Run a series of simulated trials according to a specified design

Usage

sim_trial(
  design,
  runs1,
  runs2,
  adapt_rule,
  data_gen_1,
  data_gen_2,
  include_designs = FALSE
)

Arguments

design: cer_design or multiarm_design object to be used for the simulation
runs1: Number of trials to run in the first stage
runs2: Number of second stage to run for every first stage trial
adapt_rule: function that takes a cer_design object after the first interim test and returns the same object with appropiate adaptions
data_gen_1: function for generating first stage data, see details
data_gen_2: function for generating second stage data, see details
include_designs: boolean indicating whether to include the designs objects in the output

Value

A dataframe containing the various results

Details

data_gen_1 and data_gen_2 should take two arguments, the number of trials to simulate and the adapted design object, and return a matrix or dataframe with each row being the p-values for a single trial.

See also get_data_gen() and get_data_gen_2() for further information on how the input and output of data generating functions should look.

Examples

as <- function(x,t) 2-2*pnorm(qnorm(1-x/2)/sqrt(t))
design <- multiarm_cer_design(
 controls = 1,
 treatment_assoc = c(1,1),
 n_controls = 50,
 n_treatments = 50,
 weights = c(0.5, 0.5),
 alpha = 0.05,
 test_m = rbind(c(0, 1),
              c(1, 0)),
 alpha_spending_f = as,
 t = 0.5)

adaption <- function(design) {
  design |> multiarm_drop_arms(1)
}

data_gen <- get_data_gen(
  matrix(1),
  rbind(
   c(1, 0.5),
   c(0.5, 1)
 ),
  c(0, 0.3),
  100,
  100
)

data_gen_2 <- get_data_gen_2(
  matrix(1),
  rbind(
   c(1, 0.5),
   c(0.5, 1)
  ),
  c(0, 0)
)

sim_trial(
  design,
  5,
  3,
  adaption,
  data_gen,
  data_gen_2
)
#>    run1 rej_1 rej_2 rej_any p_1         p_2 interim_rej_1 interim_rej_2
#> 1     1 FALSE FALSE   FALSE   1 0.961581340         FALSE         FALSE
#> 2     1 FALSE FALSE   FALSE   1 0.338177340         FALSE         FALSE
#> 3     1 FALSE FALSE   FALSE   1 0.633886523         FALSE         FALSE
#> 4     2 FALSE  TRUE    TRUE   1 0.020024391         FALSE         FALSE
#> 5     2 FALSE  TRUE    TRUE   1 0.005126956         FALSE         FALSE
#> 6     2 FALSE FALSE   FALSE   1 0.242792223         FALSE         FALSE
#> 7     3 FALSE FALSE   FALSE   1 0.380815234         FALSE         FALSE
#> 8     3 FALSE FALSE   FALSE   1 0.035221224         FALSE         FALSE
#> 9     3 FALSE FALSE   FALSE   1 0.726402738         FALSE         FALSE
#> 10    4 FALSE FALSE   FALSE   1 0.274957013         FALSE         FALSE
#> 11    4 FALSE FALSE   FALSE   1 0.054485800         FALSE         FALSE
#> 12    4 FALSE  TRUE    TRUE   1 0.011712328         FALSE         FALSE
#> 13    5 FALSE FALSE   FALSE   1 0.047619141         FALSE         FALSE
#> 14    5 FALSE FALSE   FALSE   1 0.272722814         FALSE         FALSE
#> 15    5 FALSE FALSE   FALSE   1 0.098743385         FALSE         FALSE
#>    interim_p_1 interim_p_2
#> 1    0.9883835  0.58079009
#> 2    0.9883835  0.58079009
#> 3    0.9883835  0.58079009
#> 4    0.2565430  0.00480112
#> 5    0.2565430  0.00480112
#> 6    0.2565430  0.00480112
#> 7    0.9918850  0.39739172
#> 8    0.9918850  0.39739172
#> 9    0.9918850  0.39739172
#> 10   0.3274491  0.02894867
#> 11   0.3274491  0.02894867
#> 12   0.3274491  0.02894867
#> 13   0.6145201  0.04164344
#> 14   0.6145201  0.04164344
#> 15   0.6145201  0.04164344