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Operating Characteristics of a TITE-BOIN Design Across Several Scenarios

Source: R/sim_tite_boin_multi.R
sim_tite_boin_multi.Rd

Run sim_tite_boin under a list of dose-toxicity scenarios that share the same design, and collect the results into one table.

Usage

sim_tite_boin_multi(
  target,
  scenarios,
  n_cohort,
  cohort_size,
  window,
  accrual_rate,
  method = c("imputation", "ess"),
  accrual = c("exponential", "uniform", "fixed"),
  dlt_time = c("weibull", "uniform"),
  late_fraction = 0.5,
  prior_weights = c(1, 1, 1)/3,
  max_pending_ratio = NULL,
  min_completed = NULL,
  min_follow_up = 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,
  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 0.30.

scenarios

A list of scenarios. Each element is a list with a name and a p_true vector, 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. 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.

method

Character scalar, "imputation" (the default) or "ess". How the pending patients enter the estimate of the DLT probability. See tite_boin_decision_table.

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), as in the code of Lin and Yuan (2020), or "fixed" for arrivals exactly 1 / accrual_rate apart.

dlt_time

Character scalar. Distribution of the time to DLT within the window: "weibull" (the default), with the share of DLTs in the second half of the window set by late_fraction, 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, the setting of Yuan et al. (2018) and Lin and Yuan (2020).

prior_weights

Numeric vector of three non-negative values. Prior probabilities that a DLT occurs in the first, second and last third of the assessment window, used to weight the follow-up of the pending patients in STFT (Yuan et al., 2018, Supplementary Appendix D) and ESS (Lin and Yuan, 2020, Supplementary S1). They are rescaled to sum to one. Defaults to equal weights, which give the unweighted STFT. The decision table does not change with the weights; only the value compared with its boundaries does. This describes the design and is separate from dlt_time and late_fraction, which describe how the simulated DLTs actually occur.

max_pending_ratio

Numeric scalar or NULL. Suspension rule on the proportion of pending patients. See tite_boin_decision_table.

min_completed

Integer scalar or NULL. Number of completed assessments required for escalation. See tite_boin_decision_table.

min_follow_up

Numeric scalar between 0 and 1. Shortest follow-up, as a fraction of window, that every pending patient at the current dose must have reached before the dose is escalated; accrual is suspended until then. Defaults to 0, which never suspends on this ground. See tite_boin_decision_table.

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 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.

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_tite_boin. Defaults to 123.

Value

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

results

Named list of tite_boin_oc objects, one per scenario.

summary_table

Data frame collecting all scenarios, with the four rows of sim_boin_multi and two more per scenario for the average trial duration and the percentage of trials with accrual suspended, both in the last column.

scenario_names

Character vector of scenario names.

n_doses

Number of dose levels.

call

The matched call.

See also

sim_tite_boin, sim_boin_multi

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_tite_boin_multi(
  target = 0.30,
  scenarios = scenarios,
  n_cohort = 10,
  cohort_size = 3,
  window = 3,
  accrual_rate = 2,
  n_trials = 200,
  seed = 123
)
oc
#> TITE-BOIN operating characteristics across 2 scenarios
#>   method          : single mean imputation (Yuan et al., 2018)
#>   target DLT rate : 0.3
#>   trials each     : 200
#>   max sample size : 30
#>   window          : 3
#>   accrual         : 2 patients per unit of time (exponential)
#> 
#>       Scenario                         Item  DL1  DL2  DL3  DL4  DL5
#>  MTD at dose 3            True DLT rate (%)  5.0 15.0 30.0 45.0 60.0
#>                            MTD selected (%)  2.0 30.5 56.0 11.5  0.0
#>                            Patients treated  5.8 10.6  9.1  3.2  0.4
#>                           Patients with DLT  0.3  1.6  2.8  1.5  0.3
#>                       Trial duration (mean)                         
#>                Accrual suspended (% trials)                         
#>      All toxic            True DLT rate (%) 35.0 45.0 55.0 65.0 75.0
#>                            MTD selected (%) 75.0  8.0  1.0  0.0  0.0
#>                            Patients treated 16.4  4.1  0.4  0.0  0.0
#>                           Patients with DLT  5.7  1.9  0.2  0.0  0.0
#>                       Trial duration (mean)                         
#>                Accrual suspended (% trials)                         
#>  Total / No MTD
#>                
#>             0.0
#>            29.2
#>             6.5
#>            23.5
#>           100.0
#>                
#>            16.0
#>            21.0
#>             7.8
#>            15.6
#>            98.0