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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
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)the target TB score
pmat is a list. Each element is a matrix, representing the probability of different toxicity type and scale under different dose levels.
the severity weight
the number of cohort
the cohort size
the early stopping parameter. The default value is n.earlystop = 100
the number of simulated trial
the lower bound. The default value is p.saf = 0.6 * target
the upper bound. The default value is mu_2 = 1.4 * target
the starting dose level. The default value is startdose = 1
the seed. The default value is seed = 100
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
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
# NOT RUN {
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)
# }
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