Abstract–In the pharmaceutical industry, all analytical methods must be shown to deliver unbiased and precise results. In an assay qualification or validation study, the trueness, accuracy, and intermediate precision are usually assessed by comparing the measured concentrations to their nominal levels. Trueness is assessed by using Confidence Intervals (CIs) of mean measured concentration, accuracy by Prediction Intervals (PIs) for a future measured concentration, and the intermediate precision by the total variance. ICH and USP guidelines alike request that all relevant sources of variability must be studied, for example, the effect of different technicians, the day-to-day variability or the use of multiple reagent lots. Those different random effects must be modeled as crossed, nested, or a combination of both; while concatenating them to simplify the model is often taken place. This article compares this simplified approach to a mixed model with the actual design. Our simulation study shows an under-estimation of the intermediate precision and, therefore, a substantial reduction of the CI and PI. The power for accuracy or trueness is consequently over-estimated when designing a new study. Two real datasets from assay validation study during vaccine development are used to illustrate the impact of such concatenation of random variables.

摘要-在制药行业，必须证明所有分析方法均能提供无偏且精确的结果。在化验鉴定或验证研究中，通常通过将测得的浓度与其标称水平进行比较来评估真实性，准确性和中间精度。通过使用平均测得浓度的置信区间（CI），通过对未来测得浓度的预测区间（PIs）的准确性以及通过总方差的中间精度来评估真实性。ICH和USP准则均要求必须研究所有相关的变异性来源，例如，不同技术人员的影响，日常变异性或使用多个试剂批次。必须将那些不同的随机效应建模为交叉，嵌套或两者的组合。同时将它们连接起来以简化模型通常会发生。本文将这种简化方法与具有实际设计的混合模型进行了比较。我们的模拟研究表明对中间精度的估计不足，因此，CI和PI大大降低了。设计新研究时，准确性或真实性的功效因此被高估了。在疫苗开发过程中，来自分析验证研究的两个真实数据集用于说明这种随机变量串联的影响。设计新研究时，准确性或真实性的功效因此被高估了。在疫苗开发过程中，来自分析验证研究的两个真实数据集用于说明这种随机变量串联的影响。设计新研究时，准确性或真实性的功效因此被高估了。在疫苗开发过程中，来自分析验证研究的两个真实数据集用于说明这种随机变量串联的影响。