Bioequivalence and Bioavailability Forum • Pilot study

konkous
☆

Greece,
2020-12-11 09:25
(1224 d 13:38 ago)

Posting: # 22140
Views: 2,220

Pilot study [Power / Sample Size]

Dear colleagues,

We have conducted an in-vitro permeation pilot study for confirming the suitability of our design in terms of time points, duration and for estimating the sample size for the pivotal study based on the within-reference %CV and the GMR calculated. The GMR was approximately 1.19 and the %CV around 25%. As it can easily be inferred the 90% C.I did not fall within the 0.80-1.25 interval. I realize that no safe conclusions can be drawn from a very small sample size regarding the equivalence of the products but i wonder if there is an established methodology for assessing if there is any point in proceeding with the pivotal study.

Thank you,

Best regards,

Constantinos

Edit: Category changed; see also this post #1. [Helmut]

Helmut
★★★

Vienna, Austria,
2020-12-11 12:54
(1224 d 10:09 ago)

@ konkous
Posting: # 22143
Views: 1,761

“Bad” GMR

Post reply

Hi Constantinos,

❝ We have conducted an in-vitro permeation pilot study for confirming the suitability of our design in terms of time points, duration and for estimating the sample size for the pivotal study based on the within-reference %CV and the GMR calculated.

I don’t get the connection. :confused:

❝ The GMR was approximately 1.19 and the %CV around 25%. As it can easily be inferred the 90% C.I did not fall within the 0.80-1.25 interval.

Without the sample size of the pilot we can only guess (see below).

❝ I realize that no safe conclusions can be drawn from a very small sample size regarding the equivalence of the products but i wonder if there is an established methodology …

Yes.

❝ … for assessing if there is any point in proceeding with the pivotal study.

No, it isn’t. Try this (assuming a 2×2×2 design, ≥80% power) …

library(PowerTOST) GMR <- 1.19 # terrible! CV <- 0.25 res <- data.frame(n = seq(12, 20, 2), lower.CL = NA, upper.CL = NA) for (j in 1:nrow(res)) { res[j, 2:3] <- round(100*CI.BE(pe = GMR, CV = CV, n = res$n[j]), 2) res$n1[j] <- sampleN.TOST(CV = CV, theta0 = GMR, details = FALSE, print = FALSE)[["Sample size"]] res$n2[j] <- expsampleN.TOST(CV = CV, theta0 = GMR, prior.type = "CV", prior.parm = list(m = res$n[j], design = "2x2x2"), details = FALSE, print = FALSE)[["Sample size"]] } print(res, row.names = FALSE)

… which gives

n lower.CL upper.CL n1 n2 12 99.18 142.78 312 366 14 100.81 140.47 312 356 16 102.08 138.72 312 350 18 103.11 137.33 312 344 20 103.97 136.20 312 340

… where n is the sample size of the pilot, n1 the estimated sample size of the pivotal by the “carved in stone” approach (i.e., assuming that you will get exactly the same GMR and CV as in the pilot), and n2 the sample size estimated by the Bayesian approach (taking the uncertainty of the CV observed in the pilot into account). If you would also take the uncertainty of the GMR into account, the algo would fail.
In short: reformulate.

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Helmut Schütz

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konkous
☆

Greece,
2020-12-11 15:44
(1224 d 07:18 ago)

@ Helmut
Posting: # 22144
Views: 1,744

“Bad” GMR

Post reply

Dear Helmut,

Thank you very much for your prompt feedback.

Please let me provide some more information regarding the study and its design.
The study is an in vitro permeation study for a topical product. So the design is not the typical 2x2 crossover design since we are dealing with tissues from different donors. Each of the reference and test products is applied on replicate skin sections of each donor. The only effect here (maybe simplifying things a little) is the formulation effect since each of the test and reference formulations is applied on each donor at the same time on separate skin sections.
To this end the statistical comparison is closer to a paired TOST.

The comparison is made according to the provisions of FDA draft guidance on acyclovir cream.

The aim of the pilot was to see if the duration of the study is sufficient to capture the entire release profile (increase and subsequent decrease of flux) and to have an idea about the GMR.

You are absolutely right regarding the conclusion that the chances for establishing equivalence are slim to none. I was wondering whether someone could "translate" the criterion of the pivotal study into a scaled criterion for the pilot taking into account the small sample size of the latter and its associated uncertainty. For example i have seen the following approach for reaching a go / no-go decision
https://www.researchgate.net/publication/7196063_Average_Bioequivalence_Evaluation_General_Methods_for_Pilot_Trials

Thanks a million!
Best regards,
Constantinos