Estimate Vaccine Effectiveness (VE)

This function provides methods for estimating VE. It relies on the Kaplan-Meier estimator and the Cox model for proportional hazards from the {survival} package. Currently, the default method is VE = 1 - HR, where HR is the Hazard Ratio calculated using the Cox model. The proportional hazards assumption is tested using the Schoenfeld test, with the p-value provided in the results. Log-log plots are also generated using the Kaplan-Meier estimator for a visual test of the proportional hazards hypothesis. The function uses column names provided in the tags outcome_status_col, time_to_event_col, and vaccine_status_col of the linelist object and status names from make_vaccineff_data. The return is an S3 class object with the VE (CI95%), results from the Cox model, and the Kaplan-Meier estimator. This object is compatible with summary and plot methods.

Usage

estimate_vaccineff(vaccineff_data, at)

Arguments

vaccineff_data

Object of the class vaccineff_data with vaccineff data.

at

Number of days at which VE is estimated from the beginning of the follow-up period.

Value

Object of the class vaccineff: a list with results from the estimation of VE. ve: data.frame with VE(CI95%) cox_model: survival object with Cox model results kaplan_meier: survival object with Kaplan-Meier estimator

Examples

# \donttest{
# Load example data
data("cohortdata")

# 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
)

# Estimate the Vaccine Effectiveness (VE)
ve <- estimate_vaccineff(vaccineff_data, 90)

# Print summary of VE
summary(ve)
#> Vaccine Effectiveness at 90 days computed as VE = 1 - HR:
#>      VE lower.95 upper.95
#>  0.6943   0.4889   0.8172
#> 
#> Schoenfeld test for Proportional Hazards assumption:
#> p-value = 0.027
#> Warning:
#> 
#> p-value < 0.05. Please check loglog plot for Proportional Hazards assumption

# Generate loglog plot to check proportional hazards
plot(ve, type = "loglog")


# Generate Survival plot
plot(ve, type = "surv", percentage = FALSE, cumulative = FALSE)

# }