Huang Ying, Sullivan Pepe Margaret, Feng Ziding
University of Washington Biostatistics Department, F-600 Health Sciences Building, Box 357232, Seattle, Washington 98195-7232, USA.
Biometrics. 2007 Dec;63(4):1181-8. doi: 10.1111/j.1541-0420.2007.00814.x. Epub 2007 May 8.
Consider a continuous marker for predicting a binary outcome. For example, the serum concentration of prostate specific antigen may be used to calculate the risk of finding prostate cancer in a biopsy. In this article, we argue that the predictive capacity of a marker has to do with the population distribution of risk given the marker and suggest a graphical tool, the predictiveness curve, that displays this distribution. The display provides a common meaningful scale for comparing markers that may not be comparable on their original scales. Some existing measures of predictiveness are shown to be summary indices derived from the predictiveness curve. We develop methods for making inference about the predictiveness curve, for making pointwise comparisons between two curves, and for evaluating covariate effects. Applications to risk prediction markers in cancer and cystic fibrosis are discussed.
考虑一个用于预测二元结局的连续标志物。例如，前列腺特异性抗原的血清浓度可用于计算活检中发现前列腺癌的风险。在本文中，我们认为标志物的预测能力与给定标志物的风险人群分布有关，并提出一种图形工具——预测性曲线，用于展示这种分布。该展示提供了一个通用的有意义尺度，用于比较那些在原始尺度上可能不可比的标志物。一些现有的预测性度量被证明是从预测性曲线导出的汇总指标。我们开发了用于对预测性曲线进行推断、在两条曲线之间进行逐点比较以及评估协变量效应的方法。还讨论了其在癌症和囊性纤维化风险预测标志物方面的应用。
