Department of Biostatistics, Boston University, Boston, MA 02115, USA.
Clin Chem Lab Med. 2010 Dec;48(12):1703-11. doi: 10.1515/CCLM.2010.340. Epub 2010 Aug 18.
The discovery and development of new biomarkers continues to be an exciting and promising field. Improvement in prediction of risk of developing disease is one of the key motivations in these pursuits. Appropriate statistical measures are necessary for drawing meaningful conclusions about the clinical usefulness of these new markers. In this review, we present several novel metrics proposed to serve this purpose. We use reclassification tables constructed on the basis of clinically meaningful disease risk categories to discuss the concepts of calibration, risk separation, risk discrimination, and risk classification accuracy. We discuss the notion that the net reclassification improvement (NRI) is a simple yet informative way to summarize information contained in risk reclassification tables. In the absence of meaningful risk categories, we suggest a 'category-less' version of the NRI and integrated discrimination improvement as metrics to summarize the incremental value of new biomarkers. We also suggest that predictiveness curves be preferred to receiver operating characteristic curves as visual descriptors of a statistical model's ability to separate predicted probabilities of disease events. Reporting of standard metrics, including measures of relative risk and the c statistic, is still recommended. These concepts are illustrated with a risk prediction example using data from the Framingham Heart Study.
新生物标志物的发现和开发仍然是一个令人兴奋和充满希望的领域。改善对疾病发生风险的预测是这些研究的关键动机之一。适当的统计措施对于得出关于这些新标志物临床有用性的有意义结论是必要的。在这篇综述中，我们提出了几种新的指标，用于达到这个目的。我们使用基于临床有意义的疾病风险类别构建的再分类表来讨论校准、风险分离、风险区分和风险分类准确性的概念。我们讨论了净再分类改善（NRI）作为一种简单而有信息量的方法来总结风险再分类表中包含的信息的概念。在没有有意义的风险类别的情况下，我们建议使用“无类别”版本的 NRI 和综合判别改善作为指标，以总结新生物标志物的增量价值。我们还建议使用预测曲线而不是接收者操作特征曲线作为统计模型区分疾病事件预测概率能力的可视化描述符。仍然建议报告标准指标，包括相对风险和 c 统计量的测量值。使用弗雷明汉心脏研究的数据，我们通过一个风险预测示例来说明这些概念。
