What to expect from net reclassification improvement with three categories
What to expect from net reclassification improvement with three categories
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DOI：
10.1002/sim.6286
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发表时间：
2014-12-10
影响因子：
2
通讯作者：
D'Agostino, Ralph B., Sr.
中科院分区：
文献类型：
--
作者：
Pencina, Karol M.;Pencina, Michael J.;D'Agostino, Ralph B., Sr.
净重新分类改进（NRI）已成为一种流行的衡量增加有用的标记物添加到风险预测模型。然而，三类NRI的预期大小并不清楚，导致研究人员依赖于统计显著性。在本文中，我们描述了一个轻微的修改，原来的定义的NRI，它的重量每一个重新分类的类别的数量，一个给定的个人被重新分类。这种修改解决了最近对三类NRI的一些批评，同时对它的规模影响很小。然后，我们表明，使用这个修改后的定义，事件和非事件NRI有简单的解释为计算在风险阈值的敏感性和特异性的变化的总和。我们利用这种关系来达到封闭形式的解决方案下正常的事件和非事件子群的NRI。我们观察到，中等风险类别的大小和事件发生率对NRI的大小影响有限。正如预期的那样，NRI随着添加的标记物的强度而增加，并且对于具有非弱净效应大小（大于0.25）的标记物，这种关系似乎相当成比例。此外，我们得出结论，使用NRI作为度量标准，很难改进已经表现良好的模型。版权所有（C）2014 JohnWiley & Sons，Ltd.
The net reclassification improvement (NRI) has become a popular measure of incremental usefulness of markers added to risk prediction models. However, the expected magnitude of the three-category NRI is not well understood, leading researchers to rely on statistical significance. In this paper, we describe a slight modification to the original definition of the NRI, which weighs each reclassification by the number of categories by which a given individual is reclassified. This modification resolves some recent criticisms of the three-category NRI and at the same time has a minimal impact on itsmagnitude. Then we show that using this modified definition, the event and nonevent NRIs have simple interpretations as sums of changes in sensitivities and specificities calculated at the risk thresholds. We exploit this relationship to arrive at closed-form solutions for the NRI under normality within the event and nonevent subgroups. We observe that the size of the intermediate risk category and the event rate have limited impact on the magnitude of the NRI. As expected, the NRI increases with the strength of the added marker, and this relationship appears fairly proportional for markers with non-weak net effect size (above 0.25). Furthermore, we conclude that using the NRI as a metric, it is harder to improve models that already perform well. Copyright (C) 2014 JohnWiley & Sons, Ltd.