Leveraging immunogenicity data and logistic regression for detection of covariate effects on vaccine efficacy

0576941 - ÚI 2024 RIV SI eng A - Abstrakt
Dudášová, J. - Valenta, Zdeněk - Nieddu, G. T. - Becker, A. D. - Sachs, J. R.
Leveraging immunogenicity data and logistic regression for detection of covariate effects on vaccine efficacy.
PAGE 2022 Abstracts. Ljubljana: PAGE, 2022.
[PAGE 2022: Population Approach Group Europe Meeting /30./. 28.06.2022-01.07.2022, Ljubljana]
Institucionální podpora: RVO:67985807
Klíčová slova: infectious diseases * vaccine efficacy * immunogenicity * probability of disease
Obor OECD: Infectious Diseases
https://www.page-meeting.org/default.asp?abstract=10095

ZÁKLADNÍ ÚDAJE: PAGE 2022 Abstracts. Ljubljana: PAGE, 2022. [PAGE 2022: Population Approach Group Europe Meeting /30./. 28.06.2022-01.07.2022, Ljubljana]. ABSTRAKT: This work introduces a novel effort to use immune response biomarkers to help identify covariates affecting vaccine efficacy (VE). VE is defined as a proportional reduction in risk of disease for vaccinated subjects compared to control subjects and is often assessed by counting disease cases and non-cases in randomized controlled clinical trials [1]. VE can be affected by “covariates” (demographic characteristics of enrolled subjects), e.g., age or gender. Statistical significance of covariate effects on the binary clinical outcome (diseased versus non-diseased) is typically evaluated by logistic regression. In most efficacy trials, immune response post vaccination (immunogenicity) is measured in addition to the primary clinical endpoint. An immunogenicity biomarker that reliably predicts protection is a correlate of protection (CoP) [2]. It has been shown that CoP-based VE prediction is more precise than the case-count-based VE estimate [3]. Several approaches have been proposed to model the relationship between immunogenicity and probability of disease (PoD) [3-5].
Trvalý link: https://hdl.handle.net/11104/0346317