UnifiedDoseFinding (version 0.1.10)

select_mtd_RQ_CRM: Select the maximum tolerated dose (MTD) using Quasi-CRM design

Description

Select the maximum tolerated dose (MTD) defined by the Equivalent Score (ET) when the trial is completed using Quasi-CRM design (Yuan et al. 2007) and Robust-Quasi-CRM design (Pan et al. 2014)

Usage

select_mtd_RQ_CRM(target, n, y, score, skeleton, mselection = 1)

Value

select_mtd_RQ_CRM() returns a vector to indicate which dose is selected

Arguments

target

the target toxicity score

n

the number of patients treated at each dose level

y

the toxicity score at each dose level

score

the vector weight for ordinal toxicity levels

skeleton

a matrix to provide multiple skeletons with each row presenting a skeleton

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

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

target <- 0.47
score <- c(0, 0.5, 1, 1.5)
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)
n <- c(3, 3, 3, 9, 3, 0)
y <- c(0, 0, 1, 1.333333, 3, 0)

## Example to get the ET score 1 on dose 3
## Assume three patients their corresponding score on the dose 3 is
## 0.5, 0.5 and 0.5. Then we calculate ET score as this:
## (0.5 + 0.5 + 0.5) / 1.5 = 1

## Example to get the ET score 1.333333 on dose 4
## Assume nine patients their corresponding score on the dose 4 is
## 0, 0, 0, 0, 0, 0, 0.5, 0.5 and 1. Then we calculate ET score as this:
## (0 + 0 + 0 + 0 + 0 + 0 + 0.5 + 0.5 + 1) / 1.5 = 1.333333

select_mtd_RQ_CRM(target = target, n = n, y = y, score = score,
                  skeleton = skeletons)