Power and Sample Size Estimation for the Wilcoxon Rank Sum Test with Application to Comparisons of C Statistics from Alternative Prediction Models
Power and Sample Size Estimation for the Wilcoxon Rank Sum Test with Application to Comparisons of C Statistics from Alternative Prediction Models
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DOI：
10.1111/j.1541-0420.2008.01062.x
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发表时间：
2009-03-01
期刊：
影响因子：
1.9
通讯作者：
Glynn, R. J.
中科院分区：
文献类型：
--
作者：
Rosner, B.;Glynn, R. J.
当基础分布的正态性有问题时，通常使用WMW U检验进行非参数两组比较。先前已经有一些基于这一过程的估计功率的工作（Lehmann, 1998, nonparametric）。在本文中，我们提出了一种估计II型误差的方法，该方法适用于任何连续分布，并且还扩展了该方法来处理允许关联的分组连续数据。我们将这些结果应用于获得H-1下风险预测规则的接收者工作特征曲线下面积（AUROC）的标准误差，并比较应用于同一数据集的竞争风险预测规则之间的AUROC。这些结果是基于sas可调用的函数来评估二元正态积分，因此很容易用标准软件实现。
The Wilcoxon Mann-Whitney (WMW) U test is commonly used in nonparametric two-group comparisons when the normality of the underlying distribution is questionable. There has been some previous work on estimating power based on this procedure (Lehmann, 1998, Nonparametrics). In this article, we present an approach for estimating type II error, which is applicable to any continuous distribution, and also extend the approach to handle grouped continuous data allowing for ties. We apply these results to obtaining standard errors of the area under the receiver operating characteristic curve (AUROC) for risk-prediction rules under H-1 and for comparing AUROC between competing risk prediction rules applied to the same data set. These results are based on SAS-callable functions to evaluate the bivariate normal integral and are thus easily implemented with standard software.