Statistical Methods for Evaluation of Biomarkers

当生物标志物用于疾病诊断时，众所周知，单一生物标志物通常是不够的，并且与其他生物标志物组合可以显著提高诊断准确性。然而，在文献中还没有讨论当只有组测试数据可用时组合生物标志物。在过去的一年里，该项目的研究人员和工作人员开发了新的方法来结合生物标志物，以提高基于组检测数据的诊断准确性。目的是获得并比较组检测和个体检测中的AUC估计值，同时纳入可能随组大小和组中患病个体数量变化而变化的检测结果的错误分类。该团队开发了一种创新的方法来计算基于成对双变量拟合的准确性的最大似然估计，假设生物标志物遵循多变量分布。 该团队还开发了另一种方法，通过比较分别在正态，高斯，对数正态，非参数假设下的群体测试和个体测试中的Youden指数和相关截止点，并证明了群体测试不仅降低了研究成本，而且还可以提高估计Youden指数的统计精度。

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.