R/stats-predprob.r

In ph2bayes: Bayesian Single-Arm Phase II Designs

#' The predictive probability criterion function
#'
#' Lee and Liu's criterion function for determining the
#' trial decision cutoffs based on the predictive probability.
#'
#' @usage
#' predprob(y, n, nmax, alpha_e, beta_e, p_s, theta_t)
#' @param y the number of responses among \eqn{n} patients treated by the experimental drug at a certain stage of the trial.
#' @param n the number of patients treated by the experimental drug at a certain stage of the trial.
#' @param nmax the maximum number of patients treated by the experimental drug.
#' @param alpha_e the hyperparameter (shape1) of the Beta prior for the experimental drug.
#' @param beta_e the hyperparameter (shape2) of the Beta prior for the experimental drug.
#' @param p_s the the response rate for the standard drug.
#' @param theta_t the prespecified target probability; tipically, \eqn{\theta_T = [0.85, 0.95]}.
#' @return
#' \item{prob}{the predictive probability: \eqn{PP = \sum_{x=0}^{n_{max}-n} P(x | y) I(\Pr(p_E > p_S | y, x) \geq \theta_T) }}
#' @references
#' Lee, J. J., Liu, D. D. (2008).
#' A predictive probability design for phase II cancer clinical trials.
#' \emph{Clinical Trials} \strong{5}: 93-106.
#'
#' Yin, G. (2012).
#' \emph{Clinical Trial Design: Bayesian and Frequentist Adaptive Methods.}
#' New York: Wiley.
#' @examples
#' # p. 97, PP = 0.5656
#' predprob(16, 23, 40, 0.6, 0.4, 0.6, 0.9)
#' @export
predprob <- function(y, n, nmax, alpha_e, beta_e, p_s, theta_t) {

  return(predprobCpp(y, n, nmax, alpha_e, beta_e, p_s, theta_t))

}