Quantifying discrimination of Framingham risk functions with different survival C statistics.
Quantifying discrimination of Framingham risk functions with different survival C statistics.
复制标题
DOI：
10.1002/sim.4508
复制
发表时间：
2012-07-10
影响因子：
2
通讯作者：
Song, Linye
中科院分区：
文献类型：
--
作者：
Pencina, Michael J.;D'Agostino, Ralph B., Sr.;Song, Linye
心血管风险预测功能为临床医生和患者本身提供了重要的诊断工具。它们通常使用参数或半参数生存回归模型构建。能够评估这些模型的性能至关重要，最好是提供自然和直观解释的摘要。歧视的概念，流行于逻辑回归的背景下，已扩展到生存分析。然而，扩展并不是唯一的。在本文中，我们将生存分析中的歧视定义为模型在一定时间范围内将无事件生存期较长的患者与无事件生存期较短的患者分开的能力。这个定义与逻辑回归中使用的定义保持一致，因为它评估了基于模型的预测与观测数据的匹配程度。实际和概念的例子和数值模拟研究文献中提出的四个C统计量来评估生存模型的性能。我们注意到，它们所反映的歧视的数值和方面各不相同。我们的结论是，由Harrell提出的指数是最合适的捕捉上述定义所描述的歧视。我们建议研究人员报告他们正在使用哪些C统计量，提供其选择的理由，并注意比较不同研究中的不同指数可能没有意义。
Cardiovascular risk prediction functions offer an important diagnostic tool for clinicians and patients themselves. They are usually constructed with the use of parametric or semi-parametric survival regression models. It is essential to be able to evaluate the performance of these models, preferably with summaries that offer natural and intuitive interpretations. The concept of discrimination, popular in the logistic regression context, has been extended to survival analysis. However, the extension is not unique. In this paper, we define discrimination in survival analysis as the model’s ability to separate those with longer event-free survival from those with shorter event-free survival within some time horizon of interest. This definition remains consistent with that used in logistic regression, in the sense that it assesses how well the model-based predictions match the observed data. Practical and conceptual examples and numerical simulations are employed to examine four C statistics proposed in the literature to evaluate the performance of survival models. We observe that they differ in the numerical values and aspects of discrimination that they capture. We conclude that the index proposed by Harrell is the most appropriate to capture discrimination described by the above definition. We suggest researchers report which C statistic they are using, provide a rationale for their selection, and be aware that comparing different indices across studies may not be meaningful.
登录
查看更多内容
影响因子：
1.9
作者：
Huang, Ying;Pepe, Margaret Sullivan;Feng, Ziding
通讯作者：
Feng, Ziding
影响因子：
37.8
作者：
Wilson, PWF;D'Agostino, RB;Kannel, WB
通讯作者：
Kannel, WB
影响因子：
2
作者：
Pencina, MJ;D'Agostino, RB
通讯作者：
D'Agostino, RB
影响因子：
2
作者：
KORN, EL;SIMON, R
通讯作者：
SIMON, R
影响因子：
2
作者：
Uno, Hajime;Cai, Tianxi;Pencina, Michael J.;D'Agostino, Ralph B.;Wei, L. J.
通讯作者：
Wei, L. J.