CombIncrease_sim {dfcomb}  R Documentation 
Combination design Simulator using Logistic model
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 dosefinding design for drug combination clinical trials based on the logistic model".
Usage
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, init_dose_1=1, init_dose_2=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 
prior_tox_a2 
A vector of initial guesses of toxicity probabilities associated with the doses of agent 2. Must be of length 
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 timetoevent outcome (TRUE), or as a binary outcome (default value FALSE). 
time_full 
Full followup 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 doseescalation. The default value is set at 0.85. 
c_d 
Probability threshold for dosedeescalation. The default value is set at 0.45. 
c_stop 
Probability threshold for early trial termination due to overtoxicity or undertoxicity (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 overdosing (see details). 
cmin_overunder 
Minimum number of cohorts to be included at the lowest/highest combination before possible early trial termination for overtoxicity or undertoxicity (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 startup 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. 
init_dose_1 
Initial dose for agent 1. The default is 1. 
init_dose_2 
Initial dose for agent 2. The default is 1. 
nburn 
Number of burnin 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
Startup phase:

startup=0
: No startup phase: the first tested combination is forced to be the initial combination. The following ones use the normal allocation rule.. 
startup=1
(Riviere et al 2014): Begin at the initial combination and increase both agent (+1, +1) until the first toxicity is observed or maximum combination is reached. 
startup=2
: Begin at the initial combination and increase agent 1 (+1, 0) until a toxicity is observed or maximum dose is reached. Then begin at (init_dose1,init_dose2+1) and increase agent 2 (0, +1) until a toxicity is observed or maximum dose is reached. 
startup=3
: Begin at the initial combination and increase alternatively each agent (+1, 0) then (0, +1) until the first toxicity is observed or maximum combination is reached.
Allocation rule:

alloc_rule=1
(Riviere et al 2014): If P(toxicity probability at combination (i,j) <target
) >c_e
: among combinations in the neighborhood (1, +1), (0, +1), (+1, 0), (+1, 1), choose the combination with a higher estimated toxicity probability than the current combination and with the estimated toxicity probability closest totarget
. If P(toxicity probability at combination (i,j) >target
) > 1c_d
: among neighborhood (1, +1), (1, 0), (0, 1), (+1, 1), choose the combination with a lower estimated toxicity probability than the current combination and with the estimated toxicity probability closest totarget
. Otherwise, remain on the same combination. 
alloc_rule=2
: Among combinations already tested and combinations in the neighborhood (1, 0), (1, +1), (0, +1), (+1, 0), (+1, 1), (0, 1), (1, 1) of a combination tested, choose the combination with the highest posterior probability to be in the targeted interval [target_min
,target_max
] while controling overdosing i.e. P(toxicity probability at combination (i,j) >target_max
) <c_over
. 
alloc_rule=3
: Among combinations in the neighborhood (1, 0), (1, +1), (0, +1), (+1, 0), (+1, 1), (0, 1), (1, 1) of the current combination, choose the combination with the highest posterior probability to be in the targeted interval [target_min
,target_max
] while controling overdosing i.e. P(toxicity probability at combination (i,j) >target_max
) <c_over
.
Early stopping for overdosing:
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 underdosing:
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:

early_stop=1
(Riviere et al 2014): No stopping rule, include patients until maximum sample size is reached. 
early_stop=2
: If the next recommended combination has been tested on at leastcmin_mtd
cohorts and has a posterior probability to be in the targeted interval [target_min
,target_max
] that is >=c_t
and also control overdosing i.e. P(toxicity probability at current combination >target_max
) <c_over
then stop the trial and recommend this combination. 
early_stop=3
: If at leastcmin_mtd
cohorts have been included at the next recommended combination then stop the trial and recommend this combination.
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)
JacquesHenri Jourdan and MarieKarelle RiviereJourdan eldamjh@gmail.com
References
Riviere, MK., Yuan, Y., Dubois, F., and Zohar, S. (2014). A Bayesian dosefinding design for drug combination clinical trials based on the logistic model. Pharmaceutical Statistics.
See Also
Examples
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_a1,
prior_tox_a2=prior_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)