Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, MD 20852, USA.
Stat Med. 2011 Jul 20;30(16):2005-14. doi: 10.1002/sim.4238. Epub 2011 Apr 7.
Diagnostic accuracy can be improved considerably by combining multiple biomarkers. Although the likelihood ratio provides optimal solution to combination of biomarkers, the method is sensitive to distributional assumptions which are often difficult to justify. Alternatively simple linear combinations can be considered whose empirical solution may encounter intensive computation when the number of biomarkers is relatively large. Moreover, the optimal linear combinations derived under multivariate normality may suffer substantial loss of efficiency if the distributions are apart from normality. In this paper, we propose a new approach that linearly combines the minimum and maximum values of the biomarkers. Such combination only involves searching for a single combination coefficient that maximizes the area under the receiver operating characteristic (ROC) curves and is thus computation-effective. Simulation results show that the min-max combination may yield larger partial or full area under the ROC curves and is more robust against distributional assumptions. The methods are illustrated using the growth-related hormones data from the Growth and Maturation in Children with Autism or Autistic Spectrum Disorder Study (Autism/ASD Study).
通过联合使用多个生物标志物，可以显著提高诊断准确性。尽管似然比为生物标志物的联合提供了最佳解决方案，但该方法对分布假设非常敏感，而这些假设往往难以证明。或者可以考虑简单的线性组合，但当生物标志物数量较多时，其经验解可能需要大量计算。此外，在多元正态性下得出的最优线性组合，如果分布偏离正态性，可能会导致效率大幅降低。在本文中，我们提出了一种新的方法，即线性组合生物标志物的最小值和最大值。这种组合只涉及搜索单个组合系数，该系数最大化接收者操作特征（ROC）曲线下的面积，因此计算效率高。模拟结果表明，最小-最大组合可能会产生更大的 ROC 曲线下部分或全部面积，并且对分布假设更稳健。该方法使用来自自闭症或自闭症谱系障碍研究（自闭症/ASD 研究）中与生长相关的激素数据进行了说明。
