CombIncrease_sim: Combination design Simulator using Logistic model

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

CombIncrease_sim is used to generate simulation replicates of phase I clinical trial for combination studies where the toxicity and efficacy of both agents is assumed to increase with the dose using the design proposed by Riviere et al. entitled "A Bayesian dose-finding design for drug combination clinical trials based on the logistic model".

Usage

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CombIncrease_sim(ndose_a1, ndose_a2, p_tox, target, target_min, target_max,
  prior_tox_a1, prior_tox_a2, n_cohort, cohort, tite=FALSE, time_full=0,
  poisson_rate=0, nsim, c_e=0.85, c_d=0.45, c_stop=0.95, c_t=0.5, c_over=0.25,
  cmin_overunder=2, cmin_mtd=3, cmin_recom=1, startup=1, alloc_rule=1,
  early_stop=1, nburn=2000, niter=5000, seed=14061991)

Arguments

ndose_a1

Number of dose levels for agent 1.

ndose_a2

Number of dose levels for agent 2.

p_tox

A matrix of the true toxicity probabilities associated with the combinations. True toxicity probabilities should be entered with agent 1 in row and agent 2 in column, with increasing toxicity probabilities with both row and column numbers (see examples).

target

Toxicity (probability) target.

target_min

Minimum of the targeted toxicity interval.

target_max

Maximum of the targeted toxicity interval.

prior_tox_a1

A vector of initial guesses of toxicity probabilities associated with the doses of agent 1. Must be of length ndose_a1.

prior_tox_a2

A vector of initial guesses of toxicity probabilities associated with the doses of agent 2. Must be of length ndose_a2.

n_cohort

Total number of cohorts to include in the trial.

cohort

Cohort size.

tite

A boolean indicating if the toxicity is considered as a time-to-event outcome (TRUE), or as a binary outcome (default value FALSE).

time_full

Full follow-up time window. This argument is used only if tite=TRUE.

poisson_rate

A value indicating the rate for the Poisson process used to simulate patient arrival, i.e. expected number of arrivals per observation window. This argument is used only if tite=TRUE.

nsim

Number of simulations.

c_e

Probability threshold for dose-escalation. The default value is set at 0.85.

c_d

Probability threshold for dose-deescalation. The default value is set at 0.45.

c_stop

Probability threshold for early trial termination due to over-toxicity or under-toxicity (see details). The default value is set at 0.95.

c_t

Probability threshold for early trial termination for finding the MTD (see details). The default value is set at 0.5.

c_over

Probability threshold to control over-dosing (see details).

cmin_overunder

Minimum number of cohorts to be included at the lowest/highest combination before possible early trial termination for over-toxicity or under-toxicity (see details). The default value is set at 2.

cmin_mtd

Minimum number of cohorts to be included at the recommended combination before possible early trial termination for finding the MTD (see details). The default value is set at 3.

cmin_recom

Minimum number of cohorts to be included at the recommended combination at the end of the trial. The default value is set at 1.

startup

Interger (0, 1, 2, or 3) indicating which start-up phase is used (see details). The default value is set at 1.

alloc_rule

Interger (1, 2, or 3) indicating which allocation rule is used (see details). The default value is set at 1.

early_stop

Interger (1, 2, or 3) indicating which early stopping rule is used (see details). The default value is set at 1.

nburn

Number of burn-in for HMC. The default value is set at 2000.

niter

Number of iterations for HMC. The default value is set at 5000.

seed

Seed of the random number generator. Default value is set at 14061991.

Details

Start-up phase:

Allocation rule:

Early stopping for over-dosing: If the current combination is the lowest (1, 1) and at least cmin_overunder cohorts have been included at that combination and P(toxicity probability at combination (i,j) > target) >= c_stop then stop the trial and do not recommend any combination.

Early stopping for under-dosing: If the current combination is the highest and at least cmin_overunder cohorts have been included at that combination and P(toxicity probability at combination (i,j) < target) >= c_stop then stop the trial and do not recommend any combination.

Early stopping for identifying the MTD:

Stopping at the maximum sample size: If the maximum sample size is reached and no stopping rule is met, then the recommended combination is the one that was tested on at least cmin_recom cohorts and with the highest posterior probability to be in the targeted interval [target_min, target_max].

Value

An object of class "CombIncrease_sim" is returned, consisting of the operating characteristics of the design specified. Objects generated by CombIncrease_sim contain at least the following components:

rec_dose

Percentage of combination selection.

n_pat_dose

Mean number of patients at each combination.

n_tox_dose

Mean number of toxicities at each combination.

inconc

Percentage of inclusive trials.

early_conc

Percentage of trials stopping with criterion for finding MTD.

nsim

Number of simulations (if function stopped while executed, return the current number of simulations performed with associated other outputs).

pat_tot

Total mean number of patients accrued.

tab_pat

Vector with the number of patients included for each simulation.

Author(s)

Jacques-Henri Jourdan and Marie-Karelle Riviere-Jourdan eldamjh@gmail.com

References

Riviere, M-K., Yuan, Y., Dubois, F., and Zohar, S. (2014). A Bayesian dose-finding design for drug combination clinical trials based on the logistic model. Pharmaceutical Statistics.

See Also

CombIncrease_next.

Examples

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p_tox_sc1 = matrix(c(0.05,0.10,0.15,0.30,0.45,
                     0.10,0.15,0.30,0.45,0.55,
                     0.15,0.30,0.45,0.50,0.60),nrow=5,ncol=3)
prior_a1 = c(0.12, 0.2, 0.3, 0.4, 0.5)
prior_a2 = c(0.2, 0.3, 0.4)

sim1 = CombIncrease_sim(ndose_a1=5, ndose_a2=3, p_tox=p_tox_sc1, target=0.30,
  target_min=0.20, target_max=0.40, prior_tox_a1=prior_tox_a1,
  prior_tox_a2=prior_tox_a2, n_cohort=20, cohort=3, tite=FALSE, nsim=2000,
  c_over=1, cmin_overunder=3, cmin_recom=1, startup=1, alloc_rule=1,
  early_stop=1, seed=14061991)
sim1


# Dummy example, running quickly
useless = CombIncrease_sim(ndose_a1=3, ndose_a2=2,
  p_tox=matrix(c(0.05,0.15,0.30,0.15,0.30,0.45),nrow=3), target=0.30,
  target_min=0.20, target_max=0.40, prior_tox_a1=c(0.2,0.3,0.4),
  prior_tox_a2=c(0.2,0.3), n_cohort=2, cohort=2, nsim=1)

dfcomb documentation built on May 2, 2019, 7:23 a.m.