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#'Escalation With Overdose Control
#'
#'Finding the next dose for a phase I clinical trial based on the
#'Escalation with Overdose Control (EWOC) design considering the
#'classical parametrization for binary responses and single agent.
#'
#'@param formula an object of class \code{\link[Formula]{Formula}}: a symbolic
#'description of the model to be fitted with only one regressor term
#'corresponding to the dose for the right side and a numeric vector a response
#'containing number of DLT for the left side.
#'@param type a character describing the type of the Maximum Tolerable Dose
#'(MTD) variable.
#'@param theta a numerical value defining the proportion of expected patients
#'to experience a medically unacceptable, dose-limiting toxicity (DLT) if
#'administered the MTD.
#'@param alpha a numerical value defining the probability that the dose selected
#'by EWOC is higher than the MTD.
#'@param mtd_prior a matrix 1 x 2 of hyperparameters for the Beta prior
#'distribution associated with the parameter MTD.
#'@param rho_prior a matrix 1 x 2 of hyperparameters for the Beta prior distribution
#'associated with the parameter rho.
#'@param min_dose a numerical value defining the lower bound of the support of
#'the MTD.
#'@param max_dose a numerical value defining the upper bound of the support of
#'the MTD.
#'@param first_dose a numerical value for the first allowable dose in the trial.
#'It is only necessary if type = 'continuous'.
#'@param last_dose a numerical value for the last allowable dose in the trial.
#'It is only necessary if type = 'continuous'.
#'@param dose_set a numerical vector of allowable doses in the trial.
#'It is only necessary if type = 'discrete'.
#'@param max_increment a numerical value indicating the maximum increment from the current dose to the next dose.
#'It is only applied if type = 'continuous'.
#'@param 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.
#'@param 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'.
#'@param n_adapt the number of iterations for adaptation.
#'See \code{\link[rjags]{adapt}} for details.
#'@param burn_in numerical value indicating the number of iterations before to start monitoring.
#'@param n_mcmc numerical value indicating the number of iterations to monitor.
#'@param n_thin numerical value corresponding to the thinning interval for monitors.
#'@param n_chains numerical value indicating the number of parallel chains for the model.
#'
#'@return \code{next_dose} the next recommend dose.
#'@return \code{mtd} the posterior MTD distribution.
#'@return \code{rho} the posterior rho_0 distribution.
#'@return \code{sample} a list of the MCMC chains distribution.
#'@return \code{trial} a list of the trial conditions.
#'
#'@references Babb, J., Rogatko, A. and Zacks, S., 1998.
#'Cancer phase I clinical trials: efficient dose escalation with overdose
#'control. Statistics in medicine, 17(10), pp.1103-1120.
#'
#'@examples
#'DLT <- 0
#'dose <- 20
#'test <- ewoc_d1classical(DLT ~ dose, type = 'discrete',
#' theta = 0.33, alpha = 0.25,
#' min_dose = 20, max_dose = 100,
#' dose_set = seq(20, 100, 20),
#' rho_prior = matrix(1, ncol = 2, nrow = 1),
#' mtd_prior = matrix(1, ncol = 2, nrow = 1),
#' rounding = "nearest")
#'summary(test)
#'plot(test)
#'
#'@import stats
#'
#'@export
ewoc_d1classical <- function(formula, theta, alpha,
mtd_prior, 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) {
formula <- Formula::Formula(formula)
if (class(formula)[2] != "formula")
stop("Invalid formula! \n")
data_base <- model.frame(formula, na.action = na.exclude,
drop.unused.levels = FALSE)
dose_matrix <- model.matrix(formula, data_base, rhs = 1)
if (length(formula)[2] == 1){
design_matrix <- dose_matrix
colnames(design_matrix) <- c("intercept", "dose")
} else {
stop("This design cannot accommodate a covariable.")
}
response <- model.response(data_base)
if (length(type) > 1 | !(type == "continuous" | type == "discrete"))
stop("'type' should be either 'continuous' or 'discrete'.")
if (type == "discrete") {
if (is.null(dose_set))
stop("'dose_set' should be informed for type = 'discrete'.")
if (length(rounding) > 1 | !(rounding == "down" | rounding == "nearest"))
stop("'rounding' should be either 'down' or 'nearest'.")
