Predictive accuracy and explained variation in Cox regression
Predictive accuracy and explained variation in Cox regression
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DOI：
10.1111/j.0006-341x.2000.00249.x
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发表时间：
2000-03-01
期刊：
影响因子：
1.9
通讯作者：
Schemper, M
中科院分区：
文献类型：
--
作者：
Schemper, M
我们提出了一个新的措施的比例可能删失的生存时间的变化解释一个给定的比例风险模型。所提出的措施，称为V，共享几个有利的性能与早期的V-1，但也提高了审查的处理。该统计量对比了个体1/0生存过程与有和无协变量信息的拟合生存曲线之间的距离度量。这些距离测量值，D-x和D，分别作为绝对而不是相对预测准确性的总结本身是信息性的。我们建议对预后指数组的生存曲线进行图形比较，以提高对V、D-x和D值的理解。它们的使用和解释是约克郡肺癌生存研究的例证。从这一点以及对几个众所周知的临床数据集的概述中，我们表明，即使存在高度显着且相对较强的预后因素，相对或绝对预测准确性的可能程度往往较低。
We suggest a new measure of the proportion of the variation of possibly censored survival times explained by a given proportional hazards model. The proposed measure, termed V, shares several favorable properties with an earlier V-1 but also improves the handling of censoring. The statistic contrasts distance measures between individual 1/0 survival processes and fitted survival curves with and without covariate information. These distance measures, D-x and D, respectively, are themselves informative as summaries of absolute rather than relative predictive accuracy. We recommend graphical comparisons of survival curves for prognostic index groups to improve the understanding of obtained values for V, D-x, and D. Their use and interpretation is exemplified for a Yorkshire lung cancer study on survival. From this and an overview for several well-known clinical data sets, we show that the likely amount of relative or absolute predictive accuracy is often low even if there are highly significant and relatively strong prognostic factors.