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.
Keywords: Bootstrap; Cut point; Immunogenicity; Nonparametric method; Random effect model; Tolerance interval.
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