crmsim: CRM Simulator
In dfcrm: Dose-Finding by the Continual Reassessment Method

Description Usage Arguments Value References See Also Examples

View source: R/dfcrm.R

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

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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)

`PI`	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.
`n`	Sample size of the trial.
`x0`	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.

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:

`PI`	True toxicity rates.
`prior`	Initial guesses of toxicity rates.
`target`	The target probability of toxicity at the MTD.
`n`	Sample size.
`x0`	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.

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.

crm, titesim.

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)  # summarize trial graphically

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

simulation number: 1 
simulation number: 2 
simulation number: 3 
simulation number: 4 
simulation number: 5 
simulation number: 6 
simulation number: 7 
simulation number: 8 
simulation number: 9 
simulation number: 10 

Number of simulations:	 10 
Patient accrued:	 24 
Target DLT rate:	 0.2 
            1   2   3    4   5    6
Truth    0.10 0.2 0.4 0.50 0.6 0.65
Prior    0.05 0.1 0.2 0.35 0.5 0.70
Selected 0.30 0.6 0.1 0.00 0.0 0.00
Nexpt    7.00 7.2 6.8 2.80 0.2 0.00
Ntox     0.70 1.3 2.5 1.20 0.2 0.00

The trials are generated by a Group CRM starting at dose 3 with group size 2 

Restriction apply to avoid
	 (1) Skipping doses in escalation;
	 (2) Escalation immediately after a toxic outcome.

The working model is empiric 
	ptox = dose^{exp(beta)} with doses = 0.05 0.1 0.2 0.35 0.5 0.7 
	and beta is estimated by its posterior mean 
	assuming a normal prior with mean 0 and variance 1.34