pklogit: Dose finding method PKLOGIT.

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

Description

The PKLOGIT model, inspired by Whitehead et al. (2007), uses z_i instead of dose d_i as a covariate in a logistic regression model for p_T. Therefore, we have that:

\mbox{logit}(p_T(z, \boldsymbol{β})) = -β_2 + β_3 z

with a bivariate Uniform distribution as prior distribution for \boldsymbol{β} = (β_2, β_3) and the hierarchical model of PK-toxicity for z_i given as:

z_{i} \vert \boldsymbol{β}, ν \sim N ≤ft( β_0 + β_1 \log d_{i}, ν^{2} \right)

where \boldsymbol{β} = (β_0,β_1) are the regression parameters and ν is the standard deviation.

The default choices of the priors are:

\boldsymbol{β} \vert ν \sim N(m, ν*beta0),

ν \sim Beta(1,1),

m = (-log(CL_{pop}), 1),

where Cl_{pop} is the population clearance.

β_2 \sim U(0, beta2mean),

β_3 \sim U(0, beta3mean)

where default choices are Cl_{pop} = 10, beta0 = 10000, beta2mean = 20 and beta3mean = 10. Therefore, the default choices for model's priors are given by

betapriors = c(Cl_{pop} = 10, beta0 = 10000, beta2mean = 20, beta3mean = 10)

Finally, the PKLOGIT 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

pklogit(y, auc, doses, x, theta, prob = 0.9, options = list(nchains = 4, niter = 4000, 
        nadapt = 0.8), betapriors = c(10, 10000, 20, 10), thetaL = NULL, 
        p0=NULL, L=NULL, deltaAUC=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 values for the prior distribution of the regression parameters in the model; defaults to betapriors = c(Cl_{pop}, beta0, beta2mean, beta3mean), where Cl_{pop} = 10, beta0 = 10000, beta2mean = 20 and beta3mean = 10.
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 pklogit 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>.

Whitehead, J., Zhou, Y., Hampson, L., Ledent, E., and Pereira, A. (2007) A bayesian approach for dose-escalation in a phase i clinical trial incorporating pharmacodynamic endpoints. Journal of Biopharmaceutical Statistics, 17 (6), 1117-1129.

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)
        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)

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

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