CFO (version 2.1.0)

CFO.oc: Generate operating characteristics of phase I trials single-drug trials in multiple simulations

Description

Based on the toxicity outcomes, this function is used to perform multiple simulations for phase I single-drug trials and obtain relevant operating characteristics.

Usage

CFO.oc(nsimu = 5000, design, target, p.true, init.level = 1, ncohort, cohortsize,
       assess.window = NA, tte.para = NA, accrual.rate = NA, accrual.dist = NA, 
       prior.para = list(alp.prior = target, bet.prior = 1 - target),
       cutoff.eli = 0.95, early.stop = 0.95, seeds = NULL)

Value

The CFO.oc() function returns basic setup of ($simu.setup) and the operating characteristics of the design:

• p.true: the true DLT rates under the different dose levels.

• selpercent: the selection percentage at each dose level.

• npatients: the averaged number of patients treated at each dose level in one simulation.

• ntox: the averaged number of toxicity observed at each dose level in one simulation.

• MTDsel: the percentage of correct selection of the MTD.

• MTDallo: the percentage of patients allocated to the MTD.

• oversel: the percentage of selecting a dose above the MTD.

• overallo: the percentage of allocating patients at dose levels above the MTD.

• averDLT: the percentage of the patients suffering DLT.

• averdur: the average trial duration if trials with late-onset toxicities.

• percentstop: the percentage of early stopping without selecting the MTD.

• simu.setup: the parameters for the simulation set-up.

Arguments

nsimu

the total number of trials to be simulated. The default value is 5000.

design

option for selecting different designs, which can be set as 'CFO', 'aCFO', 'rCFO', 'TITE-CFO', 'TITE-aCFO', 'fCFO', 'f-aCFO', 'bCFO', and 'b-aCFO'. Specifically, 'bCFO' refers to the benchmark CFO design, and 'b-aCFO' denotes the benchmark aCFO design.

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.

assess.window

the maximal assessment window size. NA should be assigned if the design without late-oneset outcomes.

tte.para

the parameter related with the distribution of the time to DLT events. The time to DLT is sampled from a Weibull distribution, with tte.para representing the proportion of DLTs occurring within the first half of the assessment window. NA should be assigned if the design without late-oneset outcomes.

accrual.rate

the accrual rate, i.e., the number of patients accrued per unit time. NA should be assigned if the design without late-onset outcomes.

accrual.dist

the distribution of the arrival times of patients. When accrual.dist = 'fix', it corresponds to all patients in each cohort arriving simultaneously at a given accrual rate. When accrual.dist = 'unif', it corresponds to a uniform distribution, and when accrual.dist = 'exp', it corresponds to an exponential distribution. NA should be assigned if the design without late-oneset outcomes.

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.

seeds

a vector of random seeds for each simulation, for example, seeds = 1:nsimu (default is 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.
Jin H, Yin G (2023). Time‐to‐event calibration‐free odds design: A robust efficient design for phase I trials with late‐onset outcomes. Pharmaceutical Statistics. 22(5), 773–783.
Yin G, Zheng S, Xu J (2013). Fractional dose-finding methods with late-onset toxicity in phase I clinical trials. Journal of Biopharmaceutical Statistics, 23(4), 856-870.
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

## setting
nsimu <- 5; target <- 0.2; ncohort <- 10; cohortsize <- 3; init.level <- 1
p.true <- c(0.01, 0.07, 0.20, 0.35, 0.50, 0.65, 0.80)
prior.para = list(alp.prior = target, bet.prior = 1 - target)
assess.window <- 3; accrual.rate <- 2; tte.para <- 0.5; accrual.dist <- 'unif'


## get the operating characteristics for 5 simulations using the f-aCFO design
faCFOoc <- CFO.oc (nsimu, design='f-aCFO', target, p.true, init.level, ncohort, cohortsize,
        assess.window, tte.para, accrual.rate, accrual.dist, seeds = 1:nsimu)
summary(faCFOoc)
plot(faCFOoc)

# \donttest{
# This test may take longer than 5 seconds to run
# It is provided for illustration purposes only
# Users can run this code directly

## get the operating characteristics for 5 simulations using the CFO design
CFOoc <- CFO.oc (nsimu, design = 'CFO', target, p.true, init.level, ncohort, cohortsize,
         assess.window = NA, tte.para = NA, accrual.rate = NA, accrual.dist = NA, seeds = 1:nsimu)
summary(CFOoc)
plot(CFOoc)

## get the operating characteristics for 5 simulations using the aCFO design
aCFOoc <- CFO.oc (nsimu, design = 'aCFO', target, p.true, init.level, ncohort, cohortsize,
       assess.window = NA, tte.para = NA, accrual.rate = NA, accrual.dist = NA, seeds = 1:nsimu)
summary(aCFOoc)
plot(aCFOoc)

## get the operating characteristics for 5 simulations using the rCFO design
rCFOoc <- CFO.oc (nsimu, design = 'rCFO', target, p.true, init.level, ncohort, cohortsize,
       assess.window = NA, tte.para = NA, accrual.rate = NA, accrual.dist = NA, seeds = 1:nsimu)
summary(rCFOoc)
plot(rCFOoc)

## get the operating characteristics for 5 simulations using the TITE-CFO design
TITECFOoc <- CFO.oc (nsimu, design = 'TITE-CFO', target, p.true, init.level, ncohort, cohortsize,
        assess.window, tte.para, accrual.rate, accrual.dist, seeds = 1:nsimu)
summary(TITECFOoc)
plot(TITECFOoc)
## get the operating characteristics for 5 simulations using the TITE-aCFO design
TITEaCFOoc <- CFO.oc (nsimu, design = 'TITE-aCFO', target, p.true, init.level, ncohort, cohortsize,
        assess.window, tte.para, accrual.rate, accrual.dist, seeds = 1:nsimu)
summary(TITEaCFOoc)
plot(TITEaCFOoc)
## get the operating characteristics for 5 simulations using the fCFO design
fCFOoc <- CFO.oc (nsimu, design = 'fCFO', target, p.true, init.level, ncohort, cohortsize,
        assess.window, tte.para, accrual.rate, accrual.dist, seeds = 1:nsimu)
summary(fCFOoc)
plot(fCFOoc)
## get the operating characteristics for 5 simulations using the bCFO design
bCFOoc <- CFO.oc (nsimu, design = 'bCFO', target, p.true, init.level, ncohort, cohortsize,
        assess.window, tte.para, accrual.rate, accrual.dist, seeds = 1:nsimu)
summary(bCFOoc)
plot(bCFOoc)
## get the operating characteristics for 5 simulations using the b-aCFO design
baCFOoc <- CFO.oc (nsimu, design = 'b-aCFO', target, p.true, init.level, ncohort, cohortsize,
        assess.window, tte.para, accrual.rate, accrual.dist, seeds = 1:nsimu)
summary(baCFOoc)
plot(baCFOoc)
# }