If you have data… [Design Issues]

posted by Helmut Homepage – Vienna, Austria, 2022-09-27 13:52 (73 d 11:01 ago) – Posting: # 23323
Views: 700

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.
[image]-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


Dif-tor heh smusma 🖖🏼 Довге життя Україна! [image]
Helmut Schütz
[image]

The quality of responses received is directly proportional to the quality of the question asked. 🚮
Science Quotes

Complete thread:

UA Flag
Activity
 Admin contact
22,436 posts in 4,696 threads, 1,599 registered users;
18 visitors (0 registered, 18 guests [including 12 identified bots]).
Forum time: 23:53 CET (Europe/Vienna)

Meta-analysis – A technique for adding apples and pears
together to produce turkeys.    Stephen Senn

The Bioequivalence and Bioavailability Forum is hosted by
BEBAC Ing. Helmut Schütz
HTML5