#' Simulated cohort 1
#'
#' Survival outcome was simulated with true hazard:
#' \deqn{\log \{h(t)\} = \log \{h_0\} + \log(1.1)Age + \log(2)Gender.}
#'
#' @format A data frame with 10000 rows and 5 variables:
#' \describe{
#' \item{id}{Subject ID of each subject.}
#' \item{y}{Event/censoring indicator. Events are indicated by y=1.}
#' \item{t}{Event/censoring time (in years). Maximum follow-up time is 25 years.}
#' \item{age}{Age of subjects (rounded to integers).}
#' \item{gender}{Gender of subjects (1 for male and 0 for female).}
#' }
#'
#' @examples
#' library(survival)
#' data("cohort_1", package = "SamplingDesignTools")
#' summary(coxph(Surv(t, y) ~ age + gender, data = cohort_1))
#'
"cohort_1"
#' Nested case-control (NCC) data 1
#'
#' Time-matched data drawn from \code{\link{cohort_1}}, with one control
#' matched to each case.
#'
#' @format A data frame with 1164 rows and 6 variables:
#' \describe{
#' \item{Set}{Set ID.}
#' \item{Map}{Row numbers in \code{cohort_1}.}
#' \item{Time}{Event time (in years) of the case in each \code{Set}.}
#' \item{Fail}{Case-control indicator.}
#' \item{age}{Age of subjects (rounded to integers).}
#' \item{gender}{Gender of subjects (1 for male and 0 for female).}
#' }
"ncc_1"
#' Simulated cohort 2
#'
#' Survival outcome was simulated with true hazard:
#' \deqn{\log \{h(t)\} = \log \{h_0\}
#' + \log(1.5)x + \log(4)z + \log(2)xz + \log(1.01)Gender + \log(1.01)Age.}
#'
#' @format A data frame with 100000 rows and 8 variables:
#' \describe{
#' \item{id}{Subject ID of each subject.}
#' \item{y}{Event/censoring indicator. Events are indicated by y=1.}
#' \item{t}{Event/censoring time (in years). Maximum follow-up time is 25 years.}
#' \item{x}{Binary exposure.}
#' \item{age}{Age of subjects (rounded to integers and mean-centred).}
#' \item{age_cat}{Age category of subjects.}
#' \item{gender}{Gender of subjects (1 for male and 0 for female).}
#' \item{z}{Binary effect modifier.}
#' }
#'
#' @examples
#' library(survival)
#' data("cohort_2", package = "SamplingDesignTools")
#' summary(coxph(Surv(t, y) ~ x * z + age + gender, data = cohort_2))
#'
"cohort_2"
#' Nested case-control (NCC) data 2
#'
#' @description NCC data drawn from \code{\link{cohort_2}} matched on age group
#' and gender, with five controls matched to each case.
#'
#' @format A data frame with 16638 rows and 10 variables:
#' \describe{
#' \item{Set}{Set ID.}
#' \item{Map}{Row numbers in \code{cohort_2}.}
#' \item{Time}{Event time (in years) of the case in each \code{Set}.}
#' \item{Fail}{Case-control indicator.}
#' \item{age_cat}{Age category of subjects.}
#' \item{gender}{Gender of subjects (1 for male and 0 for female).}
#' \item{x}{Binary exposure.}
#' \item{age}{Age of subjects (rounded to integers).}
#' \item{z}{Binary effect modifier.}
#' \item{t}{Actual time/censoring (in years) of each subject.}
#' }
"ncc_2"
#' Number of subjects at risk at each time point in \code{ncc_2}
#'
#' @format A data frame with 2773 rows and 4 variables:
#' \describe{
#' \item{Time}{Event time (in years) of cases in \code{ncc_2}.}
#' \item{gender}{Gender of subjects (1 for male and 0 for female).}
#' \item{age_cat}{Age category of subjects.}
#' \item{n.risk}{Number of subjects at risk at each event time in each gender-age strata.}
#' }
#' @details Note how column names in \code{n_at_risk} match those in
#' \code{ncc_2}. This is critical for computing the KM-type weights for
#' subjects in \code{ncc_2}.
"n_at_risk"
#' Simulated mini cohorts
#'
#' @description Mini cohorts with common and staggered entry time.
#'
#' @format A data frame with 10 rows and the following variables:
#' \describe{
#' \item{id}{Subject ID.}
#' \item{t}{Event/censoring time, with follow-up starting at time 0.}
#' \item{t_start}{Start time of follow-up.}
#' \item{t_end}{End time of follow-up.}
#' \item{status}{Censoring status.}
#' }
#'
#' @name mini_cohorts
"mini_cohort"
#' @rdname mini_cohorts
"mini_cohort2"
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