titecrm: Executing the TITE-CRM
In dfcrm: Dose-Finding by the Continual Reassessment Method

Description Usage Arguments Details Value References See Also Examples

View source: R/dfcrm.R

titecrm is used to compute a dose for the next patient in a phase I trial according to the TITE-CRM.

titecrm(prior, target, tox, level, n = length(level), weights = NULL,
followup = NULL, entry = NULL, exit = NULL, obswin = NULL, 
scheme = "linear", conf.level = 0.9, dosename = NULL, include = 1:n, 
pid = 1:n, method = "bayes", model = "empiric", var.est = TRUE, 
scale = sqrt(1.34), intcpt = 3, model.detail = TRUE, patient.detail = TRUE, 
tite = 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.
`weights`	A vector of weights assigned to observations. A weight must be between 0 and 1. If given, the arguments `followup`, `entry`, `exit`, `obswin`, and `scheme` will be ignored. If not supplied, users must provide follow-up information via the argument `followup` or `entry` and `exit`, as well as the observation window `obswin`. The length of `weights` must be equal to that of `tox`.
`followup`	A vector of follow-up times of patients. If given, the arguments `entry` and `exit` will be ignored.
`entry`	A vector of entry times of the patients.
`exit`	A vector of exit times of the patients due to either end of follow-up or toxicity.
`obswin`	The observation window with respect to which the MTD is defined. If not supplied, users must provide `weights`.
`scheme`	A character string to specify the method for assigning weights. Default is “linear”. An adaptive weight function is specified by “adaptive”.
`conf.level`	Confidence level for the probability/confidence interval of the returned dose-toxicity curve.
`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`.
`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”.
`var.est`	If TRUE, variance of the estimate of the model parameter and probability/confidence interval for the dose-toxicity curve will be computed.
`scale`	Standard deviation of the normal prior of the model parameter. Default is sqrt(1.34).
`intcpt`	The intercept of the working logistic model. The default is 3. If `model`=“empiric”, this argument will be ignored.
`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.
`tite`	If FALSE, the time components in patient summary of an “mtd” object will be omitted. Default in TRUE.

The adaptive weighting scheme is given in Cheung and Chappell (2000) given in the reference list.

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.
`followup`	Follow-up times of patients.
`obswin`	Observation window with respect to which the MTD is defined.
`weights`	Weights assigned to patients.
`entry`	Entry times of patients.
`exit`	Exit times of patients.
`scheme`	Weighting scheme.

Cheung, Y. K. and Chappell, R. (2000). Sequential designs for phase I clinical trials with late-onset toxicities. Biometrics 56:1177-1182.

Cheung, Y. K. (2011). Dose Finding by the Continual Reassessment Method. New York: Chapman & Hall/CRC Press.

crm

# 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, 3, 3, 4, 4, 3, 2, 2, 2, 3)
y <- c(0, 0, 1, 0, 1, 0, 0, 0, 0, 0)
u <- c(178, 181, 168, 181, 24, 181, 179, 102, 42, 3)
tau <- 180
foo <- titecrm(prior, target, y, level, followup=u, obswin=tau)
rec <- foo$mtd  # recommend a dose level for next patient

# An example with adaptive weight
foo2 <- titecrm(prior, target, y, level, followup=u, obswin=tau, scheme="adaptive")
wts <- foo2$weights

# The `weights' argument makes `followup' and `obswin' obsolete
foo3 <- titecrm(prior, target, y, level, weights=wts, followup=u, obswin=tau)
## Not run: plot(foo3, ask=T)

## Patient time information via `entry' and `exit' arguments
# entry time (days since study begins)
entry <- c(7, 29, 49, 76, 92, 133, 241, 303, 363, 402)
# exit time (days since study begins)
exit <- c(185, 210, 217, 257, 116, 314, 420, 405, 405, 405)
foo4 <- titecrm(prior, target, y, level, exit=exit, entry=entry, obswin=tau)
## Not run: plot(foo4, ask=T)