crm is used to compute a dose for the next patient in a phase I
trial according to the CRM.
crm(prior, target, tox, level, n = length(level), dosename = NULL,
include = 1:n, pid = 1:n, conf.level = 0.9, method = "bayes",
model = "empiric", intcpt = 3, scale = sqrt(1.34), model.detail = TRUE,
patient.detail = TRUE, var.est = TRUE)A vector of initial guesses of toxicity probabilities associated the doses.
The target DLT rate.
A vector of patient outcomes; 1 indicates a toxicity, 0 otherwise.
A vector of dose levels assigned to patients. The length
of level must be equal to that of tox.
The number of patients enrolled.
A vector containing the names of the regimens/doses
used. The length of dosename must be equal to that of
prior.
A subset of patients included in the dose calculation.
Patient ID provided in the study. Its length must be equal
to that of level.
Confidence level for the probability/confidence interval of the returned dose-toxicity curve.
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".
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".
The intercept of the working logistic model. The
default is 3. If model="empiric", this argument will be
ignored.
Standard deviation of the normal prior of the model parameter. Default is sqrt(1.34).
If FALSE, the model content of an "mtd" object
will not be displayed. Default is TRUE.
If FALSE, patient summary of an "mtd" object
will not be displayed. Default is TRUE.
If TRUE, variance of the estimate of the model parameter and probability/confidence interval for the dose-toxicity curve will be computed
An object of class "mtd" is returned, consisting of the summary
of dose assignments thus far and the recommendation of dose for the
next patient.
Initial guesses of toxicity rates.
The target probability of toxicity at the MTD.
Updated estimates of toxicity rates.
Lower confidence/probability limits of toxicity rates.
Upper confidence/probability limits of toxicity rates.
The updated estimate of the MTD.
The variance of the normal prior.
The posterior variance of the model parameter.
Estimate of the model parameter.
The method of estimation.
The working model.
The scaled doses obtained via backward substitution.
Patients' toxicity indications.
Dose levels assigned to patients.
For maximum likelihood estimation, the variance of the estimate of
\(\beta\) (post.var) is approximated by the posterior variance of
\(\beta\) with a dispersed normal prior.
The empiric model is specified as \(F(d, \beta) = d^{\exp(\beta)}\).
The logistic model is specified as logit \((F(d,\beta))\) = intcpt
\(+ \exp(\beta) \times d\). For method="bayes", the prior on
\(\beta\) is normal with mean 0. Exponentiation of \(\beta\) ensures an
increasing dose-toxicity function.
O'Quigley, J. O., Pepe, M., and Fisher, L. (1990). Continual reassessment method: A practical design for phase I clinical trials in cancer. Biometrics 46:33-48.
Cheung, Y. K. (2011). Dose Finding by the Continual Reassessment Method. New York: Chapman & Hall/CRC Press.
# NOT RUN {
# Create a simple data set
prior <- c(0.05, 0.10, 0.20, 0.35, 0.50, 0.70)
target <- 0.2
level <- c(3, 4, 4, 3, 3, 4, 3, 2, 2, 2)
y <- c(0, 0, 1, 0, 0, 1, 1, 0, 0, 0)
foo <- crm(prior, target, y, level)
ptox <- foo$ptox # updated estimates of toxicity rates
# }
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