论文标题
修改后的净重新分类改进统计量
A Modified Net Reclassification Improvement Statistic
论文作者
论文摘要
连续的净重新分类改进（NRI）统计量是一种流行的模型变更度量，用于评估风险预测模型中新因素的增量价值。文献中确定的两个突出的统计问题将该措施的效用归功于：（1）它不是一个适当的评分函数，并且（2）在测试新因素是否有助于风险模型时，它具有很高的误报率。对于二进制响应回归模型，提出了这些受试者的审问，并在基于似然的得分残留的指导下对连续NRI进行了修改，以解决这些问题。在嵌套模型框架内，修改后的NRI可以被视为两个风险模型之间的距离度量。使用前列腺癌数据说明了修改后的NRI的应用。
The continuous net reclassification improvement (NRI) statistic is a popular model change measure that was developed to assess the incremental value of new factors in a risk prediction model. Two prominent statistical issues identified in the literature call the utility of this measure into question: (1) it is not a proper scoring function and (2) it has a high false positive rate when testing whether new factors contribute to the risk model. For binary response regression models, these subjects are interrogated and a modification of the continuous NRI, guided by the likelihood-based score residual, is proposed to address these issues. Within a nested model framework, the modified NRI may be viewed as a distance measure between two risk models. An application of the modified NRI is illustrated using prostate cancer data.
