Bioequivalence and Bioavailability Forum

Hi Loky do,

❝ but I am quite confused, some products show high intrasubject variability more than anticipated or published in the literature …

If you design studies for 80% power and all of your assumptions are exactly realized (T/R-ratio, CV, dropout-rate), one out of five will fail be pure chance. That’s life.
If the T/R-ratio is worse, and/or the CV higher, and/or the dropout-rate higher, you loose power though you might still pass (see there). Happens all the time.

❝ … and I think this variability affect the reliability of results, …

Not necessarily.

❝ … for example, I have a BE study for (lansoprazole), partially replicate design, the published intrasubject variability ~ 40% …

That’s on the lower end for lansoprazole. I’ve seen studies with substantially higher CVs.

❝ … but the practical intrasubject variability we had was near 85%

Bad luck if you designed the study for 40%. Was risky.

❝ … study protocol also stated using ema guidelines for scaling but the study failed, when I used FDA method it passes

We are going in circles. Since you stated in the protocol that the study will be assessed for the EMA’s approach, you failed. Full stop. The fact that you would pass with RSABE is irrelevant.

❝ … does this high variability have a role in this confusing results?

Of course, it does. In ABEL you impose an upper limit to expanding the limits, which is at CV_wR = 50% for the EMA (max. expansion 69.84–143.19%) and CV_wR ~57.4% for Health Canada (66.7–150.0%). For the FDA’s RSABE there is no such restriction. Hence, for any CV, the sample size for RSABE will be lower than the ones for the variants of ABEL. Or the other way ’round: For a given CV and sample size, RSABE has more power than ABEL. See there.

library(PowerTOST)
CV     <- sort(c(seq(40, 90, 10), 85))
theta0 <- 0.95
design <- "2x2x4"
target <- 0.80
res    <- data.frame(CV = CV, n = NA_integer_,
                     power.ABEL = NA_real_, power.RSABE = NA_real_,
                     power.ABEL.n1 = NA_real_, power.RSABE.n1 = NA_real_)
for (j in 1:nrow(res)) {
  tmp                <- sampleN.scABEL(CV = CV[j]/100, theta0 = theta0,
                                       design = design, targetpower = target,
                                       details = FALSE, print = FALSE)
  res$n[j]           <- tmp[["Sample size"]]
  res$power.ABEL[j]  <- tmp[["Achieved power"]]
  res$power.RSABE[j] <- power.RSABE(CV = CV[j]/100, theta0 = theta0,
                                    design = design, n = res$n[j])
  if (j == 1) {
    res$power.ABEL.n1[j]  <- res$power.ABEL[j]
    res$power.RSABE.n1[j] <- res$power.RSABE[j]
  } else {
    res$power.ABEL.n1[j]  <- power.scABEL(CV = CV[j]/100, theta0 = theta0,
                                          design = design, n = res$n[1])
    res$power.RSABE.n1[j] <- power.RSABE(CV = CV[j]/100, theta0 = theta0,
                                         design = design, n = res$n[1])
  }
}
names(res)[5:6] <- c(paste0("power.ABEL (n=", res$n[1], ")"),
                     paste0("power.RSABE (n=", res$n[1], ")"))
res$CV <- sprintf("%.0f%%", res$CV)
print(res, row.names = FALSE)

Gives:

  CV  n power.ABEL power.RSABE power.ABEL (n=20) power.RSABE (n=20)
 40% 20    0.82131     0.86422           0.82131            0.86422
 50% 22    0.84475     0.90013           0.79996            0.86790
 60% 24    0.80957     0.90602           0.71029            0.85313
 70% 30    0.81094     0.91987           0.57715            0.82659
 80% 38    0.82330     0.92529           0.43569            0.79581
 85% 40    0.80422     0.91998           0.36923            0.78044
 90% 44    0.80858     0.92106           0.30796            0.76509

That’s why your study failed with ABEL and may have passed with RSABE.

❝ … also in the future protocol can I specify that if the intrasubject variability is more than 50% can I switch to the FDA method for calculation, as many authorities we submit our studies to follow ema guidelines?

No, you can’t. That’s data-driven. OK, you can but it will not be accepted.