Statistical Methods for Evaluation of Biomarkers

当生物标志物用于疾病诊断时，众所周知，单一的生物标志物通常是不够的，与其他生物标志物的结合可以显著提高诊断的准确性。然而，当仅有组测试数据可用时，组合生物标记物还没有在文献中被提及。在过去的一年里，该项目的研究人员和工作人员开发了结合生物标记物的新方法，以提高基于群体测试数据的诊断准确性。其目的是获得和比较群体测试和个体测试中的AUC估计，同时纳入测试结果的错误分类，这些错误分类可能会随着群体规模和组中患病个体数量的变化而变化。该团队开发了一种创新的方法，基于成对的双变量拟合，假设生物标志物服从多变量分布，计算精度的最大似然估计。 该团队还开发了另一种方法，通过比较正态、高斯、对数正态、非参数假设下的团体测试和个体测试中的Youden指数及其相关截止点，证明了团体测试不仅降低了研究成本，而且可以提高Youden指数估计的统计精度。

When biomarkers are used for disease diagnosis it is well known that single biomarker is usually not sufficient and combing with other biomarkers can substantially improve the diagnostic accuracy. However, combining biomarkers when only group testing data are available has not been addressed in the literature. In the past year investigators and staff on this project developed novel methods for combining biomarkers to improve the diagnostic accuracy based on group testing data. The objective is to obtain and compare the AUC estimation in group testing and individual testing, while incorporating misclassification of testing results that may vary as the group size and the number of the diseased individuals in the group changes. The team developed an innovative approach to compute the maximum likelihood estimates of the accuracy based on the pairwise bivariate fitting, assuming that the biomarkers follow multivariate distributions. The team also developed another approach through comparing Youden Index and the associated cut-off point in group testing and individual testing under Normal, Gaussian, Log-Normal, non-parametric assumption, respectively, and demonstrated that group testing not only reduces the study cost but also can improve the statistical precision for estimation of the Youden index.