}
if (!(alpha > 0 & alpha < 1))
stop("'alpha' should be in the interval (0, 1).")
if (!(theta > 0 & theta < 1))
stop("'theta' should be in the interval (0, 1).")
if (nrow(mtd_prior) != 1 | ncol(mtd_prior) != 2)
stop(paste0("'mtd_prior' should be a matrix with 2 columns and 1 row."))
if (nrow(rho_prior) != 1 | ncol(rho_prior) != 2)
stop(paste0("'rho_prior' should be a matrix with 2 columns and 1 row."))
limits <- limits_d1nocov(first_dose = first_dose, last_dose = last_dose,
min_dose = min_dose, max_dose = max_dose,
type = type, rounding = rounding,
dose_set = dose_set)
if (is.null(max_increment))
max_increment <- limits$last_dose - limits$first_dose
current_dose <- design_matrix[nrow(design_matrix), 2]
if (type == "continuous"){
if (current_dose < min_dose | current_dose > max_dose)
stop("The first patient is receiving a dose outside of the dose boundaries given by
'min_dose' and 'max_dose'.")
} else {
if (!(current_dose %in% dose_set))
stop("The first patient is receiving a dose outside of the dose set")
}
design_matrix[, 2] <-
standard_dose(dose = design_matrix[, 2],
min_dose = limits$min_dose,
max_dose = limits$max_dose)
my_data <- list(response = response, design_matrix = design_matrix,
theta = theta, alpha = alpha, limits = limits,
dose_set = dose_set,
max_increment = max_increment,
no_skip_dose = no_skip_dose,
current_dose = current_dose,
rho_prior = rho_prior, mtd_prior = mtd_prior,
type = type[1], rounding = rounding)
class(my_data) <- c("ewoc_d1classical", "d1classical")
my_data$mcmc <- jags(my_data, n_adapt, burn_in, n_mcmc, n_thin, n_chains)
out <- next_dose(my_data)
design_matrix[, 2] <-
inv_standard_dose(dose = design_matrix[, 2],
min_dose = limits$min_dose,
max_dose = limits$max_dose)
trial <- list(response = response, design_matrix = design_matrix,
theta = theta, alpha = alpha,
first_dose = limits$first_dose, last_dose = limits$last_dose,
min_dose = limits$min_dose, max_dose = limits$max_dose,
dose_set = dose_set,
max_increment = max_increment,
no_skip_dose = no_skip_dose,
rho_prior = rho_prior, mtd_prior = mtd_prior,
type = type, rounding = rounding,
n_adapt = n_adapt, burn_in = burn_in, n_mcmc = n_mcmc,
n_thin = n_thin, n_chains = n_chains)
out$trial <- trial
class(out) <- c("ewoc_d1classical", "d1classical")
return(out)
}
#'@importFrom rjags jags.model coda.samples
jags.d1classical <- function(data, n_adapt, burn_in,
n_mcmc, n_thin, n_chains) {
# JAGS model function
jfun <- "model {
for(i in 1:nobs) {
dlt[i] ~ dbin(p[i], 1)
p[i] <- ifelse(1/(1 + exp(-lp[i])) == 1, 0.99, 1/(1 + exp(-lp[i])))
lp[i] <- inprod(design_matrix[i, ], beta)
}
beta[1] <- logit(rho)
beta[2] <- (logit(theta) - logit(rho))/gamma
rho <- theta*v[1]
v[1] ~ dbeta(rho_prior[1, 1], rho_prior[1, 2])
gamma <- v[2]
v[2] ~ dbeta(mtd_prior[1, 1], mtd_prior[1, 2])
}"
data_base <- list('dlt' = data$response,
'design_matrix' = data$design_matrix,
'theta' = data$theta,
'nobs' = length(data$response),
'rho_prior' = data$rho_prior,
'mtd_prior' = data$mtd_prior)
inits <- function() {
v <- rep(NA, 2)
v[1] <- rbeta(1, data$rho_prior[1], data$rho_prior[2])
v[2] <- rbeta(1, data$mtd_prior[1], data$mtd_prior[2])
out <- list(v = v)
return(v)
}
# Calling JAGS
j <- jags.model(textConnection(jfun),
data = data_base,
inits = list(v = inits()),
n.chains = n_chains,
n.adapt = n_adapt)
update(j, burn_in)
sample <- coda.samples(j, variable.names = c("beta", "gamma", "rho"),
n.iter = n_mcmc, thin = n_thin,
n.chains = n_chains)
beta <- sample[[1]][, 1:2]
gamma <- sample[[1]][, 3]
rho <- sample[[1]][, 4]
out <- list(beta = beta, gamma = gamma, rho = rho, sample = sample)
return(out)
}
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