## Truncated 72 hours [NCA / SHAM]

Hi stic-stats,

❝ ❝ […] Since AUC72 is a primary PK metric consider hospitalizing subjects in the future. By opting for ambulatory sampling you saved some rupees but risked to substantially loose power.

❝ May I know what type of power loose.

Fewer eligible subjects will always decrease power. To which extent is unclear (mmw posted more than five years ago) and without the CV and sample size we would need a crystal ball.
Example (2×2 crossover, T/R 0.95, target power 0.80):

library(PowerTOST) CV  <- seq(20, 40, 5) # intra-subject CV in percent dor <- seq(0, 15, 5)  # dropout-rate in percent res <- data.frame(CV = rep(CV, each = length(dr)), n = NA, dor = dor,                   elig = NA, power = NA) for (j in 1:nrow(res)) {   tmp <- sampleN.TOST(CV = res$CV[j]/100, print = FALSE, details = FALSE) res$n[j] <- tmp[["Sample size"]]   if (res$dor[j] == 0) { res$elig[j]  <- res$n[j] res$power[j] <- tmp[["Achieved power"]]   } else {     res$elig[j] <- floor(tmp[["Sample size"]]*(100-res$dor[j])/100)     res$power[j] <- suppressMessages(power.TOST(CV = res$CV[j]/100,                                                  n = res$elig[j])) } } res$power <- signif(res\$power, 5) names(res)[1:4] <- c("CV (%)", "dosed", "dropouts (%)", "eligible") print(res, row.names = FALSE) 

Gives:

 CV (%) dosed dropouts (%) eligible   power      20    20            0       20 0.83468      20    20            5       19 0.81324      20    20           10       18 0.79124      20    20           15       17 0.76365      25    28            0       28 0.80744      25    28            5       26 0.77606      25    28           10       25 0.75766      25    28           15       23 0.71729      30    40            0       40 0.81585      30    40            5       38 0.79533      30    40           10       36 0.77239      30    40           15       34 0.74666      35    52            0       52 0.80747      35    52            5       49 0.78311      35    52           10       46 0.75583      35    52           15       44 0.73545      40    66            0       66 0.80525      40    66            5       62 0.77978      40    66           10       59 0.75824      40    66           15       56 0.73466

❝ Is their any impact on calculation of AUC due to number of missed sample?

Yes. Since generally studies are planned for the PK-metric with the highest variability (likely Cmax) the overall impact on power should be limited.

❝ Could these subjects be excluded from statistical analysis? If yes, on what basis we exclude these subjects?

Everything is possible – if stated in the statistical protocol. You could exclude them (only AUC whilst keeping Cmax), use an estimate (if λz is reliable), use the AUC to the last time point where you have concentrations for both T and R,…
Please search the forum; lots of posts.

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