Description Usage Arguments Details Value References See Also Examples
Returns an initial design that is compatible with the specified CRM setup when used in a two-stage design.
1 2 |
prior |
A vector of initial guesses of toxicity probabilities associated the doses. |
target |
The target DLT rate. |
n |
The sample size of the trial. |
nK |
The minimum number of subjects required at the highest test dose in case of no toxicity throughout the trial. |
method |
A character string to specify the method for parameter estimation. The default method “bayes” estimates the model parameter by the posterior mean. Maximum likelihood estimation is specified by “mle”. |
model |
A character string to specify the working model used in the method. The default model is “empiric”. A one-parameter logistic model is specified by “logistic”. |
intcpt |
The intercept of the working logistic model. The
default is 3. If |
scale |
Standard deviation of the normal prior of the model parameter. Default is sqrt(1.34). |
detail |
If TRUE, intermediate designs will be displayed. |
An initial design will be incompatible to the CRM setup if the
escalation pace is too conservative, i.e. slow. The algorithm in
getinit
starts the search of a compatible design with an
aggressive initial design that starts a trial at the second highest
dose. A more conservative design will be subsequently tested for
compatibility if the current design is compatible. The sequence
returned may be viewed as a conservative compatible initial design.
A non-decreasing sequence of dose levels is returned.
Cheung, Y. K. (2005). Coherence principles in dose-finding studies. Biometrika 92:863-873.
Cheung, Y. K. (2011). Dose Finding by the Continual Reassessment Method. New York: Chapman & Hall/CRC Press.
1 2 3 4 5 6 7 8 | prior <- c(0.05, 0.10, 0.20, 0.35, 0.50, 0.70)
target <- 0.2
# Seach stops because it requires at least 8 subjects at the highest dose
getinit(prior, target, 25, nK=8, method="mle", detail=TRUE)
# Search stops because an incompatible design is reached
getinit(prior, 0.3, 25, nK=8, method="mle", detail=TRUE)
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