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Simulate the 3+3 Design

Source: R/sim_3p3.R
sim_3p3.Rd

Simulate the traditional 3+3 dose-finding design and summarize the same quantities that oc_3p3 computes exactly. The two are meant to be used together: oc_3p3() gives the answer without Monte Carlo error, and this function confirms it from independent trials.

Usage

sim_3p3(
  p_true,
  n_trials = 10000,
  mtd_rule = c("previous", "expand"),
  start_dose = 1,
  overdose_cutoff = NULL,
  seed = 123
)

Arguments

p_true

Numeric vector. True DLT probability at each dose level, in increasing dose order.

n_trials

Integer scalar. Number of trials to simulate. Defaults to 10000.

mtd_rule

Character scalar, either "previous" or "expand". See oc_3p3. Defaults to "previous".

start_dose

Integer scalar. Dose level for the first cohort. Defaults to 1.

overdose_cutoff

Numeric scalar or NULL. Doses whose true DLT probability exceeds this value count as overdoses in the overdose component of the result. Defaults to NULL, which uses one third.

seed

Integer scalar or NULL. Random seed. The state of the calling session is restored on exit. Defaults to 123.

Value

An object of class oc_3p3, with the components described in oc_3p3. The method component is the number of trials rather than "exact".

Details

The design is described under oc_3p3. Since the operating characteristics of the 3+3 design can be written in closed form, this function is a check on that closed form rather than the way to obtain the numbers. Simulation is the only option when a quantity is wanted that the closed form does not provide, such as the distribution of the sample size rather than its mean.

See also

oc_3p3, sim_boin

Examples

exact <- oc_3p3(p_true = c(0.30, 0.48, 0.67))
simulated <- sim_3p3(p_true = c(0.30, 0.48, 0.67), n_trials = 2000, seed = 1)

round(exact$sel_percent, 2)
#> [1] 39.77  9.23  0.42
round(simulated$sel_percent, 2)
#> [1] 40.30  9.60  0.55