vaccineff: Tools for estimating vaccine effectiveness and vaccine related metrics 
vaccineff is developed at Pontificia Universidad Javeriana as part of the Epiverse-TRACE initiative.
vaccineff is an R package that offers tools for estimating vaccine effectiveness (VE), using a series of epidemiological designs including cohort studies, test-negative case-control, and screening methods (Halloran, Longini, and Struchiner 2010). The current version of the package provides a set of features for preparing, visualizing, and managing cohort data, estimating vaccine effectiveness, and assessing the performance of the models. Test-negative design and screening method will be included in future versions.
Installation
Our stable versions are released on CRAN, and can be installed using:
install.packages("vaccineff", build_vignettes = TRUE)The current development version of vaccineff can be installed from GitHub using the pak package.
if(!require("pak")) install.packages("pak")
pak::pak("epiverse-trace/vaccineff")Or using the remotes package
if(!require("remotes")) install.packages("remotes")
remotes::install_github("epiverse-trace/vaccineff")Quick start
vaccineff provides a minimal cohort dataset that can be used to test out the models.
# Load example `cohortdata` included in the package
data("cohortdata")
head(cohortdata, 5)
#> id sex age death_date death_other_causes vaccine_date_1 vaccine_date_2
#> 1 04edf85a M 50 <NA> <NA> <NA> <NA>
#> 2 c5a83f56 M 66 <NA> <NA> <NA> <NA>
#> 3 82991731 M 81 <NA> <NA> <NA> <NA>
#> 4 afbab268 M 74 <NA> <NA> 2021-03-30 2021-05-16
#> 5 3faf2474 M 54 <NA> <NA> 2021-06-01 2021-06-22
#> vaccine_1 vaccine_2
#> 1 <NA> <NA>
#> 2 <NA> <NA>
#> 3 <NA> <NA>
#> 4 BRAND2 BRAND2
#> 5 BRAND1 BRAND1The function make_vaccineff_data allows defining different aspects of the study design—such as vaccination dates, immunization delays, and potential confounding factors—and creates an object of class vaccineff_data. Its output is used to estimate VE using a Cox model regression invoked by the function estimate_vaccineff, which returns the object vaccineff.
The proportional hazard assumption can be tested both through the Schoenfeld test and visually using the plot method by setting type = "loglog".
# Create `vaccineff_data`
vaccineff_data <- make_vaccineff_data(
data_set = cohortdata,
outcome_date_col = "death_date",
censoring_date_col = "death_other_causes",
vacc_date_col = "vaccine_date_2",
vaccinated_status = "v",
unvaccinated_status = "u",
immunization_delay = 15,
end_cohort = as.Date("2021-12-31"),
match = TRUE,
exact = "sex",
nearest = c(age = 1)
)
# Estimate VE
ve <- estimate_vaccineff(vaccineff_data, at = 180)
# Print summary of VE
summary(ve)
#> Vaccine Effectiveness at 180 days computed as VE = 1 - HR:
#> VE lower.95 upper.95
#> 0.7254 0.5437 0.8347
#>
#> Schoenfeld test for Proportional Hazards assumption:
#> p-value = 0.1507
# Generate loglog plot to check proportional hazards
plot(ve, type = "loglog")
Package vignettes
More details on how to use vaccineff can be found in the online documentation as package vignettes, and in the articles “Get Started with vaccineff” and “Introduction to cohort design with vaccineff”.
Help
To report a bug or to request a new feature please open an issue.
Contribute
Contributions to vaccineff are welcomed. Contributions are welcome via pull requests.
Contributors to the project include:
Authors: David Santiago Quevedo and Zulma M. Cucunubá (maintainer)
Contributors: Geraldine Gómez, Pratik Gupte, Érika J. Cantor, Santiago Loaiza, Jaime A. Pavlich-Mariscal, Hugo Gruson, Chris Hartgerink, Felipe Segundo Abril-Bermúdez, Joshua W. Lambert, Julian Otero
Code of conduct
Please note that the vaccineff project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.