Department of Biostatistics, School of Public Health, University of Washington, Seattle, USA.
Am J Epidemiol. 2011 Aug 1;174(3):364-74. doi: 10.1093/aje/kwr086. Epub 2011 Jun 14.
The integrated discrimination improvement (IDI) index is a popular tool for evaluating the capacity of a marker to predict a binary outcome of interest. Recent reports have proposed that the IDI is more sensitive than other metrics for identifying useful predictive markers. In this article, the authors use simulated data sets and theoretical analysis to investigate the statistical properties of the IDI. The authors consider the common situation in which a risk model is fitted to a data set with and without the new, candidate predictor(s). Results demonstrate that the published method of estimating the standard error of an IDI estimate tends to underestimate the error. The z test proposed in the literature for IDI-based testing of a new biomarker is not valid, because the null distribution of the test statistic is not standard normal, even in large samples. If a test for the incremental value of a marker is desired, the authors recommend the test based on the model. For investigators who find the IDI to be a useful measure, bootstrap methods may offer a reasonable option for inference when evaluating new predictors, as long as the added predictive capacity is large.
综合判别改善（IDI）指数是一种常用于评估标志物预测感兴趣的二项结局能力的工具。最近的报告表明，IDI 比其他指标更能敏感地识别有用的预测标志物。在本文中，作者使用模拟数据集和理论分析来研究 IDI 的统计性质。作者考虑了一种常见的情况，即风险模型拟合有和没有新候选预测因子的数据集。结果表明，用于估计 IDI 估计标准误差的已发表方法往往会低估误差。文献中提出的基于 IDI 的新生物标志物检验的 z 检验是无效的，因为即使在大样本中，检验统计量的零分布也不是标准正态分布。如果需要对标志物的增量值进行检验，作者建议基于模型的检验。对于发现 IDI 是一种有用的度量的研究人员来说，当评估新的预测因子时，自举方法可能是一种合理的推断选择，只要增加的预测能力很大。
