dfpk (version 1.0)

pkpop: Dose finding method PKPOP

Description

A variation of PKLOGIT model, that we call PKPOP, 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 outcomes; TRUE indicates a toxicity, FALSE otherwise.

auc

The AUC numbers of each patient.

doses

The doses levels of the drug.

lev

A vector of dose levels assigned to patients.

theta

The toxicity (probability) target.

p_0

Skeleton of CRM.

L

A threshold set before starting the trial.

betapriors

A vector of the regression parameters in pkpop model; the default is NULL for this 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, sim

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)