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Generate Sample Survival Data

Source: R/kaplan-meier.R
sample_survival_data.Rd

Simulates exponential survival times with administrative censoring at study_years. The default hazard_rate = 0.05 yields roughly 63\

Usage

sample_survival_data(
  n = 500,
  hazard_rate = 0.05,
  strata_levels = NULL,
  hazard_ratios = NULL,
  study_years = 20,
  seed = 42
)

Arguments

n

Number of observations. Must be a positive integer. Defaults to 500.

hazard_rate

Base annual hazard rate (exponential distribution parameter). Defaults to 0.05.

strata_levels

Optional character vector of stratum labels (e.g. c("Type A", "Type B")). When NULL (the default) a single unstratified cohort is generated.

hazard_ratios

Numeric vector of hazard multipliers — one per element of strata_levels — relative to hazard_rate. Defaults to all 1 (equal hazard across strata). Ignored when strata_levels is NULL.

study_years

Length of administrative follow-up in years. Subjects event-free beyond this point are censored. Defaults to 20.

seed

Integer random seed for reproducibility. Defaults to 42.

Value

A data frame with columns:

iv_dead

Follow-up time in years (numeric), truncated at study_years.

dead

Logical event indicator; TRUE if the subject experienced the event before administrative censoring.

iv_opyrs

Operation year offset — uniform over [1990, 1990 + study_years].

age_at_op

Age at operation (years); drawn from \(N(65, 10)\), capped to [30, 90].

valve_type

(Only when strata_levels is not NULL) Character stratum label.

Examples

# Unstratified
dta <- sample_survival_data(n = 500, seed = 42)
head(dta)
#>     iv_dead  dead iv_opyrs age_at_op
#> 1  3.966736  TRUE 2003.503  58.08251
#> 2 13.217905  TRUE 2008.716  65.37466
#> 3  5.669821  TRUE 1990.072  58.80676
#> 4  0.763838  TRUE 2005.739  66.45205
#> 5  9.463533  TRUE 2006.139  75.84440
#> 6 20.000000 FALSE 1991.461  60.21944

# Stratified with differential hazard
dta_s <- sample_survival_data(
  n             = 500,
  strata_levels = c("Type A", "Type B"),
  hazard_ratios = c(1, 1.4),
  seed          = 42
)
table(dta_s$valve_type)
#> 
#> Type A Type B 
#>    250    250