titesim2 {titecrm} | R Documentation |
Returns an object of class ``sim'' that generates and summarizes the dose assignments of a simulated trial by a two-stage TITE-CRM.
titesim2(PI, prior, target, n, x0, obswin=1, tgrp=obswin, rate=1, accrual="fixed", surv="uniform", scheme="linear", method="bayes", scale=sqrt(1.34), seed=1099)
PI |
A vector of the true toxicity probabilites associated with the doses. |
prior |
A vector of initial estimates 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 |
A vector of treatment sequence according to the initial
design. Must be of length n . |
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. |
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 arrival 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''. Adaptive weight using Kaplan-Meier ``KM'' is to be made available. |
method |
A character string to specify the method for parameter estimation. The default method ``bayes'' estimates the model parameter by the posterior mean. Estimation using ``mle'' is to be made available. |
scale |
Standard deviation of the normal prior of the model parameter. Default is sqrt(1.34). |
seed |
Seed of the random number generator. |
Dose-toxicity relationship is assumed as an empiric power model $a_i^{exp(β)}$ where $a_i$ is the initial estimate of toxicity probability of dose level i and the model parameter $β$ has a normal prior with mean 0 and scale to be provided by users.
The simulation of a trial run by a two-stage TITE-CRM. Users need to provide an initial sequence before switching the TITE-CRM.
An object of class ``mtd'' is returned, consisting of the summary of dose assignments and the final dose recommendation in a simulated trial.
PI |
True toxicity probabilites. |
prior |
Initial estimates of toxicity probabilities. |
target |
The target probability of toxicity at the MTD. |
recommend |
The recommended dose level for the next patient. |
scale |
The standard deviation of the normal prior. |
estimate |
Estimate of the model parameter. |
level |
Dose levels assigned to patients. |
tox |
Patients' toxicity indications. |
arrival |
Patients' arrival times. |
ttox.pt |
Patients' times-to-toxicity. |
ttox.cal |
Patients' times-to-toxicity on study time. |
obswin |
Observation window with respect to which the MTD is defined. |
weights |
Weights assigned to patients. |
scheme |
Weighting scheme. |
accrual |
Patient's arrival pattern. |
rate |
Rate of patient's arrival. |
surv |
Distribution of time-to-toxicity. |
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.
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 trial of size 24 foo <- titesim2(PI,prior,target,24,x0, obswin=6,rate=4,accrual="poisson") rec <- foo$recommend # recommend a dose level for next patient plot(foo) # summarize trial graphically