R/nBinomial1Sample.R

In gsDesign: Group Sequential Design

# See gsBinomialExact for help file text which is together with functions documented there
#
# based on code from Marc Schwartz \email{marc_schwartz@@me.com}

# nBinomial1Sample roxy [sinew] ---
#' @export 
#' @rdname gsBinomialExact
#' @importFrom stats qbinom pbinom
# nBinomial1Sample function [sinew] ----
nBinomial1Sample <- function(p0 = 0.90, p1 = 0.95,
                             alpha = 0.025, beta = NULL,
                             n = 200:250, outtype = 1, conservative = FALSE) {

  # Required number of events, given a vector of sample sizes (n)
  # to be considered at the null proportion, for the given alpha
  CritVal <- stats::qbinom(p = 1 - alpha, size = n, prob = p0) + 1
  Alpha <- stats::pbinom(q = CritVal - 1, size = n, prob = p0, lower.tail = FALSE)
  # Get the Power for each n at the alternate hypothesis
  # proportion
  Power <- stats::pbinom(CritVal - 1, n, p1, lower.tail = FALSE)
  bta <- 1 - Power
  if (is.null(beta)){
    beta <- bta
    return(data.frame(p0=p0, p1=p1, alpha=alpha, beta=beta, 
                      n=n, b=CritVal, alphaR=Alpha, Power=Power))
  }
  if (max(Power)< 1-beta) stop("Input sample size insufficient to power trial")
  if (min(Power)>= 1-beta) stop("All input sample sizes produce at least desired power, so minimum required sample size could not be determined")
  # Find the smallest sample size yielding at least the required power
  if (!conservative){SampSize <- min(which(Power >= 1-beta))
  }else SampSize <- max(which(Power < 1-beta)) + 1

  if (outtype == 2) {
    return(data.frame(
      p0 = p0, p1 = p1, alpha = alpha, beta = beta,
      n = n[SampSize], b = CritVal[SampSize],
      alphaR = Alpha[SampSize], Power = Power[SampSize]
    ))
  }
  return(n[SampSize])
}