Schaarschmidt Frank, Hofmann Matthias, Jaki Thomas, Grün Bettina, Hothorn Ludwig A
Institute of Biostatistics, Leibniz Universität Hannover, Herrenhaeuser Str. 2, D-30419 Hannover, Germany.
Novartis Institutes for Biomedical Research, CH-4057 Basel, Switzerland.
J Immunol Methods. 2015 Mar;418:84-100. doi: 10.1016/j.jim.2015.02.004. Epub 2015 Feb 27.
Cut points in immunogenicity assays are used to classify future specimens into anti-drug antibody (ADA) positive or negative. To determine a cut point during pre-study validation, drug-naive specimens are often analyzed on multiple microtiter plates taking sources of future variability into account, such as runs, days, analysts, gender, drug-spiked and the biological variability of un-spiked specimens themselves. Five phenomena may complicate the statistical cut point estimation: i) drug-naive specimens may contain already ADA-positives or lead to signals that erroneously appear to be ADA-positive, ii) mean differences between plates may remain after normalization of observations by negative control means, iii) experimental designs may contain several factors in a crossed or hierarchical structure, iv) low sample sizes in such complex designs lead to low power for pre-tests on distribution, outliers and variance structure, and v) the choice between normal and log-normal distribution has a serious impact on the cut point. We discuss statistical approaches to account for these complex data: i) mixture models, which can be used to analyze sets of specimens containing an unknown, possibly larger proportion of ADA-positive specimens, ii) random effects models, followed by the estimation of prediction intervals, which provide cut points while accounting for several factors, and iii) diagnostic plots, which allow the post hoc assessment of model assumptions. All methods discussed are available in the corresponding R add-on package mixADA.
免疫原性分析中的切点用于将未来的样本分类为抗药物抗体（ADA）阳性或阴性。在研究前验证期间确定切点时，通常会在多个微量滴定板上分析未接触过药物的样本，同时考虑未来变异性的来源，如批次、日期、分析人员、性别、加药情况以及未加药样本本身的生物学变异性。有五种现象可能会使统计切点估计变得复杂：i）未接触过药物的样本可能已经是ADA阳性，或者产生错误地看似为ADA阳性的信号；ii）通过阴性对照均值对观测值进行归一化后，各板之间的均值差异可能仍然存在；iii）实验设计可能包含交叉或分层结构中的几个因素；iv）在这种复杂设计中样本量较小，导致对分布、异常值和方差结构进行预测试的功效较低；v）正态分布和对数正态分布之间的选择对切点有严重影响。我们讨论了处理这些复杂数据的统计方法：i）混合模型，可用于分析包含未知比例（可能更大）的ADA阳性样本的样本集；ii）随机效应模型，随后估计预测区间，在考虑多个因素的同时提供切点；iii）诊断图，可用于对模型假设进行事后评估。所讨论的所有方法在相应的R附加包mixADA中均可用。
