Lusa Lara, Miceli Rosalba, Mariani Luigi
Department of Experimental Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy.
Comput Methods Programs Biomed. 2007 Aug;87(2):132-7. doi: 10.1016/j.cmpb.2007.05.009. Epub 2007 Jun 29.
When the purpose of a survival regression model is to predict future outcomes, the predictive accuracy of the model needs to be evaluated before practical application. Various measures of predictive accuracy have been proposed for survival data, none of which has been adopted as a standard, and their inclusion in statistical software is disregarded. We developed the surev library for R and S-PLUS, which includes functions for evaluating the predictive accuracy measures proposed by Schemper and Henderson. The library evaluates the predictive accuracy of parametric regression models and of Cox models. The predictive accuracy of the Cox model can be obtained also when time-dependent covariates are included because of non-proportional hazards or when using Bayesian model averaging. The use of the library is illustrated with examples based on a real data set.
当生存回归模型的目的是预测未来结果时，在实际应用之前需要评估模型的预测准确性。针对生存数据已经提出了各种预测准确性的度量方法，但没有一种被采纳为标准，并且统计软件中也未包含这些方法。我们为R和S-PLUS开发了surev库，其中包括用于评估Schemper和Henderson提出的预测准确性度量方法的函数。该库可评估参数回归模型和Cox模型的预测准确性。当由于非比例风险而纳入时间依存协变量时，或者在使用贝叶斯模型平均时，也可以获得Cox模型的预测准确性。通过基于真实数据集的示例说明了该库的使用方法。
