crm {dfcrm} | R Documentation |
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)
prior |
A vector of initial guesses of toxicity probabilities associated the doses. |
target |
The target DLT rate. |
tox |
A vector of patient outcomes; 1 indicates a toxicity, 0 otherwise. |
level |
A vector of dose levels assigned to patients. The length
of level must be equal to that of tox . |
n |
The number of patients enrolled. |
dosename |
A vector containing the names of the regimens/doses
used. The length of dosename must be equal to that of
prior . |
include |
A subset of patients included in the dose calculation. |
pid |
Patient ID provided in the study. Its length must be equal
to that of level . |
conf.level |
Confidence level for the probability/confidence interval of the returned dose-toxicity curve. |
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 model =``empiric'', this argument will be
ignored. |
scale |
Standard deviation of the normal prior of the model parameter. Default is sqrt(1.34). |
model.detail |
If FALSE, the model content of an ``mtd'' object will not be displayed. Default is TRUE. |
patient.detail |
If FALSE, patient summary of an ``mtd'' object will not be displayed. Default is TRUE. |
var.est |
If TRUE, variance of the estimate of the model parameter and probability/confidence interval for the dose-toxicity curve will be computed |
For maximum likelihood estimation, the variance of the estimate of
$β$ (post.var
) is approximated by the posterior variance of
$β$ with a dispersed normal prior.
The empiric model is specified as $F(d, β) = d^{exp(β)}$.
The logistic model is specified as logit$(F(d,β))$ = intcpt
$+ exp(β) times d$. For method=bayes
, the prior on
$β$ is normal with mean 0. Exponentiation of $β$ ensures an
increasing dose-toxicity function.
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.
prior |
Initial guesses of toxicity rates. |
target |
The target probability of toxicity at the MTD. |
ptox |
Updated estimates of toxicity rates. |
ptoxL |
Lower confidence/probability limits of toxicity rates. |
ptoxU |
Upper confidence/probability limits of toxicity rates. |
mtd |
The updated estimate of the MTD. |
prior.var |
The variance of the normal prior. |
post.var |
The posterior variance of the model parameter. |
estimate |
Estimate of the model parameter. |
method |
The method of estimation. |
model |
The working model. |
dosescaled |
The scaled doses obtained via backward substitution. |
tox |
Patients' toxicity indications. |
level |
Dose levels assigned to patients. |
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.
# 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