| get_oc_RQ_CRM {UnifiedDoseFinding} | R Documentation |
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)
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 |
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
Author(s)
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)