Abstract
Vaccine efficacy is often assessed by counting disease cases in a clinical trial. A new quantitative framework proposed here (“PoDBAY,” Probability of Disease Bayesian Analysis), estimates vaccine efficacy (and confidence interval) using immune response biomarker data collected shortly after vaccination. Given a biomarker associated with protection, PoDBAY describes the relationship between biomarker and probability of disease as a sigmoid probability of disease (“PoD”) curve. The PoDBAY framework is illustrated using clinical trial simulations and with data for influenza, zoster, and dengue virus vaccines. The simulations demonstrate that PoDBAY efficacy estimation (which integrates the PoD and biomarker data), can be accurate and more precise than the standard (case-count) estimation, contributing to more sensitive and specific decisions than threshold-based correlate of protection or case-count-based methods. For all three vaccine examples, the PoD fit indicates a substantial association between the biomarkers and protection, and efficacy estimated by PoDBAY from relatively little immunogenicity data is predictive of the standard estimate of efficacy, demonstrating how PoDBAY can provide early assessments of vaccine efficacy. Methods like PoDBAY can help accelerate and economize vaccine development using an immunological predictor of protection. For example, in the current effort against the COVID-19 pandemic it might provide information to help prioritize (rank) candidates both earlier in a trial and earlier in development.
Highlights
The protective efficacy of a vaccine is defined as the proportional reduction in risk of disease among vaccinated subjects compared to control subjects and is often assessed in randomized double-blinded controlled clinical trials[1]
Other authors[22] have provided an excellent overview of approaches to Overview of results PoDBAY relates the probability of disease (“PoD”) to a correlate of protection (CoP) via a decreasing sigmoid surrogate markers and proposed optimizing curve shape using function
This section first shows the properties of PoDBAY for simulated data using a range of trial sizes, VEs, and CoP properties
Summary
The protective efficacy of a vaccine (vaccine efficacy, “VE”) is defined as the proportional reduction in risk of disease among vaccinated subjects compared to control (placebo vaccinated) subjects and is often assessed in randomized double-blinded controlled clinical trials[1]. The term “correlate of protection,” or “CoP,” is used in accordance with the terminology of Plotkin and Gilbert[6], in which a CoP is a biomarker that can be used to reliably predict VE. This was chosen because the biomarker used in the proposed method can be a mechanistic CoP, a non-mechanistic CoP, an absolute CoP, or a relative CoP. Dunning[16] proposed to use the logistic function of log biomarker value to model the protection curve He provided a formula to calculate VE using the protection curve and biomarker values of individuals in vaccinated and control groups. Other authors[22] have provided an excellent overview of approaches to
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