Optimal Spiking Experiment for Noninferiority of Qualitative Microbiological Methods on Accuracy With Multiple Microorganisms

The European and United States Pharmacopoeia demand a noninferiority study on the detection of microorganisms when an alternate qualitative microbiological method is intended to replace the compendial microbiological method. However, without imposing any modeling assumptions or constraints, noninferiority studies require large numbers of test samples for a proposed noninferiority criterion of 0.7 or higher for each microorganism. When we can assume that the accuracy of the alternate method with respect to the compendial method is homogeneous across microorganisms, a joint statistical analysis of the data from all microorganisms can be used to help reduce the sample size dramatically. For this situation, we provide a test statistic for noninferiority, an optimal spiking experiment, and a sample size calculation approach under only mild modeling assumptions of the microorganism-specific detection proportions. We illustrate our approach on a real dataset and demonstrate good performance of our method using simulation studies.

当拟采用替代定性微生物学方法（alternate qualitative microbiological method）取代法定药典微生物检测方法（compendial microbiological method）时，《欧洲药典》与《美国药典》要求开展微生物检测的非劣效性研究（noninferiority study）。然而，若不施加任何建模假设或约束条件，当针对每种微生物设定0.7及以上的非劣效性界值时，该类非劣效性研究需要大量测试样本。若可假设替代方法相对于法定方法的准确度在所有微生物间保持同质，则可对所有微生物的检测数据开展联合统计分析，从而大幅缩减样本量。针对该场景，我们在仅针对微生物特异性检测比例施加较弱建模假设的前提下，提出了非劣效性检验统计量（test statistic）、最优加标实验（spiking experiment）以及样本量计算方法。我们通过真实数据集对所提方法进行了演示，并借助仿真研究（simulation studies）验证了该方法的优良性能。