Survival model predictive accuracy and ROC curves
Survival model predictive accuracy and ROC curves
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DOI：
10.1111/j.0006-341x.2005.030814.x
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发表时间：
2005-03-01
期刊：
影响因子：
1.9
通讯作者：
Zheng, YY
中科院分区：
文献类型：
--
作者：
Heagerty, PJ;Zheng, YY
生存模型的预测准确性可以使用模型解释的变异比例的扩展或 R 2 来概括：通常用于连续响应模型，或者使用灵敏度和特异性的扩展（通常用于二元响应模型）。在本文中，我们基于针对风险集计算的敏感性和特异性的特定时间版本，提出了新的与时间相关的准确性摘要。我们将准确性摘要与之前提出的全局一致性度量联系起来，该度量是 Kendall tau 的变体。此外，我们还展示了如何使用标准 Cox 回归输出来获得时间依赖性敏感性和特异性以及时间依赖性受试者工作特征 (ROC) 曲线的估计。介绍了适用于比例和非比例危险数据的半参数估计方法，在模拟中进行评估，并使用两个熟悉的生存数据集进行说明。
The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R 2: commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time-dependent accuracy summaries based on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to a previously proposed global concordance measure, which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time-dependent receiver operating characteristic (ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.