Pepe Margaret S, Feng Ziding, Huang Ying, Longton Gary, Prentice Ross, Thompson Ian M, Zheng Yingye
Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
Am J Epidemiol. 2008 Feb 1;167(3):362-8. doi: 10.1093/aje/kwm305. Epub 2007 Nov 2.
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.
有两种常用的生物标志物评估统计方法。一种方法是使用例如逻辑回归对疾病风险（或疾病结局）进行建模。如果一个标志物对风险有强烈影响，那么它就被认为是有用的。第二种方法是通过使用灵敏度、特异性、预测值和受试者工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同一数据给出相互矛盾的结果。作者提出了一种新的图形——预测性曲线，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。尽管预测性曲线与分类性能指标相关，但它也显示了受试者工作特征曲线未显示的关于风险的重要信息。作者建议，在一个综合图表中一起展示标志物的预测性和分类性能，可以对风险标志物或模型进行全面且连贯的评估。文中用1993 - 2003年前列腺癌预防试验中前列腺特异性抗原和风险因素的数据对这些方法进行了演示。
