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.
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")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 afade1b2 F 37 <NA> <NA> <NA> <NA>
#> 2 556c8c76 M 19 <NA> <NA> <NA> <NA>
#> 3 04edf85a M 50 <NA> <NA> <NA> <NA>
#> 4 7e51a18e F 8 <NA> <NA> <NA> <NA>
#> 5 c5a83f56 M 66 <NA> <NA> <NA> <NA>
#> vaccine_1 vaccine_2
#> 1 <NA> <NA>
#> 2 <NA> <NA>
#> 3 <NA> <NA>
#> 4 <NA> <NA>
#> 5 <NA> <NA>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`
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("2044-12-31"),
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
#> 0.8457 0.7429 0.9074
#>
#> Schoenfeld test for Proportional Hazards assumption:
#> p-value = 0.0046
#> Warning in print.summary_vaccineff(x):
#> p-value < 0.05. Please check loglog plot for Proportional Hazards assumption
# 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
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.
Halloran, Elizabeth, Ira Longini, and Claudio Struchiner. 2010. Design and Analysis of Vaccine Studies. Springer.