titecrm {dfcrm}

Executing the TITE-CRM

Description

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

Usage

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) 

Arguments

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.

Details

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

Value

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.

References

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.

See Also

crm

Examples

# 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)