UnifiedDoseFinding (version 0.1.10)

get_oc_RQ_CRM: Generate operating characteristics for finding the maximum tolerated dose (MTD) defined by Equivalent Score (ET) using Quasi-CRM design

Description

Obtain the operating characteristics of Quasi-CRM design (Yuan et al. 2007) and Robust-Quasi-CRM design (Pan et al. 2014) for finding the maximum tolerated dose (MTD) using Equivalent Score (ET) derived from toxicity grade information

Usage

get_oc_RQ_CRM(ptox, skeletons, target, score, cohortsize,
              ncohort, n.earlystop = 100, start.dose = 1,
              mselection = 1, cutoff.eli = 0.90, ntrial = 10,
              seed = 100)

Value

get_oc_RQ_CRM() returns the operating characteristics of (Robust)-Quasi-CRM design as a list object, including: (1) selection percentage at each dose level (2) patients treated at each dose level

Arguments

ptox

true toxicity probability at each dose level

skeletons

a matrix to provide multiple skeletons with each row presenting a skeleton. If just one row, the function implements the Quasi-CRM design; if >=2 rows, the function implements the Robust-Quasi-CRM designn

target

the target toxicity score

score

the vector weight for ordinal toxicity levels

cohortsize

the cohort size

ncohort

the number of cohort

n.earlystop

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

start.dose

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

mselection

mselection = 1 (or 0) indicate to use Bayesian model selection (or mode averaging) to make inference across multiple skeletons. The default value is mselection = 1. It only applies to the Robust-Quasi-CRM design

cutoff.eli

the cutoff to eliminate an overly toxic dose for safety. The default value is cutoff.eli = 0.90

ntrial

the number of simulated trials. The default value is ntrial = 10

seed

the seed. The default value is seed = 100

Author

Chia-Wei Hsu, Haitao Pan, Rongji Mu

References

Yuan, Z., R. Chappell, and H. Bailey. "The continual reassessment method for multiple toxicity grades: a Bayesian quasi-likelihood approach." Biometrics 63, no. 1 (2007): 173-179.

Pan, Haitao, Cailin Zhu, Feng Zhang, Ying Yuan, Shemin Zhang, Wenhong Zhang, Chanjuan Li, Ling Wang, and Jielai Xia. "The continual reassessment method for multiple toxicity grades: a Bayesian model selection approach." PloS one 9, no. 5 (2014): e98147.

Examples

### Scenario 1 in Yuan et al. (2007) and Pan et al. (2014)
target <- 0.47
score <- c(0, 0.5, 1, 1.5)
cohortsize <- 3
ncohort <- 10
ntrial <- 10

ptox <- matrix(nrow = 4, ncol = 6)
ptox[1,] <- c(0.83, 0.75, 0.62, 0.51, 0.34, 0.19)
ptox[2,] <- c(0.12, 0.15, 0.18, 0.19, 0.16, 0.11)
ptox[3,] <- c(0.04, 0.07, 0.11, 0.14, 0.15, 0.11)
ptox[4,] <- c(0.01, 0.03, 0.09, 0.16, 0.35, 0.59)


### specify one skeleton (Quasi-CRM design)
p1 <- c(0.11, 0.25, 0.40, 0.55, 0.75, 0.85)

get_oc_RQ_CRM(ptox = ptox, skeletons = p1, target = target,
              score = score, cohortsize = cohortsize,
              ncohort = ncohort, ntrial = ntrial)




###########################################

### specify three skeletons (Quasi-CRM design)
p1 <- c(0.11, 0.25, 0.40, 0.55, 0.75, 0.85)
p2 <- c(0.05, 0.10, 0.15, 0.25, 0.40, 0.65)
p3 <- c(0.20, 0.40, 0.60, 0.75, 0.85, 0.95)
skeletons <- rbind(p1, p2, p3)


get_oc_RQ_CRM(ptox = ptox, skeletons = skeletons, target = target,
              score = score, cohortsize = cohortsize,
              ncohort = ncohort, ntrial = ntrial)