探索流感疫苗抗体建模中因低于检测限的值导致的偏差：CIVIC研究的案例分析与指导手册
Exploring bias due to below-limit-of-detection values in influenza vaccine antibody modeling: A case study and instructional guide for the CIVIC study.
作者信息
Ge Yang, Handel Andreas, Giabbanelli Philippe J, Lemacks Jennifer, Greer Tammy, Raynee Pooja, Bahl Justin, Skarlupka Amanda L, Dobbin Kevin K, Ross Ted M, Shen Ye
机构信息
College of Public Health, The University of Georgia, Athens 30606, GA, USA.
College of Public Health, The University of Georgia, Athens 30606, GA, USA; Center for the Ecology of Infectious Diseases, The University of Georgia, Athens 30606, GA, USA.
出版信息
Vaccine. 2025 Mar 7;49:126802. doi: 10.1016/j.vaccine.2025.126802. Epub 2025 Feb 4.
In many laboratory assay datasets, missing values due to a limit of detection (LOD) are not uncommon. We observed this issue in our CIVIC-UGAFLUVAC hemagglutination inhibition assay (HAI) dataset. The standard imputation method recodes these values as either equal to the LOD or LOD/2. However, ignoring censoring can lead to falsely significant results in research. In this study, we explored the bias in modeling vaccine HAI titer increase. Moreover, we modified the titer increase modeling within the interval censoring framework to adjust for bias in parameter estimates. Our method provided less biased results compared to the standard imputation method. We anticipate that this study will serve as a case study and instructional guide for future vaccine research.
在许多实验室检测数据集中，由于检测限（LOD）导致的缺失值并不罕见。我们在CIVIC - UGAFLUVAC血凝抑制试验（HAI）数据集中观察到了这个问题。标准的插补方法将这些值重新编码为等于检测限或检测限的一半。然而，忽略删失可能会在研究中导致错误的显著结果。在本研究中，我们探讨了在对疫苗血凝抑制效价增加进行建模时的偏差。此外，我们在区间删失框架内修改了效价增加模型，以调整参数估计中的偏差。与标准插补方法相比，我们的方法提供了偏差较小的结果。我们预计这项研究将作为未来疫苗研究的一个案例研究和指导指南。
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