pkcov: Dose finding method PKCOV.

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

Description

The PKCOV model is a modification of the method proposed by Piantadosi and Liu (1996) who suggested to use the AUC as covariate for p_T, probability of toxicity, through the logit link. Therefore, the dose-toxicity model is:

logit(p_T(d_k, Δ {z_d}_k, \boldsymbol{β})) = -β_0 + β_1 log(d_k) + β_2 Δ {z_d}_k

where \boldsymbol{β} = (β_1, β_2), β_0 is a constant with β_0 = -beta0mean,

β_1 \sim U(l_1, u_1),

β_2 \sim U(0,5),

beta1mean = c(l_1, u_1)

where default choices are beta0mean = -14.76, beta1mean = c(0, 8.23) and Δ {z_d}_k is the difference between the logarithm of population AUC at dose d_k and z, the logarithm of AUC of the subject at the same dose. Therefore, the default choices for model's priors are given by

betapriors = c(beta0mean = -14.76, l_1 = 0, u_1 = 8.23)

Finally, the PKCOV 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

pkcov(y,auc,doses,x,theta, deltaAUC, prob = 0.9, options=list(nchains = 4,
      niter = 4000, nadapt = 0.8), betapriors = c(-14.76, 0, 3.23+5), 
      thetaL = 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 = -14.76 and beta1mean = c(0, 8.23).
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 pkcov 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>.

Piantadosi, S. and Liu, G. (1996) Improved designs for dose escalation studies using pharmacokinetic measurements. Statistics in Medicine, 15 (15), 1605-1618.

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
        AUCs <-  c(0.43, 1.4, 5.98, 7.98, 11.90, 3.45)
        x <- c(1,2,3,4,5,6)
        y <- c(FALSE,FALSE,FALSE,FALSE,TRUE,FALSE)
        deltaAUC <- c(0, 1.3, -0.34, -2.7, 0.39, -2.45)
        options <- list(nchains = 2, niter = 4000, nadapt = 0.8)
        res <- pkcov(y, AUCs, doses, x, theta, deltaAUC, options=options)
    
## End(Not run)

dfpk documentation built on May 2, 2019, 8:31 a.m.