Description Usage Arguments Value Author(s) References See Also Examples
The PKTOX model is essentially the PKLOGIT model with a probit regression model replacing the logistic regression, that is given by:
p_T(z, \boldsymbol{β}) = Φ(-β_2 + β_3z)
with a bivariate Uniform distribution as prior distribution for the parameters \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 PKTOX 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.
1 2 3 |
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(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. |
A list is returned, consisting of determination of the next recommended dose and estimations of the model. Objects generated by pktox 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. |
Artemis Toumazi artemis.toumazi@gmail.com, Moreno Ursino moreno.ursino@inserm.fr, Sarah Zohar sarah.zohar@inserm.fr
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 clincial trial incorporating pharmacodynamic endpoints. Journal of Biopharmaceutical Statistics, 17 (6), 1117-1129.
pklogit
, sim.data
, nsim
, nextDose
1 2 3 4 5 6 7 8 9 10 11 | ## 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 <- pktox(y, AUCs, doses, x, theta, options = options)
## End(Not run)
|
Loading required package: Rcpp
Loading required package: rstan
Loading required package: ggplot2
Loading required package: StanHeaders
rstan (Version 2.17.3, GitRev: 2e1f913d3ca3)
For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores()).
To avoid recompilation of unchanged Stan programs, we recommend calling
rstan_options(auto_write = TRUE)
SAMPLING FOR MODEL 'reg_auc' NOW (CHAIN 1).
Gradient evaluation took 1.3e-05 seconds
1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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SAMPLING FOR MODEL 'reg_auc' NOW (CHAIN 2).
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SAMPLING FOR MODEL 'cdf_reg_pktox' NOW (CHAIN 1).
Gradient evaluation took 1.3e-05 seconds
1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
Adjust your expectations accordingly!
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Elapsed Time: 0.078913 seconds (Warm-up)
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SAMPLING FOR MODEL 'cdf_reg_pktox' NOW (CHAIN 2).
Gradient evaluation took 6e-06 seconds
1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
Adjust your expectations accordingly!
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Elapsed Time: 0.087771 seconds (Warm-up)
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0.178438 seconds (Total)
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