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"epi.sscomps" <- function(treat, control, n, power, r = 1, design = 1, sided.test = 2, nfractional = FALSE, conf.level = 0.95) {
alpha.new <- (1 - conf.level) / sided.test
z.alpha <- qnorm(1 - alpha.new, mean = 0, sd = 1)
if(!is.na(treat) & !is.na(control) & is.na(n) & !is.na(power)){
# From: Therneau TM and Grambsch PM 2000. Modelling Survival Data - Extending the Cox Model. Springer, London, p 61 - 65.
z.beta <- qnorm(power, mean = 0, sd = 1)
p <- r / (r + 1); q <- 1 - p
# p <- 0.5; q <- 1 - p
exp.beta <- log(treat) / log(control)
n <- ((z.alpha + z.beta)^2) / (p * q * log(exp.beta)^2)
# Account for the design effect:
n <- n * design
if(nfractional == TRUE){
n.crude <- n
n.treat <- (n / (r + 1)) * r
n.control <- (n / (r + 1)) * 1
n.total <- n.treat + n.control
}
if(nfractional == FALSE){
n.crude <- ceiling(n)
n.treat <- ceiling(n / (r + 1)) * r
n.control <- ceiling(n / (r + 1)) * 1
n.total <- n.treat + n.control
}
rval <- list(n.crude = n.crude, n.total = n.total, n.treat = n.treat, n.control = n.control)
}
else
if(!is.na(treat) & !is.na(control) & !is.na(n) & is.na(power)){
# From: Therneau TM and Grambsch PM 2000. Modelling Survival Data - Extending the Cox Model. Springer, London, p 61 - 65.
beta <- log(treat / control)
p <- r / (r + 1); q <- 1 - p
# Account for the design effect:
n <- n / design
z.beta <- sqrt(n * p * q * beta^2) - z.alpha
power <- pnorm(z.beta, mean = 0, sd = 1)
rval <- list(power = power)
}
else
if(is.na(treat) & is.na(control) & !is.na(n) & !is.na(power)){
# From: Therneau TM and Grambsch PM 2000. Modelling Survival Data - Extending the Cox Model. Springer, London, p 61 - 65.
p <- r / (r + 1); q <- 1 - p
z.beta <- qnorm(power, mean = 0, sd = 1)
# Account for the design effect:
n <- n / design
beta <- sqrt(((z.alpha + z.beta)^2) / (n * p * q))
delta <- exp(beta)
rval <- list(hazard = sort(c(delta, 1/delta)))
}
rval
}
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