R/cp.R

In AGSDest: Estimation in Adaptive Group Sequential Trials

#' \code{cp} is a function that computes the conditional power of a GSD.
#' 
#' @title coditional power of a GSD
#' @param GSD object of the \code{class} \code{GSTobj} or list with the following elements: K = number of stages, a = vector with futility boundaries (not supported yet), b = rejection boundaries, t = vector with information fractions, Imax = maximum information number, delta =  effect size used for planning the trial; see example blow.
#' @return \code{cp} returns the conditional power of a GSD.  
#' @references
#' O'Brien, PC, Fleming, TR (1979) ''A multiple testing procedure for clinical trials'',
#' \emph{Biometrics}, 35 , 549-556 \cr
#' Schoenfeld, D (2001) ''A simple Algorithm for Designing Group Sequential Clinical Trials'',
#' \emph{Biometrics}, 27, 972-974 
#' @author Niklas Hack \email{niklas.hack@@meduniwien.ac.at} and Werner Brannath \email{werner.brannath@@meduniwien.ac.at}
#' @seealso \code{\link{GSTobj}}
#' @examples
#' ##The following calculates the conditional power of a GSD. 
#' GSD <- list(K=4,a=rep(-8,4),b=c(4.333,2.963,2.359,2.014),t=c(0.25,0.5,0.75,1),Imax=0.22,delta=4)
#' cp(GSD)
#' @keywords methods
#' @export
cp <- function(GSD) {
    if(GSD$K > 1) seqmon(a=GSD$a[1:GSD$K], b=pbounds(h=GSD$delta, pT=list(t=GSD$t, b=GSD$b, Imax=GSD$Imax), iD=list(T=0)), t=GSD$t[1:GSD$K], int=500*rep.int(1, GSD$K))[2*GSD$K]
    else NULL
}