pkcrm | R Documentation |
The PKCRM model is a combination of PKLIM as given below:
z_{i} \vert \boldsymbol{\beta}, \nu \sim N \left( \beta_0 + \beta_1 \log d_{i}, \nu^{2} \right)
where \boldsymbol{\beta} = (\beta_0, \beta_1)
are the regression parameters and \nu
is the standard deviation,
and the Continual Reassessment Method's (CRM) model:
p_T(d_k, \beta) = d^\beta_k(CRM)
The default choices of the priors are:
\boldsymbol{\beta} \vert \nu \sim N(m, \nu*beta0),
\nu \sim Beta(1,1),
m = (-log(CL_{pop}), 1)
where Cl_{pop}
is the population clearance.
where default choices are Cl_{pop} = 10
and beta0 = 10000. Therefore, the default choices for model's priors are given by
betapriors = c(Cl_{pop} = 10, beta0 = 10000)
For the CRM model:
Skeleton CRM = (0.01, 0.05, 0.1, 0.2, 0.35, 0.45) and
\beta \sim N(0, 1.34)
Finally, the PKCRM 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.
pkcrm(y, auc, doses, x, theta, p0, L, prob = 0.9, options = list(nchains = 4,
niter = 4000, nadapt = 0.8), betapriors = c(10, 10000), thetaL=NULL,
deltaAUC = NULL, CI = TRUE)
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( |
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 pkcrm 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>.
Patterson, S., Francis, S., Ireson, M., Webber, D., and Whitehead, J. (1999) A novel bayesian decision procesure 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.
sim.data
, nsim
, nextDose
## Not run:
p0 <- c(.01,.05,.1,.2,.35,0.45) # Skeleton of CRM
L <- log(15.09) # Threshold set
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 <- pkcrm(y, AUCs, doses, x, theta, p0, L, options = options)
## End(Not run)
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