Finite-Sample Simulation Study for Competing Risks

Finite-Sample Simulation Study for Competing Risks

Usage

bd_simulation_competing(
  n = c(200, 400),
  n_sim = 100,
  pars = list(c(b = 2.5, c = 1.8, k = 0.04), c(b = 3.5, c = 1.2, k = 0.02)),
  gammas = NULL,
  censor_rate = 0.15,
  submodel = TRUE,
  n_starts = 2,
  level = 0.95,
  seed = NULL,
  quiet = FALSE
)

Arguments

n

Vector of sample sizes.

n_sim

Number of replicates at each sample size.

pars

List of true parameter vectors, one per cause.

gammas

Optional accelerated failure time coefficients, one per cause.

censor_rate

Approximate proportion of right-censored observations.

submodel

Logical; fit Exponentiated Danish cause-specific kernels.

n_starts

Starting points per fit.

level

Nominal coverage level.

seed

Optional integer seed.

quiet

Logical; suppress progress messages.

Value

An object of class `"bd_simulation"`, with `parameter` naming the cause and quantity, for example `c1:b`.

See also

[bd_simulation_study()], [fit_bd_competing()]

Examples

# \donttest{
s <- bd_simulation_competing(n = 120, n_sim = 2, n_starts = 1,
                             seed = 1, quiet = TRUE)
s
#> 
#> Beta-Danish simulation study: competing risks
#>   replicates: 2   sample sizes: 120
#>   nominal coverage: 95%
#> 
#>    n parameter truth    mean     bias    rmse se_ratio coverage n_fail
#>  120      c1:b  2.50 7.16677  4.66677 4.66677       NA        1      1
#>  120      c1:c  1.80 2.38974  0.58974 0.58974       NA        1      1
#>  120      c1:k  0.04 0.02877 -0.01123 0.01123       NA        1      1
#>  120      c2:b  3.50 1.97462 -1.52538 1.52538       NA        1      1
#>  120      c2:c  1.20 2.37215  1.17215 1.17215       NA        1      1
#>  120      c2:k  0.02 0.09135  0.07135 0.07135       NA        1      1
#> 
#>   Some replicates found no admissible optimum. A high count means
#>   the parameter region is hard to estimate at that sample size.
#> 
# }