Validate an Optimal Cut-point Using Bootstrapping

Assesses cut-point stability from find_cutpoint via bootstrap analysis, generating 95% confidence intervals. Streamlined for survival (time-to-event) analysis.

Usage

validate_cutpoint(
  cutpoint_result,
  num_replicates = 500,
  n_cores = 1,
  seed = NULL,
  nmin = NULL,
  ...
)

Arguments

cutpoint_result

An object from find_cutpoint.

num_replicates

Number of bootstrap replicates. Default is 500.

n_cores

Number of CPU cores to use. Default is 1 (sequential). Set to > 1 to enable parallel processing.

seed

Optional integer for reproducible results.

nmin

Minimum group size for bootstrap runs. Defaults to 90% of original nmin to reduce failures.

...

Additional arguments passed to find_cutpoint (e.g., pop.size, max.generations for genetic algorithm).

Value

An object of class validate_cutpoint_result with original cuts, 95% CIs, bootstrap distribution, and parameters.

srrstats compliance

.

Examples

if (FALSE) { # \dontrun{
if (requireNamespace("survival", quietly = TRUE)) {
  library(survival)

  # 1. Create a tiny simulated baseline clinical cohort dataset
  set.seed(123)
  n <- 45
  toy_data <- data.frame(
    time = rexp(n, rate = 0.05),
    event = sample(c(0, 1), n, replace = TRUE, prob = c(0.4, 0.6)),
    marker = rnorm(n, mean = 4, sd = 1.2)
  )

  # 2. Locate initial baseline cut-points via systematic search
  initial_cut <- find_cutpoint(
    data = toy_data, predictor = "marker",
    outcome_time = "time", outcome_event = "event",
    num_cuts = 1, method = "systematic", criterion = "logrank", nmin = 10
  )

  # 3. Run a lightweight bootstrap validation stress-test execution loop
  val_res <- validate_cutpoint(
    cutpoint_result = initial_cut,
    num_replicates = 25, # Small iteration tier for rapid check verification
    n_cores = 1,
    seed = 123
  )

  # 4. Invoke structural S3 output verification hooks
  print(val_res)
  summary(val_res)
  plot(val_res)
}
} # }