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.
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("epiverse-trace/vaccineff") pak
Or using the remotes
package
if(!require("remotes")) install.packages("remotes")
::install_github("epiverse-trace/vaccineff") remotes
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 BRAND1
The 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`
<- make_vaccineff_data(
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
<- estimate_vaccineff(vaccineff_data, at = 180)
ve
# 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")
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”.
To report a bug or to request a new feature please open an issue.
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
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.