dfpk (version 2.0.0)

pkpop: Dose finding method PKPOP.

Description

The PKPOP model is a variation of the PKLOGIT model which consists of replacing AUCs with AUC of the population (AUCpop). AUCpop is the mean value of the logarithm of AUC at dose k, predicted by the hierarchical model. In other words we replaced the value of patient with the mean value of the population. In this way we passes directly to the population level.

Usage

pkpop(y, auc, doses, lev, theta, p_0, L, betapriors, D_AUC, options)

Arguments

y

A vector of patient's toxicity outcomes; TRUE indicates a toxicity, FALSE otherwise.

auc

The AUC values of each patient.

doses

The dose levels of the drug.

lev

A vector of dose levels assigned to the patients.

theta

The toxicity (probability) target.

p_0

The skeleton of CRM; defaults to NULL. (must be defined only in the PKCRM model)

L

A threshold set before starting the trial; defaults to NULL. (must be defined only in the PKCRM model)

betapriors

A vector of the regression parameters in the model.

D_AUC

A vector specifying the difference between the AUCs and AUC_pop; defaults to NULL.

options

A list of three integers specifying the stan model's 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)

References

Ursino, M., et al, (2016) Dose-finding methods using pharmacokinetics in small populations (under review).

Patterson, S., Francis, S., Ireson, M., Webber, D., and Whitehead, J. (1999) A novel bayesian decision procedure for early-phase dose-finding studies. Journal of Biopharmaceutical Statistics, 9 (4), 583-597.

Whitehead, J., Patterson, S., Webber, D., Francis, S., and Zhou, Y. (2001) Easy-to-implement bayesian methods for dose-escalation studies in healthy volunteers. Biostatistics, 2 (1), 47-61.

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

pklogit, scenarios, nsim, nextDose

Examples

p_0 = NULL
L = NULL
d <- c(12.59972,34.65492,44.69007,60.80685,83.68946,100.37111)
theta <- 0.2      # choice
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)
D_AUC <- c(0, 1.3, -0.34, -2.7,0.39, -2.45)

### Betapriors ###
betapriors = NULL

pkpop(y, AUCs, d, x, theta, p_0, L, betapriors, D_AUC, options)