CFO (version 2.1.0)

CFO.simu: Conduct one simulation using the calibration-free odds (CFO), accumulative CFO (aCFO) design, or randomized CFO (rCFO) design for phase I trials.

Description

In the CFO, aCFO, and rCFO designs for phase I trials, the function is used to conduct one single simulation and find the maximum tolerated dose (MTD).

Usage

CFO.simu(design, target, p.true, init.level = 1, ncohort, cohortsize,
       prior.para = list(alp.prior = target, bet.prior = 1 - target), 
       cutoff.eli = 0.95, early.stop = 0.95, seed = NULL)

Value

The CFO.simu function returns a list object comprising the following components:

• target: the target DLT rate.

• MTD: the selected MTD. MTD = 99 indicates that the simulation is terminated due to early stopping.

• correct: a binary indicator of whether the recommended dose level matches the correct MTD (1 for yes). The correct MTD is the dose level at which the true DLT rate is closest to the target DLT rate.

• npatients: the total number of patients allocated to all dose levels.

• ntox: the total number of DLTs observed for all dose levels.

• over.doses: a vector indicating whether each dose is overdosed or not (1 for yes).

• cohortdose: a vector including the dose level assigned to each cohort.

• ptoxic: the percentage of subjects assigned to dose levels with a DLT rate greater than the target.

• patientDLT: a vector including the DLT outcome observed for each patient.

• sumDLT: the total number of DLT observed.

• earlystop: a binary indicator of whether the trial is early stopped (1 for yes).

• p_est: the isotonic estimate of the DLT probablity at each dose and associated \(95\%\) credible interval. p_est = NA if all tested doses are overly toxic.

• p_overdose: p_overdose: the probability of overdosing defined as \(Pr(toxicity > \code{target}|data)\). p_overdose = NA if all tested doses are overly toxic.

Arguments

design

option for selecting different designs, which can be set as 'CFO', 'aCFO' or 'rCFO'.

target

the target DLT rate.

p.true

the true DLT rates under the different dose levels.

init.level

the dose level assigned to the first cohort. The default value init.level is 1.

ncohort

the total number of cohorts.

cohortsize

the number of patients of each cohort.

prior.para

the prior parameters for a beta distribution, where set as list(alp.prior = target, bet.prior = 1 - target) by default, alp.prior and bet.prior represent the parameters of the prior distribution for the true DLT rate at any dose level. This prior distribution is specified as Beta(alpha.prior, beta.prior).

cutoff.eli

the cutoff to eliminate overly toxic doses for safety. We recommend the default value of cutoff.eli = 0.95 for general use.

early.stop

the threshold value for early stopping. The default value early.stop = 0.95 generally works well.

seed

an integer to be set as the seed of the random number generator for reproducible results. The default value is set to NULL.

Author

Jialu Fang, Ninghao Zhang, Wenliang Wang, and Guosheng Yin

References

Jin H, Yin G (2022). CFO: Calibration-free odds design for phase I/II clinical trials. Statistical Methods in Medical Research, 31(6), 1051-1066.
Fang J, Yin G (2024). Fractional accumulative calibration‐free odds (f‐aCFO) design for delayed toxicity in phase I clinical trials. Statistics in Medicine, 43(17), 3210-3226.

Examples

target <- 0.2; ncohort <- 12; cohortsize <- 3; init.level <- 1
p.true <- c(0.01, 0.07, 0.20, 0.35, 0.50, 0.65, 0.80)
### find the MTD for a single CFO simulation
CFOtrial <- CFO.simu(design = 'CFO', target, p.true, init.level, ncohort, cohortsize, seed = 1)
summary(CFOtrial)
plot(CFOtrial)
# \donttest{
# This test may take longer than 5 seconds to run
# It is provided for illustration purposes only
# Users can run this code directly
### find the MTD for a single aCFO simulation
aCFOtrial <- CFO.simu(design = 'aCFO', target, p.true, init.level, ncohort, cohortsize, seed = 1)
summary(aCFOtrial)
plot(aCFOtrial)
### find the MTD for a single rCFO simulation
rCFOtrial <- CFO.simu(design = 'rCFO', target, p.true, init.level, ncohort, cohortsize, seed = 1)
summary(rCFOtrial)
plot(rCFOtrial)
# }