ewoc_d1extended {ewoc} | R Documentation |
Escalation With Overdose Control
Description
Finding the next dose for a phase I clinical trial based on the Escalation with Overdose Control (EWOC) design considering the extended parametrization for binary response and single agent.
Usage
ewoc_d1extended(
formula,
theta,
alpha,
rho_prior,
min_dose,
max_dose,
type = c("continuous", "discrete"),
first_dose = NULL,
last_dose = NULL,
dose_set = NULL,
max_increment = NULL,
no_skip_dose = TRUE,
rounding = c("down", "nearest"),
n_adapt = 5000,
burn_in = 1000,
n_mcmc = 1000,
n_thin = 1,
n_chains = 1
)
Arguments
formula |
an object of class |
theta |
a numerical value defining the proportion of expected patients to experience a medically unacceptable, dose-limiting toxicity (DLT) if administered the MTD. |
alpha |
a numerical value defining the probability that the dose selected by EWOC is higher than the MTD. |
rho_prior |
a matrix 3 x 2 of hyperparameters for the Beta prior distribution associated with each parameter rho. Each row corresponds to a parameter. |
min_dose |
a numerical value defining the lower bound of the support of the MTD. |
max_dose |
a numerical value defining the upper bound of the support of the MTD. |
type |
a character describing the type of the Maximum Tolerable Dose (MTD) variable. |
first_dose |
a numerical value for the first allowable dose in the trial. |
last_dose |
a numerical value for the last allowable dose in the trial. |
dose_set |
a numerical vector of allowable doses in the trial. It is only necessary if type = "discrete". |
max_increment |
a numerical value indicating the maximum increment from the current dose to the next dose. It is only applied if type = 'continuous'. |
no_skip_dose |
a logical value indicating if it is allowed to skip doses. It is only necessary if type = 'discrete'. The default is TRUE. |
rounding |
a character indicating how to round a continuous dose to the one of elements of the dose set. It is only necessary if type = "discrete". |
n_adapt |
the number of iterations for adaptation.
See |
burn_in |
the number of iterations before to start monitoring. |
n_mcmc |
the number of iterations to monitor. |
n_thin |
thinning interval for monitors. |
n_chains |
the number of parallel chains for the model. |
Value
next_dose
the next recommend dose.
mtd
a numerical vector for the posterior MTD distribution considering the next patient covariable.
rho
a matrix for the posterior rho_0 and rho_1 distributions.
sample
a list of the MCMC chains distribution.
trial
a list of the trial conditions.
References
Tighiouart, M., Cook-Wiens, G., & Rogatko, A. (2018). A Bayesian adaptive design for cancer phase I trials using a flexible range of doses. Journal of biopharmaceutical statistics, 28(3), 562-574.
Examples
DLT <- 0
dose <- 20
test <- ewoc_d1extended(DLT ~ dose, type = 'discrete',
theta = 0.33, alpha = 0.25,
dose_set = seq(20, 100, 20),
min_dose = 20, max_dose = 100,
rho_prior = matrix(1, ncol = 2, nrow = 2),
rounding = "nearest")
summary(test)
plot(test)