Improving precision of vaccine efficacy evaluation using immune correlate data in time-to-event models.

Understanding potential differences in vaccine-induced protection between demographic subgroups is key for vaccine development. Vaccine efficacy evaluation across these subgroups in phase 2b or 3 clinical trials presents challenges due to lack of precision: such trials are typically designed to demonstrate overall efficacy rather than to differentiate its value between subgroups. This study proposes a method for estimating vaccine efficacy using immunogenicity (instead of vaccination status) as a predictor in time-to-event models.

Elucidating vaccine efficacy using a correlate of protection, demographics, and logistic regression.

Background: Vaccine efficacy (VE) assessed in a randomized controlled clinical trial can be affected by demographic, clinical, and other subject-specific characteristics evaluated as baseline covariates. Understanding the effect of covariates on efficacy is key to decisions by vaccine developers and public health authorities.

Methods: This work evaluates the impact of including correlate of protection (CoP) data in logistic regression on its performance in identifying statistically and clinically significant covariates in settings typical for a vaccine phase 3 trial.

Replicate Testing of Clinical Endpoints Can Prevent No-Go Decisions for Beneficial Vaccines.

In vaccine efficacy trials, inaccurate counting of infection cases leads to systematic under-estimation-or "dilution"-of vaccine efficacy. In particular, if a sufficient fraction of observed cases are false positives, apparent efficacy will be greatly reduced, leading to unwarranted no-go decisions in vaccine development. Here, we propose a range of replicate testing strategies to address this problem, considering the additional challenge of uncertainty in both infection incidence and diagnostic assay specificity/sensitivity.

A method to estimate probability of disease and vaccine efficacy from clinical trial immunogenicity data.

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.