## If you have data… [Design Issues]

Hi Imph,

if you have individual half lives, you can calculate the geometric mean / SD and use any confidence interval you like. For planning the washout use the upper confidence limit. -script (results in blue):

g.fun <- function(x, alpha = 0.05, digits = 4, print.only = FALSE) {   # calculate geometric mean, SD, and CI of geometric mean   if (sum(is.na(x)) > 0) { # Remove NA(s)     message("NA(s) removed from the vector.")     x <- x[-is.na(x)]   }   if (sum(x <= 0) > 0) {   # Only positive numbers are allowed     message("Geometric mean applicable to",             "\npositive numbers only; others removed.")     x <- x[-which(x <= 0)]   }   mean.log <- mean(log(x))   SD.log   <- sd(log(x))   gMean    <- exp(mean.log)   gSD      <- exp(SD.log)   # confidence interval based on the t-distribution with n–1 degrees of freedom   CI       <- exp(mean.log + c(-1, +1) *                   qt(alpha / 2, length(x) - 1, lower.tail = FALSE) * SD.log)   loc.dis  <- setNames(c(gMean, gSD, CI),                        c("Geom. mean", "Geom. SD",                          paste0("lower ", 100 * (1 - alpha), "%"),                          paste0("upper ", 100 * (1 - alpha), "%")))   if (print.only) {     print(signif(loc.dis, digits))   } else {     return(loc.dis)   } } # give your data in a t12 vector, e.g., # t12 <- c(7.15, 5.44, 5.33, ...) and proceed with descriptive statistics # lognormal-distributed example data set.seed(123456) n   <- 24   # sample size mue <- 6    # geometric mean of half life CV  <- 0.25 # coefficient of variation t12 <- rlnorm(n = n,               meanlog = log(mue) - 0.5 * log(CV^2 + 1),               sdlog = sqrt(log(CV^2 + 1))) exp(summary(log(t12)))        # descriptive statistics  Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 4.425   5.412   6.383   6.530   7.764  10.780 g.fun(t12, print.only = TRUE) # default: 95% CI Geom. mean   Geom. SD  lower 95%  upper 95%      6.530      1.275      3.951     10.790 alpha      <- 0.01            # conservative: 99% CI g.fun(t12, alpha = alpha, print.only = TRUE) Geom. mean   Geom. SD  lower 99%  upper 99%      6.530      1.275      3.302     12.910 half.lives <- c(5, 7, 10) col.name   <- paste0("upper ", 100 * (1 - alpha), "%") res        <- data.frame(half.lives = half.lives,                          washout = half.lives * g.fun(t12, alpha = alpha)[[col.name]]) res$days <- ceiling(res$washout / 24) # round up washout (h) to days print(round(res, 1), row.names = FALSE)  half.lives washout days           5    64.6    3           7    90.4    4          10   129.1    6

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