dfpk (version 2.0.0)

pkcov: Dose finding method PKCOV.

Description

The PKCOV model is a modification of the model proposed by Piantadosi and Liu (1996). We maintained the idea to incorporate pharmacokinetics (PK) as covariate of $p_T$ through the logit link. As the original paper uniform priors have been taken for parameters, independently of each other.

Usage

pkcov(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).

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

See Also

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
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 ###
param_pk <- c(2,10,100)
omega2 <- 0.7
logit <- function(x) log(x/(1-x))   # logit function
xr <- d
yr <- logit(pnorm((log(xr) - log(10.96) - log(param_pk[2]))/omega2))
coeff <- lm(yr ~ log(xr))
beta0mean <- -coeff$coefficients[1]
beta1mean <- coeff$coefficients[2]
betapriors <- c(beta0mean, beta1mean)

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