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epi.ssninfc <- function(treat, control, sigma, delta, n, power, r = 1, nfractional = FALSE, alpha){
# Stop if a negative value for delta entered:
if (delta < 0){
stop("For a non-inferiority trial delta must be greater than or equal to zero.")
}
z.alpha <- qnorm(1 - alpha, mean = 0, sd = 1)
if (!is.na(treat) & !is.na(control) & !is.na(delta) & !is.na(power) & is.na(n)) {
# delta equals the max absolute tolerable difference between treat and control.
# Make delta negative:
ndelta <- -delta
beta <- (1 - power)
z.beta <- qnorm(1 - beta, mean = 0, sd = 1)
# http://powerandsamplesize.com/Calculators/Compare-2-Means/2-Sample-Non-Inferiority-or-Superiority:
# Aniko Szabo 230821: Add check for non-existent solution:
if (sign(z.alpha + z.beta) != sign(treat - control - ndelta)){
stop("Target power is not reachable. Check the exact specification of the hypotheses.")
}
n.control <- (1 + 1 / r) * (sigma * (z.alpha + z.beta) / (treat - control - ndelta))^2
n.treat <- n.control * r
if(nfractional == TRUE){
n.control <- n.control
n.treat <- n.treat
n.total <- n.treat + n.control
}
if(nfractional == FALSE){
n.control <- ceiling(n.control)
n.treat <- ceiling(n.treat)
n.total <- n.treat + n.control
}
rval <- list(n.total = n.total, n.treat = n.treat, n.control = n.control, delta = delta, power = power)
}
if (!is.na(treat) & !is.na(control) & !is.na(delta) & !is.na(n) & is.na(power) & !is.na(r) & !is.na(alpha)) {
# delta equals the max absolute tolerable difference between treat and control.
# Make delta negative:
ndelta <- -delta
# Work out the number of subjects in the control group. r equals the number in the treatment group divided by the number in the control group.
if(nfractional == TRUE){
n.control <- 1 / (r + 1) * n
n.treat <- n - n.control
n.total <- n.treat + n.control
}
if(nfractional == FALSE){
n.control <- ceiling(1 / (r + 1) * n)
n.treat <- n - n.control
n.total <- n.treat + n.control
}
z <- (treat - control - ndelta) / (sigma * sqrt((1 + 1 / r) / n.control))
# Aniko Szabo 230821 - use only one tail:
power <- pnorm(z - z.alpha, mean = 0, sd = 1)
# Original code:
# power <- pnorm(z - z.alpha, mean = 0, sd = 1) + pnorm(-z - z.alpha, mean = 0, sd = 1)
rval <- list(n.total = n.total, n.treat = n.treat, n.control = n.control, delta = delta, power = power)
}
rval
}
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