Informatics Core, Clinical and Translational Science Institute, University of Rochester Medical Center, Rochester, NY, USA.
Department of Medicine, Division of Nephrology, University of Rochester Medical Center, Rochester, NY, USA.
Comput Math Methods Med. 2019 May 8;2019:9287120. doi: 10.1155/2019/9287120. eCollection 2019.
The current gold standard for measuring antibody-based immunity to influenza viruses relies on the hemagglutinin inhibition assay (HAI), an 80-year-old technology, and the microneutralization assay (MN). Both assays use serial dilution to provide a discrete, ranked readout of 8-14 categorical titer values for each sample. In contrast to other methods of measuring vaccine antibody levels that produce a continuous readout (i.e., mPLEX-Flu and ELISA), titering methods introduce imprecision and increase false discovery rates (FDR). In this paper, we assess the degree of such statistical errors, first with simulation studies comparing continuous data with titer data in influenza vaccine study group comparison analyses and then by analyzing actual sample data from an influenza vaccine trial. Our results show the superiority of using continuous, rather than discrete, readout assays. Compared to continuous readout assays, titering assays have a lower statistical precision and a higher FDR. The results suggested that traditional titering assays could lead to increased Type-II errors in the comparison of different therapeutic arms of an influenza vaccine trial. These statistical issues are related to the mathematical nature of titer-based assays, which we examine in detail in the simulation studies. Continuous readout assays are free of this issue, and thus it is possible that comparisons of study groups could provide different results with these two methods as we have shown in our case study.
目前，衡量针对流感病毒的抗体免疫的金标准依赖于血凝抑制测定（HAI）和微量中和测定（MN），这两种技术都采用连续稀释法提供 8-14 个分类滴度值的离散、分级读数。与产生连续读数的其他疫苗抗体水平测量方法（即 mPLEX-Flu 和 ELISA）不同，滴度测定方法会引入不精确性并增加假发现率（FDR）。在本文中，我们通过流感疫苗研究组比较分析中的模拟研究，比较了连续数据与滴度数据，然后通过分析流感疫苗试验的实际样本数据，评估了这种统计误差的程度。我们的结果表明，使用连续而非离散的读数测定方法具有优势。与连续读数测定方法相比，滴度测定方法的统计精度较低，假发现率（FDR）较高。结果表明，在流感疫苗试验的不同治疗组之间的比较中，传统的滴度测定方法可能会导致 II 型错误增加。这些统计问题与基于滴度的测定方法的数学性质有关，我们在模拟研究中对此进行了详细研究。连续读数测定方法没有这个问题，因此，正如我们在案例研究中所示，这两种方法可能会对研究组的比较产生不同的结果。
