crmsim: CRM Simulator

Description

crmsim is used to generate simulation replicates of phase I trial using the (group) CRM under a specified dose-toxicity configuration.

Usage

crmsim(PI, prior, target, n, x0, nsim = 1, mcohort = 1, restrict = TRUE, 
    count = TRUE, method = "bayes", model = "empiric", intcpt = 3, 
    scale = sqrt(1.34), seed = 1009)

Arguments

A vector of the true toxicity probabilites associated with the doses.

prior

A vector of initial guesses of toxicity probabilities associated with the doses. Must be of same length as PI.

target

The target DLT rate.

Sample size of the trial.

The initial design. For one-stage TITE-CRM, it is a single numeric value indicating the starting dose. For two-stage TITE-CRM, it is a non-decreasing sequence of dose levels of length n.

nsim

The number of simulations. Default is set at 1.

mcohort

The number of patients enrolled before the next model-based update. Default is set at 1, i.e., a fully sequential update.

restrict

If TRUE, restrictions apply during the trials to avoid (1) skipping doses in escalation and (2) escalation immediately after a toxic outcome (i.e., incoherent escalation). If FALSE, dose assignments are purely model-based.

count

If TRUE, the number of the current simulation replicate will be displayed.

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).

seed

Seed of the random number generator.

Value

An object of class ``sim'' is returned, consisting of the operating characteristics of the design specified. The time component of the design is suppressed for the CRM simulator. All ``sim'' objects generated by crmsim contain at least the following components:

True toxicity rates.

prior

Initial guesses of toxicity rates.

target

The target probability of toxicity at the MTD.

Sample size.

The initial design.

MTD

Distribution of the MTD estimates. If nsim=1, this is a single numeric value of the recommended MTD of in simulated trial.

level

Average number of patients treated at the test doses. If nsim=1, this is a vector of length n indicating the doses assigned to the patients in the simulated trial.

tox

Average number of toxicities seen at the test doses. If nsim=1, this is a vector of length n indicating the toxicity outcomes of the patients in the simulated trial.

beta.hat

The estimates of the model parameter throughout the simulated trial(s). The dose assignment of the jth patient in each trial corresponds to the jth element in each row.

final.est

The final estimates of the model parameter of the simulated trials.

References

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. (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.

Examples

Run this code

# NOT RUN {
PI <- c(0.10, 0.20, 0.40, 0.50, 0.60, 0.65)
prior <- c(0.05, 0.10, 0.20, 0.35, 0.50, 0.70)
target <- 0.2
x0 <- c(rep(1,3), rep(2,3), rep(3,3), rep(4,3), rep(5,3), rep(6,9))

# Generate a single replicate of two-stage group CRM trial of group size 3
foo <- crmsim(PI, prior, target, 24, x0, mcohort=3)
# }
# NOT RUN {
plot(foo,ask=T)
# }
# NOT RUN {
  # summarize trial graphically

# Generate 10 replicates of CRM trial with 24 subjects
foo10 <- crmsim(PI, prior, target, 24, 3, nsim=10, mcohort=2)
foo10
# }