Bootstrap resampling for hazard model coefficients

Resample data with replacement, refit the hazard model on each replicate, and accumulate coefficient distributions. Returns a tidy data frame of per-replicate estimates with summary statistics. This is the R equivalent of the SAS bootstrap.hazard.sas macro.

Usage

hzr_bootstrap(
  object,
  n_boot = 200L,
  fraction = 1,
  seed = NULL,
  verbose = FALSE,
  scope = NULL,
  direction = c("both", "forward", "backward"),
  criterion = c("wald", "aic"),
  slentry = 0.3,
  slstay = 0.2,
  max_steps = 50L,
  max_move = 4L,
  force_in = character(),
  force_out = character(),
  ...
)

# S3 method for class 'hzr_bootstrap'
print(x, digits = 4, ...)

Arguments

object: A fitted hazard object (with fit = TRUE).
n_boot: Integer: number of bootstrap replicates (default 200).
fraction: Numeric in (0, 1]: fraction of data to sample per replicate (default 1.0 for full bootstrap; < 1 for bagging).
seed: Optional integer random seed for reproducibility. When supplied, set.seed(seed) is called at function entry, jumping the global RNG to the seeded state; it is not restored on exit. Pass NULL (the default) to skip the set.seed() call and start from the caller's current RNG state. Note that the bootstrap consumes random numbers either way, so the global RNG state will advance during the call – seed = NULL avoids the reset at entry, not the advance during resampling.
verbose: Logical; if TRUE, print progress every 50 replicates.
scope: Candidate variable scope for embedded stepwise selection during each bootstrap replicate. NULL (default) preserves the original fixed-formula bootstrap: every replicate refits object's exact model, and summary$pct is always ~100. When supplied (a one-sided formula, character vector, or – for multiphase fits – a named list of one-sided formulas keyed by phase, matching hzr_stepwise()'s scope), each replicate runs a fresh hzr_stepwise() selection instead; see Details.
direction, criterion, slentry, slstay, max_steps, max_move, force_in, force_out: Passed through to hzr_stepwise() on each replicate when scope is supplied; ignored when scope = NULL. See hzr_stepwise() for definitions and defaults.
...: Additional arguments forwarded to hzr_stepwise() (e.g. control = list(n_starts = 1)) when scope is supplied; ignored otherwise.
x: An hzr_bootstrap object.
digits: Number of decimal places for formatting.

Value

A list with class "hzr_bootstrap" containing:

replicates: Data frame with columns replicate, parameter, and estimate – one row per parameter per successful replicate.
summary: Data frame with columns parameter, n, pct, mean, sd, min, max, ci_lower, ci_upper – one row per parameter. In mode = "select", pct is the selection frequency and the other statistics are conditional on selection.
n_success: Number of successfully converged replicates.
n_failed: Number of replicates that failed to converge.
mode: "refit" (fixed-formula bootstrap) or "select" (embedded stepwise selection).
scope: Only present when mode == "select": the candidate scope used.

Details

When scope is supplied, each replicate instead runs a fresh hzr_stepwise() selection on the resampled data (starting from a fixed-shape refit of object) instead of refitting object's exact formula. This is the R equivalent of the SAS %HAZBOOT macro: fit the hazard shape with no covariates (fixing it via hzr_phase(..., fixed = "shapes")), then bootstrap-screen candidate covariates for how often they enter the model. summary$pct then reports the selection frequency across replicates, and summary$mean/sd/ci_* describe the coefficient distribution conditional on selection.

Examples

# \donttest{
data(avc)
avc <- na.omit(avc)
fit <- hazard(
  survival::Surv(int_dead, dead) ~ age + mal,
  data  = avc,
  dist  = "weibull",
  theta = c(mu = 0.01, nu = 0.5, 0, 0),
  fit   = TRUE
)
bs <- hzr_bootstrap(fit, n_boot = 50, seed = 123)
print(bs)
#> Bootstrap inference for hazard model
#> Mode: fixed refit 
#> Replicates: 50 successful, 0 failed
#> 
#>  parameter  n pct    mean     sd     min     max ci_lower ci_upper
#>         mu 50 100  0.0004 0.0004  0.0000  0.0023   0.0000   0.0013
#>         nu 50 100  0.2219 0.0144  0.1963  0.2618   0.1986   0.2462
#>        age 50 100 -0.0062 0.0028 -0.0129 -0.0022  -0.0125  -0.0029
#>        mal 50 100  0.8464 0.2701  0.2673  1.3909   0.4337   1.3471

# Embedded stepwise selection: screen candidate covariates for how
# often they enter the model across resamples (R equivalent of SAS
# %HAZBOOT).
base <- hazard(
  survival::Surv(int_dead, dead) ~ 1,
  data  = avc,
  dist  = "weibull",
  theta = c(mu = 0.01, nu = 0.5),
  fit   = TRUE
)
bs_sel <- hzr_bootstrap(base, n_boot = 20, seed = 123,
                         scope = ~ age + mal,
                         slentry = 0.3, slstay = 0.2)
print(bs_sel)
#> Bootstrap inference for hazard model
#> Mode: embedded stepwise selection 
#> Replicates: 20 successful, 0 failed
#> 
#>  parameter  n pct    mean     sd     min     max ci_lower ci_upper
#>        age 20 100 -0.0068 0.0031 -0.0125 -0.0022  -0.0116  -0.0027
#>         mu 20 100  0.0007 0.0009  0.0001  0.0037   0.0001   0.0028
#>         nu 20 100  0.2249 0.0143  0.2040  0.2620   0.2061   0.2543
#>        mal 14  70  0.9749 0.2623  0.5514  1.3572   0.5734   1.3368
# }

Usage

Arguments

Value

Details

See also

Examples