Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
Stat Med. 2023 Jan 30;42(2):164-177. doi: 10.1002/sim.9607. Epub 2022 Nov 20.
In vaccine research towards the prevention of infectious diseases, immune response biomarkers serve as an important tool for comparing and ranking vaccine candidates based on their immunogenicity and predicted protective effect. However, analyses of immune response outcomes can be complicated by differences across assays when immune response data are acquired from multiple groups/laboratories. Motivated by a real-world problem to accommodate the use of two different neutralization assays in COVID-19 vaccine trials, we propose methods based on left-censored multivariate normal model assuming common assay differences across settings, to adjust for differences between assays with respect to measurement error and the lower limit of detection. Our proposed methods integrate external paired-sample data with bridging assumptions to achieve two objectives, both using pooled data acquired from different assays: (i) comparing immunogenicity between vaccine regimens, and (ii) evaluating correlates of risk. In simulation studies, for the first objective, our method leads to unbiased calibrated assay mean with good coverage of bootstrap confidence interval, as well as valid test for immunogenicity comparison, while the alternative method assuming constant calibration model between assays leads to biased estimate of assay mean with undercoverage problem and invalid test with inflated type-I error; for the second objective, in the presence of noticeable left-censoring rate, our proposed method can drastically outperform the existing method that ignores left-censoring, in terms of reduced bias and improved precision. We apply the proposed methods to SARS-CoV-2 spike-pseudotyped virus neutralization assay data generated in vaccine and convalescent samples by two different laboratories.
在预防传染病的疫苗研究中，免疫反应生物标志物是一种重要的工具，可用于根据免疫原性和预测的保护效果比较和排序候选疫苗。然而，当从多个组/实验室获取免疫反应数据时，由于不同的检测方法之间存在差异，免疫反应结果的分析可能会变得复杂。受一个现实问题的启发，该问题涉及在 COVID-19 疫苗试验中容纳两种不同的中和检测方法的使用，我们提出了基于左删失多元正态模型的方法，假设在不同的检测环境中存在共同的检测差异，以调整检测方法之间的差异，包括测量误差和检测下限。我们提出的方法整合了外部配对样本数据和桥接假设，以实现两个目标，均使用来自不同检测方法的汇总数据：（i）比较疫苗方案之间的免疫原性，（ii）评估风险相关性。在模拟研究中，对于第一个目标，我们的方法可以实现无偏校准的检测平均值，具有良好的自举置信区间覆盖率，并且免疫原性比较的检验有效，而另一种方法假设检测之间的校准模型不变，则会导致检测平均值的有偏估计和检验无效，存在过大的Ⅰ类错误；对于第二个目标，在存在明显的左删失率的情况下，我们提出的方法可以大大优于忽略左删失的现有方法，在降低偏差和提高精度方面具有优势。我们将提出的方法应用于由两个不同实验室生成的 SARS-CoV-2 刺突假病毒中和检测方法数据，这些数据来自疫苗和恢复期样本。
