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

Description

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

Usage

CFOeff.oc(target, p.true=p.true, pE.true=pE.true, prior.para = 
                 list(alp.prior = target, bet.prior = 1 - target, 
                 alp.prior.eff = 0.5, bet.prior.eff = 0.5),  
                 init.level = 1, cohortsize=cohortsize, ncohort=ncohort, 
                 nsimu, cutoff.eli=0.95, 
                 early.stop=0.95, effearly.stop = 0.9, mineff,
                 seeds = NULL)

Value

The CFOeff.oc() function returns a list object, which includes the basic setup (simu.setup), comprising the following components:

p.true: the true DLT rates under the different dose levels.
pE.true: the true efficacy 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.
neff: the averaged number of efficacy outcome at each dose level in one simulation.
OBDsel: the percentage of correct selection of the OBD.
OBDallo: the percentage of patients allocated to the OBD.
averDLT: the percentage of the patients suffering DLT.
avereff: the percentage of the patients with efficacy outcomes.
percentstop: the percentage of early stopping without selecting the OBD.
simu.setup: the parameters for the simulation set-up.
class: the phase of the trial.

Arguments

target: the target DLT rate.
p.true: the true DLT rates under the different dose levels.
pE.true: the true efficacy rates under the different dose levels.
prior.para: the prior parameters for two beta distributions, where set as list(alp.prior = target, bet.prior = 1 - target, alp.prior.eff = 0.5, bet.prior.eff = 0.5) 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). alp.eff.prior and bet.eff.prior represent the parameters of the Jeffreys' prior distribution for the efficacy probability at any dose level. This prior distribution is specified as Beta(alpha.eff.prior, beta.eff.prior).
init.level: the dose level assigned to the first cohort. The default value init.level is 1.
cohortsize: the number of patients in each cohort.
ncohort: the total number of cohorts.
nsimu: the total number of trials to be simulated.
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 due to overly toxic. The default value early.stop = 0.95 generally works well.
effearly.stop: the threshold value for early stopping due to low efficacy. The trial would be terminated early if \(Pr(q_k<\psi |y_k,m_k \ge 3)\) is smaller than the value of effearly.stop where \(q_k, y_k\) and \(m_k\) are the efficacy probability, the number of efficacy outcomes and the number of patients at dose level \(k\). \(\psi\) is the the lowest acceptable efficacy rate which is set by mineff here. By default, effearly.stop is set as 0.9.
mineff: the lowest acceptable efficacy rate.
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.

Examples

Run this code

target <- 0.30; mineff <- 0.30; cohortsize = 3; ncohort = 12; nsimu = 3; init.level = 1
prior.para = list(alp.prior = target, bet.prior = 1 - target, 
                  alp.prior.eff = 0.5, bet.prior.eff = 0.5)
p.true=c(0.05, 0.07, 0.1, 0.12, 0.16)
pE.true=c(0.35, 0.45, 0.5, 0.55, 0.75)
result <- CFOeff.oc (target, p.true, pE.true, prior.para, 
          init.level,cohortsize, ncohort, nsimu, mineff = mineff, seeds = 1:nsimu)
summary(result)
plot(result)
#earlystop for overly tox
target <- 0.30; mineff <- 0.30; cohortsize = 3; ncohort = 12; nsimu = 3; init.level = 1
prior.para = list(alp.prior = target, bet.prior = 1 - target, 
                  alp.prior.eff = 0.5, bet.prior.eff = 0.5)
p.true=c(0.75, 0.77, 0.81, 0.82, 0.86)
pE.true=c(0.35, 0.45, 0.5, 0.55, 0.75)
result <- CFOeff.oc (target, p.true, pE.true, prior.para, 
          init.level,cohortsize, ncohort, nsimu, mineff = mineff, seeds = 1:nsimu)
summary(result)
plot(result)

#earlystop for lower efficacy
target <- 0.30; mineff <- 0.30; cohortsize = 3; ncohort = 20; nsimu = 3; init.level = 1
prior.para = list(alp.prior = target, bet.prior = 1 - target, 
                  alp.prior.eff = 0.5, bet.prior.eff = 0.5)
p.true=c(0.05, 0.07, 0.1, 0.12, 0.16)
pE.true=c(0.001, 0.001, 0.001, 0.002, 0.003)
result <- CFOeff.oc (target, p.true, pE.true, prior.para, 
          init.level,cohortsize, ncohort, nsimu, mineff = mineff, seeds = 1:nsimu)
summary(result)
plot(result)

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