tetano
Editor, Senior Moderator
Vaccine
. 2025 Feb 4:49:126802.
doi: 10.1016/j.vaccine.2025.126802. Online ahead of print. Exploring bias due to below-limit-of-detection values in influenza vaccine antibody modeling: A case study and instructional guide for the CIVIC study
Yang Ge[SUP] 1 [/SUP], Andreas Handel[SUP] 2 [/SUP], Philippe J Giabbanelli[SUP] 3 [/SUP], Jennifer Lemacks[SUP] 4 [/SUP], Tammy Greer[SUP] 5 [/SUP], Pooja Raynee[SUP] 4 [/SUP], Justin Bahl[SUP] 6 [/SUP], Amanda L Skarlupka[SUP] 7 [/SUP], Kevin K Dobbin[SUP] 8 [/SUP], Ted M Ross[SUP] 9 [/SUP], Ye Shen[SUP] 10 [/SUP]
Affiliations
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.
Keywords: HAI; Influenza; LOD; Limit of detection; Vaccine.
. 2025 Feb 4:49:126802.
doi: 10.1016/j.vaccine.2025.126802. Online ahead of print. Exploring bias due to below-limit-of-detection values in influenza vaccine antibody modeling: A case study and instructional guide for the CIVIC study
Yang Ge[SUP] 1 [/SUP], Andreas Handel[SUP] 2 [/SUP], Philippe J Giabbanelli[SUP] 3 [/SUP], Jennifer Lemacks[SUP] 4 [/SUP], Tammy Greer[SUP] 5 [/SUP], Pooja Raynee[SUP] 4 [/SUP], Justin Bahl[SUP] 6 [/SUP], Amanda L Skarlupka[SUP] 7 [/SUP], Kevin K Dobbin[SUP] 8 [/SUP], Ted M Ross[SUP] 9 [/SUP], Ye Shen[SUP] 10 [/SUP]
Affiliations
- PMID: 39908720
- DOI: 10.1016/j.vaccine.2025.126802
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.
Keywords: HAI; Influenza; LOD; Limit of detection; Vaccine.