Run sim_boin under a list of dose-toxicity scenarios that share
the same design, and collect the results into one table.
Usage
sim_boin_multi(
target,
scenarios,
n_cohort,
cohort_size,
n_trials = 10000,
start_dose = 1,
n_earlystop = 18,
p_saf = NULL,
p_tox = NULL,
cutoff_eli = 0.95,
extrasafe = FALSE,
offset = 0.05,
titration = FALSE,
stay_on_1_of_3 = FALSE,
bound_mtd = FALSE,
mtd_max_estimate = NULL,
min_mtd_sample = 1,
overdose_cutoff = NULL,
n_earlystop_rule = c("with_stay", "simple"),
keep_trials = FALSE,
verbose = FALSE,
seed = 123
)Arguments
- target
Numeric scalar. Target DLT probability.
- scenarios
A list of scenarios. Each element is a list with a
nameand ap_truevector, or a plain numeric vector of true DLT probabilities in which case the list names are used as scenario names. Every scenario must have the same number of doses.- n_cohort
Integer scalar. Number of cohorts in a trial.
- cohort_size
Integer scalar or vector. Number of patients per cohort.
- n_trials
Integer scalar. Number of trials per scenario. Defaults to 10000.
- start_dose
Integer scalar. Dose level for the first cohort. Defaults to 1. It is ignored when
titrationisTRUE.- n_earlystop
Integer scalar. Early stopping sample size at the current dose. Defaults to 18.
- p_saf
Numeric scalar. Highest DLT probability deemed subtherapeutic. Defaults to
0.6 * target.- p_tox
Numeric scalar. Lowest DLT probability deemed overly toxic. Defaults to
1.4 * target.- cutoff_eli
Numeric scalar. Posterior probability cutoff for dose elimination. Defaults to 0.95.
- extrasafe
Logical scalar. Apply the stricter safety stopping rule at the lowest dose.
- offset
Numeric scalar between 0 and 0.5. Relaxation of
cutoff_elifor the safety stopping rule. Defaults to 0.05.- titration
Logical scalar. Start with single patient cohorts until the first DLT.
- stay_on_1_of_3
Logical scalar. Make one DLT out of three patients a stay rather than a de-escalation. See
boin_boundary.- bound_mtd
Logical scalar. Bound the isotonic estimate at the selected MTD by the de-escalation boundary.
- mtd_max_estimate
Numeric scalar or
NULL. Largest isotonic estimate a dose may have and still be selected as the MTD. Seesim_boin.- min_mtd_sample
Integer scalar. Smallest number of patients for a dose to be eligible.
- overdose_cutoff
Numeric scalar or
NULL. Doses whose true DLT probability exceeds this value count as overdoses. Defaults toNULL, which usestarget.- n_earlystop_rule
Character scalar, either
"with_stay"or"simple".- keep_trials
Logical scalar. Keep the trial by trial data of every scenario.
- verbose
Logical scalar. Report progress while the simulations run.
- seed
Integer scalar or
NULL. Random seed, applied to every scenario, so that a scenario simulated here matches the same scenario simulated on its own withsim_boin.
Value
An object of class boin_oc_multi, which is a list with components
- results
Named list of
boin_ocobjects, one per scenario.- summary_table
Data frame collecting all scenarios.
- scenario_names
Character vector of scenario names.
- n_doses
Number of dose levels.
- call
The matched call.
Examples
scenarios <- list(
list(name = "MTD at dose 3", p_true = c(0.05, 0.15, 0.30, 0.45, 0.60)),
list(name = "All toxic", p_true = c(0.35, 0.45, 0.55, 0.65, 0.75))
)
oc <- sim_boin_multi(
target = 0.30,
scenarios = scenarios,
n_cohort = 10,
cohort_size = 3,
n_trials = 200,
seed = 123
)
oc
#> BOIN operating characteristics across 2 scenarios
#> target DLT rate : 0.3
#> trials each : 200
#> max sample size : 30
#>
#> Scenario Item DL1 DL2 DL3 DL4 DL5 Total / No MTD
#> MTD at dose 3 True DLT rate (%) 5.0 15.0 30.0 45.0 60.0
#> MTD selected (%) 1.0 26.0 52.0 21.0 0.0 0.0
#> Patients treated 4.2 9.1 10.6 4.5 0.6 29.1
#> Patients with DLT 0.2 1.4 3.2 2.1 0.4 7.3
#> All toxic True DLT rate (%) 35.0 45.0 55.0 65.0 75.0
#> MTD selected (%) 67.0 9.0 2.0 0.0 0.0 22.0
#> Patients treated 14.5 5.4 0.8 0.1 0.0 20.8
#> Patients with DLT 5.0 2.5 0.4 0.1 0.0 8.0