vaccineff

Cohort Design

In the cohort design, \(VE\) is estimated using the Hazard Ratios (\(HR\)) between vaccinated and unvaccinated populations,

\[VE = (1-HR(t))\times100.\]

The \(HR\) is estimated using the Cox Proportional Hazards model. In particular, we use the vaccine status of the individuals as the only covariate in the regression. Other confounders can be included as matching arguments to adjust for observational bias. The proportional hazards hypothesis is checked using the Schoenfeld test. A visual check is also provided using the log-log representation of the Survival Probability. If the hypothesis is not satisfied, it is recommended to stratify the population into smaller groups using the confounding variables.

Data for Cohort design

Data should be disaggregated at the individual level to track vaccinated and unvaccinated populations over time. The dataset must include the following information:

• Date(s) of vaccination for each individual: The package allows for multiple doses per individual and estimates the immunization date using delay times of outcomes and the timing of vaccine administration.

• Date(s) of outcome(s): The package estimates vaccine effectiveness against various outcomes.

• Date(s) of right censoring: The package allows for the inclusion of information on dates of events that constitute right censoring.

• Individuals’ demographic information (e.g., sex, age group, health status): These can be used as confounding variables to match the population and reduce observational bias.

An example dataset for a cohort design is included, with information on vaccination dates and biological details per dose, as well as relevant demographic information. The level of data aggregation is tailored to the characteristics and needs of the study case. To load this dataset, run the following code

# Load example data
data("cohortdata")
head(cohortdata)
#>         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
#> 6 97df7bdc   M  79       <NA>               <NA>     2021-03-21     2021-05-02
#>   vaccine_1 vaccine_2
#> 1      <NA>      <NA>
#> 2      <NA>      <NA>
#> 3      <NA>      <NA>
#> 4    BRAND2    BRAND2
#> 5    BRAND1    BRAND1
#> 6    BRAND2    BRAND2

VE for Cohort design

The current release of the package bases the estimation of \(VE\) in the cohort design on the assumption of proportional hazards between vaccinated and unvaccinated populations. The \(HR\) is estimated using the Cox proportional hazards model implemented in the R package {survival}.

The integrated dataset cohortdata serves as a minimal example of the package’s input. The data is accessed using data("cohortdata").

vaccineff has two main functions: 1. make_vaccineff_data: This function returns an S3 object of the class vaccineff_data with the relevant information for the study. This function also allows to create a matched cohort to control for confounding variables by setting match = TRUE and passing the corresponding exact and nearest arguments. make_vaccineff_data supports the methods summary() to check the characteristics of the cohort, the matching balance and the sizes of matched, excluded, and removed populations, and plot() to plot the cohort coverage.

2. estimate_vaccineff: This function provides methods for estimating VE using the \(HR\). A summary of the estimation can be obtained using summary() and a graphical representation of the methodology is generated by plot().

# 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 = c("age", "sex"),
  nearest = NULL
)

# Print summary of vaccineff data object
summary(vaccineff_data)
#> Cohort start:  2021-03-26
#> Cohort end:  2021-12-31
#> The start date of the cohort was defined as the mininimum immunization date. 
#> 65 registers were removed with outcomes before the start date.
#> 
#> Nearest neighbors matching iteratively performed.
#> Number of iterations:  4
#> Balance all:
#>               u         v         smd
#> age   63.917069 62.997438 -0.08593156
#> sex_F  0.520277  0.573474  0.10701746
#> sex_M  0.479723  0.426526 -0.10701746
#> 
#> Balance matched:
#>                u          v smd
#> age   63.7556091 63.7556091   0
#> sex_F  0.5216948  0.5216948   0
#> sex_M  0.4783052  0.4783052   0
#> 
#> Summary vaccination:
#>               u     v
#> All       10973 19905
#> Matched   10786 10786
#> Unmatched   187  9119
#> 
#> // tags: outcome_date_col:death_date, censoring_date_col:death_other_causes, vacc_date_col:vaccine_date_2, immunization_date_col:immunization_date, vacc_status_col:vaccine_status

# Plot the vaccine coverage of the total population
plot(vaccineff_data)

# Estimate the Vaccine Effectiveness at 90 days
ve90 <- estimate_vaccineff(vaccineff_data, at = 90)

# Print summary of VE
summary(ve90)
#> Vaccine Effectiveness at 90 days computed as VE = 1 - HR:
#>      VE lower.95 upper.95
#>  0.6787   0.4612   0.8084
#> 
#> Schoenfeld test for Proportional Hazards assumption:
#> p-value = 0.0234
#> Warning:
#> 
#> p-value < 0.05. Please check loglog plot for Proportional Hazards assumption

# Loglog plot to check proportional hazards
plot(ve90, type = "loglog")

For details on the estimation of VE in cohort studies see the vignette Introduction to cohort design with vaccineff