Zhang Jianchun, Li Wenjia, Roskos Lorin K, Yang Harry
MedImmune LLC, Gaithersburg, MD, United States.
University of Maryland, College Park, MD, United States.
J Immunol Methods. 2017 Mar;442:29-34. doi: 10.1016/j.jim.2017.01.001. Epub 2017 Jan 5.
The newly released FDA guidance on immunogenicity assay development and validation recommends use of a lower confidence limit of the percentile of the negative subject population as the cut point in order to guarantee a pre-specified false positive rate with high confidence. The limit is, in essence, a lower tolerance limit. Although in literature several methods are available for determining the tolerance limit, they either fail to take into account the repeated measurement of the data from a typical immunogenicity assay quantification/validation experiment or rely heavily on normality assumption of the data, which is rarely correct. As a result, the methods may result in biased estimates of the cut point, causing the false positive rate to be either lower or higher than expected. To overcome this drawback, we propose two non-parametric methods under repeated measure data structure and without normal distribution assumption. Simulation studies were carried to compare the performance of the two non-parametric approaches with the current methods. The results of the simulation studies show that one of the two nonparametric methods outperforms all the other methods and provides a satisfactory coverage probability even with moderate sample sizes. In addition, it is simple and straightforward to implement. Therefore, it is a preferred method for immunogenicity assay cut point determination.
美国食品药品监督管理局（FDA）最新发布的关于免疫原性测定方法开发与验证的指南建议，使用阴性受试者群体百分位数的较低置信限作为截断点，以便在高置信度下保证预先设定的假阳性率。本质上，该限值是一个较低的容忍限。尽管文献中有几种确定容忍限的方法，但它们要么没有考虑到典型免疫原性测定定量/验证实验中数据的重复测量，要么严重依赖数据的正态性假设，而这很少是正确的。因此，这些方法可能会导致截断点的估计有偏差，使假阳性率低于或高于预期。为克服这一缺点，我们提出了两种在重复测量数据结构下且无需正态分布假设的非参数方法。进行了模拟研究，以比较这两种非参数方法与当前方法的性能。模拟研究结果表明，两种非参数方法中的一种优于所有其他方法，即使样本量适中也能提供令人满意的覆盖概率。此外，它实现起来简单直接。因此，它是免疫原性测定截断点确定的首选方法。
