Detection of covariate effects on vaccine efficacy using a correlate of protection and logistic regression

0577163 - ÚI 2024 RIV US eng A - Abstrakt
Dudášová, J. - Valenta, Zdeněk - Sachs, J. R.
Detection of covariate effects on vaccine efficacy using a correlate of protection and logistic regression.
ACoP14 Abstracts. National Harbor: ACoP, 2023.
[ACoP 2023: American Conference on Pharmacometrics /14./. 05.11.2023-08.11.2023, National Harbor]
Institucionální podpora: RVO:67985807
Klíčová slova: vaccine efficacy * correlate of protection * baseline covariates
Obor OECD: Infectious Diseases

ZÁKLADNÍ ÚDAJE: ACoP14 Abstracts. National Harbor: ACoP, 2023. [ACoP 2023: American Conference on Pharmacometrics /14./. 05.11.2023-08.11.2023, National Harbor]. ABSTRACT: OBJECTIVES: This work introduces a novel use of correlate of protection (CoP) data to help identify baseline covariates (demographic, clinical and other subject-specific characteristics) affecting vaccine efficacy (VE). A randomized controlled trial can be used to estimate VE even if the primary analysis does not consider baseline covariates because measured and unmeasured covariates will, on average, be balanced between the vaccinated and control groups due to randomization. However, VE may be affected by baseline covariates (for example, it can vary with age) and understanding the effect of covariates on efficacy is key to decisions by vaccine developers and public health authorities. METHODS: Clinical trial simulations (CTSs) are conducted to evaluate, in settings typical for a vaccine phase 3 trial, the impact of CoP data inclusion on logistic regression performance in identifying statistically and clinically significant covariates. The proposed approach uses CoP data and covariate data as predictors of clinical outcome (diseased versus non-diseased) and is compared to logistic regression (without CoP data) to relate vaccination status and covariate data to clinical outcome. RESULTS: CTSs, in which the true relationship between CoP data and clinical outcome probability is a (pre-specified and known) sigmoid function, show that use of CoP data increases the positive predictive value for detection of a covariate effect. If the true relationship is characterized by a decreasing convex function, use of CoP data does not substantially change positive or negative predictive value. In either scenario, vaccine efficacy is estimated accurately and more precisely (i.e., confidence intervals are narrower) in covariate-defined subgroups if CoP data are used, implying that using CoP data increases the ability to determine clinical significance of baseline covariate effects on efficacy. CONCLUSIONS: This study proposed and evaluated a novel approach for assessing baseline covariates potentially affecting VE. Results show that the proposed approach can sensitively and specifically identify potentially important covariates, and provides a method for evaluating their likely clinical significance in terms of predicted impact on vaccine efficacy. It shows further that inclusion of CoP data can enable more precise VE estimation, thus enhancing study power and/or efficiency and providing even better information to support health policy and development decisions.
Trvalý link: https://hdl.handle.net/11104/0346394