dtox: Dose finding method DTOX.

In artemis-toumazi/dfpk: Bayesian Dose-Finding Designs using Pharmacokinetics (PK) for Phase I Clinical Trials

Dose finding method DTOX.

Description

The DTOX model enables us to estimate posterior probability of toxicity p_T versus dose directly. The dose-toxicity model is given by:

p_T(d_k,\boldsymbol{\beta}) = \Phi(-\beta_0 + \beta_1 log(d_k))

where \beta_q \sim U(l_q, u_q) \forall q = 0,1 and

beta0mean = c(l_0, u_0),

beta1mean = c(l_1, u_1)

where default choices are beta0mean = c(0, 16.71) and beta1mean = c(0, 6.43). So the default choices for model's priors are given by

betapriors = c(l_0 = 0, u_0 = 16.71, l_1 = 0, u_1 = 6.43)

Finally, the DTOX model has the following stopping rule in toxicity: if

P(p_T(dose) > theta) > prob

then, no dose is suggested and the trial is stopped.

Usage

dtox(y, doses, x, theta, prob = 0.9, options=list(nchains = 4, niter = 4000, 
     nadapt = 0.8), betapriors = c(0, 16.71, 0, 6.43), thetaL = NULL, 
     auc = NULL, deltaAUC = NULL, p0 = NULL, L = NULL, CI = TRUE)

Arguments

y	A binary vector of patient's toxicity outcomes; TRUE indicates a toxicity, FALSE otherwise.
doses	A vector with the doses panel.
x	A vector with the dose level assigned to the patients.
theta	The toxicity target.
prob	The threshold of the posterior probability of toxicity for the stopping rule; defaults to 0.9.
betapriors	A vector with the value for the prior distribution of the regression parameters in the model; defaults to betapriors = c(beta0mean, beta1mean), where beta0mean = c(0, 16.71) and beta1mean = c(0, 6.43).
options	A list with the Stan model's options; the number of chains, how many iterations for each chain and the number of warmup iterations; defaults to options = list(nchains = 4, niter = 4000, nadapt = 0.8).
auc	A vector with the computed AUC values of each patient for pktox, pkcrm, pklogit and pkpop; defaults to NULL.
deltaAUC	The difference between computed individual AUC and the AUC of the population at the same dose level (defined as an average); argument for pkcov; defaults to NULL.
p0	The skeleton of CRM for pkcrm; defaults to NULL (must be defined only in the PKCRM model).
L	The AUC threshold to be set before starting the trial for pklogit, pkcrm and pktox; defaults to NULL (must be defined only in the PKCRM model).
thetaL	A second threshold of AUC; must be defined only in the PKCRM model.
CI	A logical constant indicating the estimated 95% credible interval; defaults to TRUE.

Value

A list is returned, consisting of determination of the next recommended dose and estimations of the model. Objects generated by dtox contain at least the following components:

newDose	The next maximum tolerated dose (MTD); equals to "NA" if the trial has stopped before the end, according to the stopping rules.
pstim	The mean values of estimated probabilities of toxicity.
p_sum	The summary of the estimated probabilities of toxicity if CI = TRUE, otherwise is NULL.
parameters	The estimated model's parameters.

Author(s)

Artemis Toumazi [e-mail], Moreno Ursino [e-mail], Sarah Zohar [e-mail]

References

Ursino, M., et al, (2017) Dose-finding methods for Phase I clinical trials using pharmacokinetics in small populations, Biometrical Journal, <doi:10.1002/bimj.201600084>.

Toumazi, A., et al, (2018) dfpk: An R-package for Bayesian dose-finding designs using pharmacokinetics (PK) for phase I clinical trials, Computer Methods and Programs in Biomedicine, <doi:10.1016/j.cmpb.2018.01.023>.

See Also

sim.data, nsim, nextDose

Examples

    ## Not run: 
        doses <- c(12.59972,34.65492,44.69007,60.80685,83.68946,100.37111)
        theta <- 0.2
        options <- list(nchains = 2, niter = 4000, nadapt = 0.8)
        x <- c(1,2,3,4,5,6)
        y <- c(FALSE,FALSE,FALSE,FALSE,TRUE,FALSE)

        res <- dtox(y, doses, x, theta, options = options)
    
## End(Not run)

artemis-toumazi/dfpk documentation built on Sept. 5, 2023, 2:44 a.m.