Evaluating the improvement in diagnostic utility from adding new predictors.
Evaluating the improvement in diagnostic utility from adding new predictors.
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评估添加新预测变量对诊断效用的改进。
DOI：
10.1002/bimj.200900228
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Lu,Ying
中科院分区：
文献类型：
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作者：
Li,Caixia;Lu,Ying
多种诊断试验和危险因素通常适用于许多疾病。这些信息可能是多余的，也可能是免费的。组合它们可能会提高诊断/预测的准确性，但也会不必要地增加复杂性、风险和/或成本。通过加入附加变量而获得的改进的精度可以通过在有和没有新变量(S)的情况下的接收器工作特性曲线(AuC)下面积的增量来评估。在这项研究中，我们推导出一个新的检验统计量，以准确和有效地确定这种增量AUC在多元正态假设下的统计意义。我们的检验通过对所有诊断变量进行适当的线性变换，将AUC差与以逆协方差矩阵为单位的标准化均值漂移的二次形式联系起来。二次估计的分布与多元Behrens-Fisher问题有关。我们给出了估计量及其近似非中心F分布、第I类错误率和样本量公式的显式数学解。我们使用模拟研究来证明我们的新测试在实际样本量下保持了预先指定的I类错误率以及合理的统计能力。我们使用来自骨质疏松性骨折研究的数据作为应用实例来说明我们的方法。
Multiple diagnostic tests and risk factors are commonly available for many diseases. This information can be either redundant or complimentary. Combining them may improve the diagnostic/predictive accuracy, but also unnecessarily increase complexity, risks, and/or costs. The improved accuracy gained by including additional variables can be evaluated by the increment of the area under (AUC) the receiver‐operating characteristic curves with and without the new variable(s). In this study, we derive a new test statistic to accurately and efficiently determine the statistical significance of this incremental AUC under a multivariate normality assumption. Our test links AUC difference to a quadratic form of a standardized mean shift in a unit of the inverse covariance matrix through a properly linear transformation of all diagnostic variables. The distribution of the quadratic estimator is related to the multivariate Behrens–Fisher problem. We provide explicit mathematical solutions of the estimator and its approximate non‐centralF‐distribution, type I error rate, and sample size formula. We use simulation studies to prove that our new test maintains prespecified type I error rates as well as reasonable statistical power under practical sample sizes. We use data from the Study of Osteoporotic Fractures as an application example to illustrate our method.