titesim {dfcrm} | R Documentation |
TITE-CRM Simulator
Description
titesim
is used to generate simulation replicates of phase I
trial using the TITE-CRM under a specified dose-toxicity
configuration.
Usage
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)
Arguments
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 |
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
|
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 |
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, |
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 |
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.
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 |
level |
Average number of patients treated at the test doses. If
|
tox |
Average number of toxicities seen at the test doses. If
|
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
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.
See Also
Examples
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