Description
Vaccines Pharmacometrics.
Description
Estimate vaccine efficacy (VE) using immunogenicity data. The inclusion of immunogenicity data in regression models can increase precision in VE. The methods are described in the publications "Elucidating vaccine efficacy using a correlate of protection, demographics, and logistic regression" and "Improving precision of vaccine efficacy evaluation using immune correlate data in time-to-event models" by Julie Dudasova, Zdenek Valenta, and Jeffrey R. Sachs (2024).
README.md
vaxpmx
The goal of vaxpmx is to provide the functions for pharmacometric modeling in vaccines, specifically analyses related to correlates of protection (CoPs) and vaccine efficacy/effectiveness predictions using a CoP.
Installation
You can install the released version of vaxpmx from CRAN with:
install.packages("vaxpmx")
Example
This is a basic example which shows you how to solve a common problem:
library(vaxpmx)
library(survival)
# Load an example dataset
data(data_temp)
# Fit logistic model relating neutralizing titer to disease status
logisticFit <- glm(disease_any ~ nAb1, data = data_temp, family = binomial())
# Fit Cox proportional hazards model relating neutralizing titer to time to disease or end of follow-up
# coxFit <- coxph(Surv(time_event, disease_any) ~ nAb1, data = data_temp)
# Estimate vaccine efficacy and 95\% confidence interval based on the fitted models
ve(logisticFit, data_temp, nboot = 500)
ve(coxFit, data_temp, nboot = 500)