vaccineff 0.0.1

Features

vaccineff 0.0.1 provides functions for data wrangling and estimating vaccine effectiveness in a cohort design. A typical estimation pipeline in vaccineff 0.0.1 requires the explicit definition of the immunization dates, vaccine and outcome status, and time to events as follows:

data("cohortdata")

cohortdata$immunization <-
  get_immunization_date(
    data = cohortdata,
    outcome_date_col = "death_date",
    outcome_delay = 0,
    immunization_delay = 14,
    vacc_date_col = c("vaccine_date_1", "vaccine_date_2"),
    end_cohort = as.Date("2044-12-31"),
    take_first = FALSE
  )

cohortdata$vaccine_status <- set_status(
  data = cohortdata,
  col_names = "immunization",
  status = c("v", "u")
)

cohortdata$death_status <- set_status(
  data = cohortdata,
  col_names = "death_date"
)

cohortdata$time_to_death <- get_time_to_event(
  data = cohortdata,
  outcome_date_col = "death_date",
  start_cohort = as.Date("2044-01-01"),
  end_cohort = as.Date("2044-12-31"),
  start_from_immunization = FALSE
)

After defining the previous parameters, vaccine effectiveness is calculated using the function coh_eff_noconf()} , which relies on the implementation of the Cox Model for Proportional Hazards from the {survival} package.

coh_eff_noconf(
  data = cohortdata,
  outcome_status_col = "death_status",
  time_to_event_col = "time_to_death",
  status_vacc_col = "vaccine_status"
)

To assess the Proportional Hazards Hypothesis, vaccineff 0.0.1 uses the p-value of the Schoenfeld test, which is reported in the output of coh_eff_noconf(). Alternative strategies, such as stratifying the cohort, are suggested to satisfy this hypothesis. vaccineff 0.0.1 also provides the function match_cohort() to help users deal with observational bias. This feature is still under development to achieve stronger results.

Basic exploration and visualization can also be done using the functions plot_coverage() and plot_survival().