dfcomb (version 1.0-1)

logistic_sim: Combination design Simulator using Logistic model

Description

logistic_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

logistic_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, n_min=4, 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 column and agent 2 in row, with increasing toxicity probabilities with the column number, and decreasing toxicity p

target

Toxicity (probability) target (for dose allocation).

target_min

Minimum of the targeted toxicity interval (for dose recommendation).

target_max

Maximum of the targeted toxicity interval (for dose recommendation).

prior_tox_a1

A vector of initial guesses of toxicity probabilities associated with the doses of agent 1. Must be of same length as the columns of p_tox.

prior_tox_a2

A vector of initial guesses of toxicity probabilities associated with the doses of agent 2. Must be of same length as the rows of p_tox.

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. The default value is set at 0.95.

n_min

Minimum number of patients to be included before possible early trial termination. The default value is set at 4.

seed

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

Value

• An object of class "logistic_sim" is returned, consisting of the operating characteristics of the design specified. Objects generated by logistic_sim contain at least the following components:
• p_tox
  True toxicities.
• Percentage of Selection.
• Number of patients at each combination.
• Number of toxicities at each combination.
• inconc
  Percentage of inclusive trials.
• n_min
  Minimum number of cohorts to stop the trial.
• nsim
  Number of simulations.
• cohort
  Cohort size.
• n_cohort
  Number of cohort planned.
• Total patients accrued.
• target
  Toxicity target.
• Targeted toxicity interval.
• prior_tox_a1
  Prior toxicity probabilities for agent 1.
• prior_tox_a2
  Prior toxicity probabilities for agent 2.
• c_e
  Escalation threshold.
• c_d
  Deescalation threshold.
• c_stop
  Stopping threshold.
• tite
  Type of outcome for toxicity (time-to-event or binary).
• time_full
  If toxicity is a time-to-event, full follow-up time is also reminded.
• poisson_rate
  If toxicity is a time-to-event, rate for Poisson process is also reminded.

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

logistic_next.

Examples

p_tox_sc1 = matrix(c(0.15,0.10,0.05,0.30,0.15,0.10,0.45,0.30,0.15,0.50,0.45,
                     0.30,0.60,0.55,0.45),nrow=3,ncol=5)
p_tox_sc6 = matrix(c(0.15,0.09,0.05,0.30,0.12,0.08,0.45,0.15,0.10,0.50,0.30,
                     0.13,0.60,0.45,0.15),nrow=3,ncol=5)
prior_a1 = c(0.12, 0.2, 0.3, 0.4, 0.5)
prior_a2 = c(0.2, 0.3, 0.4)

# UNCOMMENT THOSE EXAMPLES
#log_sim1 = logistic_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_a1, prior_tox_a2=prior_a2, 
#  n_cohort=20, cohort=3, tite=FALSE, nsim=2, c_e=0.85, c_d=0.45, c_stop=0.95, 
#  n_min=4, seed = 14061991)
                        
#log_sim1
                        
#log_sim2 = logistic_sim(ndose_a1=5, ndose_a2=3, p_tox=p_tox_sc6, target=0.30, 
#  target_min=0.20, target_max=0.40, prior_tox_a1=prior_a1, prior_tox_a2=prior_a2, 
#  n_cohort=20, cohort=3, nsim=2)  

#log_sim2

# Dummy example, running quickly
useless = logistic_sim(ndose_a1=3, ndose_a2=2,
  p_tox=matrix(c(0.15,0.05,0.30,0.15,0.45,0.30),nrow=2), 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)