Interpreting Incremental Value of Markers Added to Risk Prediction Models
Interpreting Incremental Value of Markers Added to Risk Prediction Models
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DOI：
10.1093/aje/kws207
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发表时间：
2012-09-15
影响因子：
5
通讯作者：
Greenland, Philip
中科院分区：
文献类型：
--
作者：
Pencina, Michael J.;D'Agostino, Ralph B.;Greenland, Philip
风险预测模型的区分度测量了区分有事件和无事件的受试者的建模能力。受试者工作特征曲线下面积（AUC）是一种常用的辨别力测量方法。然而，AUC最近因其在基线模型表现良好的模型比较中的不敏感性而受到批评。因此，已经提出了另外2种措施来捕获嵌套模型的歧视改善：综合歧视改善和持续净重新分类改善。在本研究中，作者使用数学关系和数值模拟来量化不同强度的候选标记物所提供的区分度的改善，这些标记物通过其效应大小来测量。他们证明AUC的增加取决于基线模型的强度，这在较小程度上对综合辨别力的改善是真实的。另一方面，持续的净重新分类改善仅取决于候选变量的效应大小及其与其他预测因子的相关性。这些措施说明了使用Fracket模型的事件心房颤动。作者得出结论，AUC的增加、综合区分改善和净重新分类改善提供了补充信息，因此建议报告所有3项以及表征最终模型性能的指标。
The discrimination of a risk prediction model measures that models ability to distinguish between subjects with and without events. The area under the receiver operating characteristic curve (AUC) is a popular measure of discrimination. However, the AUC has recently been criticized for its insensitivity in model comparisons in which the baseline model has performed well. Thus, 2 other measures have been proposed to capture improvement in discrimination for nested models: the integrated discrimination improvement and the continuous net reclassification improvement. In the present study, the authors use mathematical relations and numerical simulations to quantify the improvement in discrimination offered by candidate markers of different strengths as measured by their effect sizes. They demonstrate that the increase in the AUC depends on the strength of the baseline model, which is true to a lesser degree for the integrated discrimination improvement. On the other hand, the continuous net reclassification improvement depends only on the effect size of the candidate variable and its correlation with other predictors. These measures are illustrated using the Framingham model for incident atrial fibrillation. The authors conclude that the increase in the AUC, integrated discrimination improvement, and net reclassification improvement offer complementary information and thus recommend reporting all 3 alongside measures characterizing the performance of the final model.