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

Description Usage Arguments Value References See Also Examples

View source: R/dfcrm.R

titesim is used to generate simulation replicates of phase I trial using the TITE-CRM under a specified dose-toxicity configuration.

titesim(PI, prior, target, n, x0, nsim = 1, restrict = TRUE, obswin = 1,
tgrp = obswin, rate = 1, accrual = "fixed", surv = "uniform", scheme =
"linear", 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.
`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.
`obswin`	The observation window with respect to which the MTD is defined.
`tgrp`	The minimum waiting time between two dose cohorts at the initial stage. Default is set as `obswin`, i.e., complete follow-up in all current patients is required before escalation to the next dose group. This argument is used only in two-stage TITE-CRM.
`rate`	Patient arrival rate: Expected number of arrivals per observation window. Example: obswin=6 and rate=3 means expecting 3 patients arrive in 6 time units.
`accrual`	Patient accrual scheme. Default is “fixed” whereby inter-patient arrival is fixed. Alternatively, use “poisson” to simulate patient arrivals by the Poisson process.
`surv`	Distribution for time-to-toxicity. Default is “uniform” where toxicity, if occurs, occurs uniformly on the interval [0,`obswin`]. Other survival distributions including exponential and Weibull are to be made available.
`scheme`	A character string to specify the method for assigning weights. Default is “linear”. An adaptive weight is specified by “adaptive”.
`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.

For a “sim” object with nsim=1, the time component of individual subjects in the simulated trial is available via the values arrival, toxicity.time, and toxicity.study.time which respectively contain patients' arrival times, times-to-toxicity, and the times-to-toxicity per study time.

For a “sim” object with nsim>1, the time component of the design is summarized via the value Duration, which is the duration of the simulated trials, computed by adding the arrival time of the last patient and obswin.

All “sim” objects 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.

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. (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.

crmsim, titecrm.

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 TITE-CRM trial of size 24
foo <- titesim(PI, prior, target, 24, x0, obswin=6, rate=4, accrual="poisson")
## Not run: plot(foo, ask=T)  # summarize trial graphically

# Generate 10 replicates of TITE-CRM trial of size 24
foo10 <- titesim(PI, prior, target, 24, 3, nsim=10, obswin=6, rate=4, accrual="poisson")

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 0.10 0.6 0.3 0.00 0.0 0.00
Nexpt    6.40 8.5 7.2 1.70 0.2 0.00
Ntox     0.40 1.1 2.6 1.20 0.1 0.00

The distribution of trial duration:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  34.89   38.31   44.14   43.12   47.54   52.04 

The trials are generated by a TITE-CRM starting at dose 3 

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 

The linear function is used to assign weights to patients.

Patient arrival is modeled as a poisson process
	with rate 4 patients per 6 time units (= observation window).