniwien.ac.at/en/kb/science-research/software/statistical-software/pshreg, by means of analysis of a real chronic kidney disease study. We discuss differences in features and capabilities of %pshreg and the recent (January 2014) SAS PROC PHREG implementation of proportional subdistribution hazards modelling.