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Operating Characteristics of a BF-BOIN Design Across Several Scenarios

Source: R/sim_bf_boin_multi.R
sim_bf_boin_multi.Rd

Run sim_bf_boin under a list of scenarios of DLT and response probabilities that share the same design, and collect the results into one table.

Usage

sim_bf_boin_multi(
  target,
  scenarios,
  n_cohort,
  cohort_size,
  window,
  accrual_rate,
  n_cap = 12,
  backfill_dose = c("highest", "lowest"),
  conflict_dose = c("highest", "lowest"),
  no_slot = c("wait", "leave"),
  accrual = c("exponential", "uniform", "fixed"),
  dlt_time = c("weibull", "uniform"),
  late_fraction = 0.5,
  resp_window = window,
  resp_late_fraction = 0.5,
  resp_cor = 0,
  n_trials = 10000,
  start_dose = 1,
  n_earlystop = 18,
  p_saf = NULL,
  p_tox = NULL,
  cutoff_eli = 0.95,
  extrasafe = FALSE,
  offset = 0.05,
  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, for example 0.25.

scenarios

A list of scenarios. Each element is a list with a p_true vector of true DLT probabilities, a p_resp vector of true response probabilities of the same length and optionally a name; without a name the list names are used, or "Scenario 1", "Scenario 2" and so on. Every scenario must have the same number of doses.

n_cohort

Integer scalar. Number of cohorts in the dose escalation.

cohort_size

Integer scalar or vector. Number of patients per cohort of the dose escalation. A scalar is used for every cohort. A vector shorter than n_cohort is padded with its last element and a longer one is truncated.

window

Numeric scalar. Length of the DLT assessment window.

accrual_rate

Numeric scalar. Average number of patients arriving per unit of time, in the same unit as window.

n_cap

Integer scalar. A dose is no longer backfilled once this many patients, from the dose escalation and from backfilling together, have been treated at it. The dose escalation can still treat patients at the dose. Defaults to 12.

backfill_dose

Character scalar. Which dose receives a backfill patient when several are open: "highest" (the default, as in both articles) or "lowest".

conflict_dose

Character scalar. When the data of several backfilled doses conflict with those of the current dose, which of them anchors the pooled estimate: "highest" (the default here, following Zhao et al., 2024) or "lowest" (the default of be_boin_simulate, following Chen et al., 2026). See the details.

no_slot

Character scalar. What happens to a patient who arrives when neither the dose escalation nor any backfill dose can take a patient: "wait" (the default), the patient waits and is treated as soon as one can, as in tite_boin_simulate; or "leave", the patient is turned away and the trial waits for the next arrival.

accrual

Character scalar. Distribution of the times between arrivals: "exponential" (the default) for a Poisson process, "uniform" for a uniform distribution on (0, 2 / accrual_rate), or "fixed" for arrivals exactly 1 / accrual_rate apart.

dlt_time

Character scalar. Distribution of the time to DLT within the window, and of the time to response within the response window: "weibull" (the default) or "uniform".

late_fraction

Numeric scalar strictly between 0 and 1. Share of the DLTs that occur in the second half of the assessment window under the Weibull distribution. Defaults to 0.5.

resp_window

Numeric scalar. Length of the window within which a response is observed. Defaults to window.

resp_late_fraction

Numeric scalar strictly between 0 and 1. Share of the responses that occur in the second half of the response window under the Weibull distribution. Defaults to 0.5.

resp_cor

Numeric scalar strictly between -1 and 1. Correlation of the Gaussian copula that links the DLT and the response of a patient; a positive value makes a response more likely in a patient with a DLT. Defaults to 0, independence.

n_trials

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

start_dose

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

n_earlystop

Integer scalar. The trial stops once this many patients, backfilled ones included, have been treated at the current dose and the design would stay there. 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. Defaults to FALSE.

offset

Numeric scalar between 0 and 0.5. Amount by which cutoff_eli is relaxed for the safety stopping rule. Defaults to 0.05.

