CFO2d.oc {CFO}

Generate operating characteristics of drug-combination trials in multiple simulations

Description

This function is used to conduct multiple simulations of drug-combination trials and obtain relevant the operating characteristics.

Usage

CFO2d.oc(nsimu = 1000, target, p.true, init.level = c(1,1), ncohort, cohortsize,
               prior.para = list(alp.prior = target, bet.prior = 1 - target), 
               cutoff.eli = 0.95, early.stop = 0.95, seeds = NULL)

Arguments

nsimu

the total number of trials to be simulated. The default value is 1000.

target

the target DLT rate.

p.true

a matrix representing the true DIL rates under the different dose levels.

init.level

a numeric vector of length 2 representing the initial dose level (default is c(1,1)).

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.

seeds

A vector of random seeds for each simulation, for example, seeds = 1:nsimu (default is NULL).

Value

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

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

• selpercent: the matrix of the selection percentage of each dose level.

• npatients: a matrix of the averaged number of patients allocated to different doses in one simulation.

• ntox: a matrix of the averaged number of DLT observed for different doses in one simulation.

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

• MTDallo: the averaged percentage of patients assigned to the target DLT rate.

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

• overallo: the averaged percentage of patients assigned to dose levels with a DLT rate greater than the target.

• averDLT: the averaged total number of DLTs observed.

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

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

Note

In the example, we set nsimu = 10 for testing time considerations. In reality, nsimu is typically set to 1000 or 5000 to ensure the accuracy of the results.

Author(s)

Jialu Fang, 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.
Wang W, Jin H, Zhang Y, Yin G (2023). Two-dimensional calibration-free odds (2dCFO) design for phase I drug-combination trials. Frontiers in Oncology, 13, 1294258.

Examples

## Simulate a two-dimensional dose-finding trial with 20 cohorts of size 3 for 10 replications.
p.true <- 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 = 3, ncol = 5, byrow = TRUE)
target <- 0.3; ncohort <- 12; cohortsize <- 3
CFO2doc <- CFO2d.oc(nsimu = 5, target, p.true, init.level = c(1,1), ncohort, cohortsize, 
                    seeds = 1:5)
summary(CFO2doc)
plot(CFO2doc)