Deep shit [RSABE / ABEL]

posted by Helmut Homepage – Vienna, Austria, 2020-01-30 21:07 (1712 d 15:59 ago) – Posting: # 21114
Views: 22,356

Hi Mikalai,

❝ […] Why should we calculate CI twice …


You don’t calculate it twice, you possibly assess it twice.

❝ … if we do not plan control TIE.


Cause you care about the patients? If you don’t give a shit about other people consider changing jobs and become a stock broker.

❝ As I understand the formula of CI for ABE and RABE/ABEL is the same (2 sequence 4-period study). Or I am wrong? We calculate ABE CI. If Cmax is out of range, we than calculate the RR for ref drug and recalculate a new bioequivalence interval if the CV is higher than 30. And then we compare whether our old ABE CI is within the new interval. In this case, we calculate CI once. Am I wrong?


See above. You calculate the CI once. If you fail ABE (first assessment with a nominal α 0.05), you assess it for ABEL (second assessment with a nominal α 0.05). Is it clear now? Two tests, each performed at level 0.05. Inflated Type I Error. Full stop.

❝ In terms of the decision tree, this approach is used. It is not unique. Multiple studies were accepted in Europe. It seems that there have been no complaints from European regulators.


Awful, just awful.

❝ […] It seems that to control TIE we may have to change the alpha after getting results from the study sample. It potentially can reduce power and the only way to balance this is to recruit more subjects. The question is how many?


Easy to answer.

❝ One should take into consideration that ABEL/RABE trials are more challenging: high dropout rates (longer than usual BE trials) …


Opt for one of the three period full replicates. Loss of power due to dropouts is overrated.

❝ … and riskier (more AE because of more blood and more drugs).


Given.

❝ If we can find a solution when we may increase the sample size slightly, then it is OK;


See the presentation linked above. Peanuts.

❝ … otherwise, we may approach the sample size of a 2-period trial that basically invalidates the whole concept.


For your approach you possibly have to adjust α below Bonferroni’s 0.025.

library(PowerTOST)
CV     <- seq(0.2, 0.5, 0.1)
design <- "2x2x4"
# defaults: theta0 0.90, targetpower 0.80
res <- data.frame(CV = CV, n.ABE = NA, n.ABEL = NA,
                  n.ABEL.Bonf = NA, n.ABEL.adj = NA, n.Molins = NA)
for (j in 1:nrow(res)) {
  res[j, 2]       <- sampleN.TOST(CV = CV[j], design = design,
                                  details = FALSE,
                                  print = FALSE)[["Sample size"]]
  res[j, 3] <- sampleN.scABEL(CV = CV[j], design = design, details = FALSE,
                              print = FALSE)[["Sample size"]]
  res[j, 4] <- sampleN.scABEL(alpha = 0.025, CV = CV[j], design = design,
                              details = FALSE, print = FALSE)[["Sample size"]]
  res[j, 5] <- sampleN.scABEL.ad(CV = CV[j], design = design, details = FALSE,
                                 print = FALSE)[["Sample size"]]
  alpha.adj <- scABEL.ad(CV = 0.3, design = design, n =  res$n.ABEL[j],
                         details = FALSE, print = FALSE)[["alpha.adj"]]
  res[j, 6] <- sampleN.scABEL(alpha = alpha.adj, CV = CV[j],
                             design = design, details = FALSE,
                             print = FALSE)[["Sample size"]]
}
res$CV <- 100*res$CV; names(res)[1] <- "CV (%)"
print(res, row.names = FALSE)

CV (%) n.ABE n.ABEL n.ABEL.Bonf n.ABEL.adj n.Molins
    20    10     18          24         18       22
    30    20     34          44         42       42
    40    34     30          38         32       36
    50    50     28          34         28       32

❝ There is also another point. In Europe, we cannot expand limits for AUC and can for Cmax when it is safe. Why cannot we tolerate in this situation TIE that is higher than 5% (why 5%?), let's say 10%? Nothing is 'carved in the stone'.


Wow! Even for a hardened atheist that smells of heresy. :not really:

❝ It appears that no drugs were withdrawn because of inflated TIE yet.


Duno.

❝ Also whether we have reliable methods to control TIE statistically on which regulators agree?


As long as you use the same framework / method stated in the GL no regulator on this planet has any reason not to accept an approach using a lower α.

❝ Finally, I disagree that pharmacovigilance is senseless. If it is done properly, it should and can pick up bad players (drugs and companies).


I don’t believe it.
The only example I’m aware of was the formulation change of levothyroxine in France (passed ABE with narrower limits and the AEs went through the ceiling).
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