stay_on_1_of_3

Logical scalar. When TRUE, one DLT out of three patients leads to staying rather than de-escalating, in every use of the decision table. Zhao et al. (2024) use this modification in their main simulation. Defaults to FALSE. See boin_boundary.

bound_mtd

Logical scalar. Require the isotonic estimate at the selected dose to be at or below the de-escalation boundary. Defaults to FALSE.

mtd_max_estimate

Numeric scalar or NULL. Largest isotonic estimate a dose may have and still be selected as the MTD. See sim_boin.

min_mtd_sample

Integer scalar. Smallest number of patients a dose must have received to be eligible as the MTD. 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 target.

n_earlystop_rule

Character scalar, either "with_stay" or "simple". See boin_simulate.

keep_trials

Logical scalar. Keep the trial by trial data of every scenario. Defaults to FALSE.

verbose

Logical scalar. Report progress while the simulations run. Defaults to FALSE.

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 with sim_bf_boin. Defaults to 123.

Value

An object of class c("bf_boin_oc_multi", "backfill_oc_multi", "boin_oc_multi"), which is a list with components

results

Named list of bf_boin_oc objects, one per scenario.

summary_table

Data frame collecting all scenarios, with seven rows per scenario: the true DLT and response rates, the MTD selection, the patients treated, the backfilled patients, the patients with DLT and the average trial duration.

scenario_names

Character vector of scenario names.

n_doses

Number of dose levels.

call

The matched call.

See also

sim_bf_boin, sim_be_boin_multi

Examples

# Scenarios 2 and 6 of Zhao et al. (2024)
scenarios <- list(
  list(name = "Scenario 2", p_true = c(0.12, 0.25, 0.42, 0.49, 0.55),
       p_resp = c(0.20, 0.30, 0.40, 0.50, 0.60)),
  list(name = "Scenario 6", p_true = c(0.12, 0.25, 0.42, 0.49, 0.55),
       p_resp = c(0.30, 0.35, 0.36, 0.36, 0.36))
)

oc <- sim_bf_boin_multi(
  target = 0.25,
  scenarios = scenarios,
  n_cohort = 10,
  cohort_size = 3,
  window = 1,
  accrual_rate = 3,
  n_earlystop = 9,
  stay_on_1_of_3 = TRUE,
  n_trials = 200,
  seed = 123
)
oc
#> BF-BOIN operating characteristics across 2 scenarios
#>   target DLT rate : 0.25
#>   trials each     : 200
#>   escalation size : 30
#>   backfill cap    : 12 patients per dose
#>   window          : 1 (DLT), 1 (response)
#>   accrual         : 3 patients per unit of time (exponential)
#> 
#>    Scenario                   Item  DL1  DL2  DL3  DL4  DL5 Total / No MTD
#>  Scenario 2      True DLT rate (%) 12.0 25.0 42.0 49.0 55.0               
#>             True response rate (%) 20.0 30.0 40.0 50.0 60.0               
#>                   MTD selected (%) 30.0 59.5  9.0  0.5  0.0            1.0
#>                   Patients treated 10.8 10.9  3.9  0.6  0.0           26.2
#>                Backfilled patients  3.2  2.2  0.3  0.0  0.0            5.7
#>                  Patients with DLT  1.3  2.7  1.6  0.3  0.0            5.9
#>              Trial duration (mean)                                    11.9
#>  Scenario 6      True DLT rate (%) 12.0 25.0 42.0 49.0 55.0               
#>             True response rate (%) 30.0 35.0 36.0 36.0 36.0               
#>                   MTD selected (%) 29.5 59.0 11.0  0.0  0.0            0.5
#>                   Patients treated 11.6 10.8  3.7  0.4  0.0           26.5
#>                Backfilled patients  3.7  2.2  0.3  0.0  0.0            6.2
#>                  Patients with DLT  1.4  2.8  1.5  0.3  0.0            5.9
#>              Trial duration (mean)                                    11.9