Integrating the predictiveness of a marker with its performance as a classifier
Integrating the predictiveness of a marker with its performance as a classifier
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DOI：
10.1093/aje/kwm305
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发表时间：
2008-02-01
影响因子：
5
通讯作者：
Zheng, Yingye
中科院分区：
文献类型：
--
作者：
Pepe, Margaret S.;Feng, Ziding;Zheng, Yingye
生物标志物评估有两种流行的统计方法。一种是通过逻辑回归等方法对疾病风险（或疾病结果）进行建模。如果一个标记对风险有很强的影响，则该标记被认为是有用的。第二个方法通过使用敏感性、特异性、预测值和接受者操作特征曲线等指标来评估分类性能。关于哪种方法更合适存在争议。此外，这两种方法可能会对相同的数据给出矛盾的结果。作者提出了一种新的图形，即预测曲线，它补充了风险建模方法。它评估风险模型应用于人群时的有用性。尽管预测曲线与分类性能测量相关，但它还显示了接收者操作特征曲线未显示的有关风险的基本信息。作者提出，将标记的预测性和分类性能一起显示在综合图中，可以对风险标记或模型提供全面且一致的评估。这些方法通过 1993-2003 年前列腺癌预防试验中前列腺特异性抗原和危险因素的数据得到证实。
There are two popular statistical approaches to biomarker evaluation. One models the risk of disease (or disease outcome) with, for example, logistic regression. A marker is considered useful if it has a strong effect on risk. The second evaluates classification performance by use of measures such as sensitivity, specificity, predictive values, and receiver operating characteristic curves. There is controversy about which approach is more appropriate. Moreover, the two approaches can give contradictory results on the same data. The authors present a new graphic, the predictiveness curve, which complements the risk modeling approach. It assesses the usefulness of a risk model when applied to the population. Although the predictiveness curve relates to classification performance measures, it also displays essential information about risk that is not displayed by the receiver operating characteristic curve. The authors propose that the predictiveness and classification performance of a marker, displayed together in an integrated plot, provide a comprehensive and cohesive assessment of a risk marker or model. The methods are demonstrated with data on prostate-specific antigen and risk factors from the Prostate Cancer Prevention Trial, 1993-2003.