Approximating the risk score for disease diagnosis using MARS.
Approximating the risk score for disease diagnosis using MARS.
复制标题
使用 MARS 估算疾病诊断的风险评分。
DOI：
10.1080/0266476yyxxxxxxxx
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发表时间：
2009
影响因子：
1.5
通讯作者：
Yu,Binbing
中科院分区：
文献类型：
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作者：
Yu,Binbing
在疾病筛查和诊断中，经常测量并组合多个标志物以提高诊断的准确性。McIntosh和Pepe [结合几种筛选测试：风险评分的最优性，生物统计学58（2002），第10页。657-664]表明，定义为以多个标记为条件的疾病概率的风险评分是基于Neyman-Pearson引理的分类的最佳函数。他们提出了一个两步程序来近似风险评分。然而，所得到的受试者工作特征（ROC）曲线仅定义在（0，1）中假阳性率的子范围（L，h）内，并且下限L的确定需要额外的先验信息。在实践中，大多数诊断测试并不完美，通常很少有一个单一的标志物是一致优于其他测试。使用模拟，我表明，多变量自适应回归样条是一个有用的工具，以近似的风险评分时，结合多个标志物，特别是当ROC曲线从多个测试交叉。由此产生的ROC定义在整个范围内（0，1），易于实现，并具有直观的解释。应用程序的示例代码见附录。
In disease screening and diagnosis, often multiple markers are measured and combined to improve the accuracy of diagnosis. McIntosh and Pepe [Combining several screening tests: optimality of the risk score, Biometrics 58 (2002), pp. 657–664] showed that the risk score, defined as the probability of disease conditional on multiple markers, is the optimal function for classification based on the Neyman–Pearson lemma. They proposed a two-step procedure to approximate the risk score. However, the resulting receiver operating characteristic (ROC) curve is only defined in a subrange (L, h) of false-positive rates in (0,1) and the determination of the lower limitLneeds extra prior information. In practice, most diagnostic tests are not perfect, and it is usually rare that a single marker is uniformly better than the other tests. Using simulation, I show that multivariate adaptive regression spline is a useful tool to approximate the risk score when combining multiple markers, especially when ROC curves from multiple tests cross. The resulting ROC is defined in the whole range of (0,1) and is easy to implement and has intuitive interpretation. The sample code of the application is shown in the appendix.