UnifiedDoseFinding (version 0.1.10)

get_oc_gBOIN_TB: Generate operating characteristics for finding the maximum tolerated dose (MTD) defined by Toxicity Burden (TB) Score using gBOIN design

Description

Obtain the operating characteristics of the generalized Bayesian optimal interval (gBOIN) design (Mu et al. 2017) for maximum tolerated dose (MTD) (defined by the toxicity burden (BT) score proposed by Bekele et al. (2004))-based dosing-finding trials using. The algorithm of this function is exactly same to the get_oc_gBOIN_Continuous() just the input parameter is used by the TB score

Usage

get_oc_gBOIN_TB(target, pmat, weight, ncohort, cohortsize,
                n.earlystop = 100, ntrial, mu_1 = 0.6 * target,
                mu_2 = 1.4 * target, startdose = 1, seed = 100)

Value

get_oc_gBOIN_TB() returns the operating characteristics of generalized Bayesian optimal interval design as a list object, including: (1) selection percentage of each dose, (2) the average number of patients treated at each dose

Arguments

target

the target TB score

pmat

pmat is a list. Each element is a matrix, representing the probability of different toxicity type and scale under different dose levels.

weight

the severity weight

ncohort

the number of cohort

cohortsize

the cohort size

n.earlystop

the early stopping parameter. The default value is n.earlystop = 100

ntrial

the number of simulated trial

mu_1

the lower bound. The default value is p.saf = 0.6 * target

mu_2

the upper bound. The default value is mu_2 = 1.4 * target

startdose

the starting dose level. The default value is startdose = 1

seed

the seed. The default value is seed = 100

Author

Chia-Wei Hsu, Haitao Pan, Rongji Mu

References

Bekele, B. Nebiyou, and Peter F. Thall. "Dose-finding based on multiple toxicities in a soft tissue sarcoma trial." Journal of the American Statistical Association 99, no. 465 (2004): 26-35.

Rongji Mu, Ying Yuan, Jin Xu, Sumithra J. Mandrekar, Jun Yin: gBOIN: a unified model-assisted phase I trial design accounting for toxicity grades, and binary or continuous end points. Royal Statistical Society 2019

Examples

target <- 3.344
ncohort <- 10
cohortsize <- 3
ntrial <- 1000
rate <- 1.1
weight <- rate * rbind(c(0,1,1.5,5,6), c(0,2.5,6,rep(0,2)), c(0,2,3,6,0),
                       c(0,1.5,2,0,0), c(0,0.5,1,0,0))
pmat <- list()
pmat[[1]] <- rbind(c(0.5,0.5,rep(0,3)),
                   c(1,rep(0,4)),
                   c(1,rep(0,4)),
                   c(1,rep(0,4)),
                   c(0.5,0,0.5,0,0))
pmat[[2]] <- rbind(c(0.5,0,0.5,0,0),
                   c(1,rep(0,4)),
                   c(0.5,0.5,0,0,0),
                   c(0.5,0.5,rep(0,3)),
                   c(0.46,0,0.54,rep(0,2)))
pmat[[3]] <- rbind(c(0.5,0,0.5,0,0),
                   c(0.4,0.6,0,0,0),
                   c(0.25,0.75,0,0,0),
                   c(0.5,0.5,0,0,0),
                   c(1,0,0,0,0))
pmat[[4]] <- rbind(c(0.5,0,0.5,0,0),
                   c(0.4,0.6,0,0,0),
                   c(0.25,0.75,0,0,0),
                   c(0.5,0.5,0,0,0),
                   c(0.5,0,0.5,0,0))
pmat[[5]] <- rbind(c(0.5,0,0.5,0,0),
                   c(0,1,0,0,0),
                   c(0.25,0.75,0,0,0),
                   c(0.5,0.5,0,0,0),
                   c(0.5,0,0.5,0,0))
pmat[[6]] <- rbind(c(0,0.5,0.5,0,0),
                   c(0,1,0,0,0),
                   c(0,1,0,0,0),
                   c(0.5,0.5,0,0,0),
                   c(0.5,0,0.5,0,0))
pmat[[7]] <- rbind(c(0,0.5,0.5,0,0),
                   c(0,1,0,0,0),
                   c(0,1,0,0,0),
                   c(0,0.5,0.5,0,0),
                   c(0.5,0,0.5,0,0))
pmat[[8]] <- rbind(c(0,0.5,0.5,0,0),
                   c(0,1,0,0,0),
                   c(0,0,1,0,0),
                   c(0,0.5,0.5,0,0),
                   c(0.5,0,0.5,0,0))
pmat[[9]] <- rbind(c(0,0,1,0,0),
                   c(0,1,0,0,0),
                   c(0,0,1,0,0),
                   c(0,0,1,0,0),
                   c(0,0,1,0,0))
pmat[[10]] <- rbind(c(0,0,1,0,0),
                    c(0,1,0,0,0),
                    c(1/3,0,0,2/3,0),
                    c(0,0,1,0,0),
                    c(0,0,1,0,0))
get_oc_gBOIN_TB(target = target, pmat = pmat, weight = weight,
                ncohort = ncohort, cohortsize = cohortsize,
                ntrial = ntrial)