dfcomb (version 1.0-1)

logistic_next: Combination determination with logistic model

Description

logistic_next is used to determine the next or recommended combination in a Phase I combination clinical trial 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_next(ndose_a1, ndose_a2, target, target_min, target_max, prior_tox_a1, 
prior_tox_a2, final, pat_incl, dose_adm1, dose_adm2, tite=FALSE, time_full, 
toxicity, time_tox, time_follow, c_e=0.85, c_d=0.45, c_stop=0.95, n_min)

Arguments

ndose_a1

Number of dose levels for agent 1.

ndose_a2

Number of dose levels for agent 2.

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.

final

A boolean with value TRUE if the trial is finished and the recommended combination for further phases should be given, or FALSE (default value) if the combination determination is performed for the next cohort of patients.

pat_incl

Current number of patients included.

dose_adm1

A vector indicating the dose levels of agents 1 administered to each patient included in the trial. Must be of length pat_incl.

dose_adm2

A vector indicating the dose levels of agents 2 administered to each patient included in the trial. Must be of length pat_incl.

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.

toxicity

A vector of observed toxicities (DLTs) for each patient included in the trial. Must be of length pat_incl.

time_tox

A vector of times-to-toxicity for each patient included in the trial. If no toxicity was observed for a patient, must be filled with +Inf. Must be of length pat_incl. This argument is used/required only if tite=TRUE.

time_follow

A vector of follow-up times for each patient included in the trial. Must be of length pat_incl. This argument is used/required only if tite=TRUE.

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.

Value

• An object of class "logistic_next" is returned, consisting of determination of the next combination and estimations. Objects generated by logistic_next contain at least the following components:
• Number of patients per combination.
• Number of observed toxicities per combination.
• Estimated toxicity probabilities (if the start-up ended).
• Estimated probabilities that the toxicity probability is inferior to target (if the start-up ended).
• Estimated probabilities of underdosing, i.e. to be inferior to target_min (if the start-up ended).
• Estimated probabilities to be in the targeted interval [target_min,target_max] (if the start-up ended).
• Estimated probabilities of overdosing, i.e. to be superior to target_max (if the start-up ended).
• Start-up phase is ended or not.
• NEXT RECOMMENDED COMBINATION.
• cohort
  Cohort size.
• Number of patients included.
• target
  Toxicity target.
• Targeted toxicity interval.
• prior_tox_a1
  Prior toxicity probabilities for agent 1.
• prior_tox_a2
  Prior toxicity probabilities for agent 2.
• n_min
  Minimum number of cohorts to stop the trial.
• 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.

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_sim.

Examples

prior_a1 = c(0.12, 0.2, 0.3, 0.4, 0.5)
prior_a2 = c(0.2, 0.3, 0.4)
toxicity1 = c(0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1)
dose1 = c(1,1,1,2,2,2,3,3,3,3,3,3,3,3,3,4,4,4)
dose2 = c(1,1,1,2,2,2,3,3,3,2,2,2,1,1,1,1,1,1)
t_tox = c(rep(+Inf,8),2.9,+Inf,4.6,+Inf,+Inf,+Inf,+Inf,+Inf,+Inf,5.2)
follow = c(rep(6,15), 4.9, 3.1, 1.3)

log_next1 = logistic_next(ndose_a1=5, ndose_a2=3, target=0.3, target_min=0.20, 
target_max=0.40, prior_tox_a1=prior_a1, prior_tox_a2=prior_a2, final=FALSE, 
pat_incl=18, dose_adm1=dose1, dose_adm2=dose2, tite=FALSE, toxicity=toxicity1, 
n_min=6)

log_next1

log_next2 =logistic_next(ndose_a1=5, ndose_a2=3, target=0.30, target_min=0.20, 
target_max=0.40, prior_tox_a1=prior_a1, prior_tox_a2=prior_a2, final=FALSE, 
pat_incl=18, dose_adm1=dose1, dose_adm2=dose2, tite=TRUE, time_full=6, 
time_tox=t_tox, time_follow=follow, n_min=6)

log_next2