Bioequivalence and Bioavailability Forum

Hi Smog,

❝ The point estimate for the Cmax was 116.97. CI is: LL 95,28, UL 146,01 in the pilot study.

Hhm, I get \(PE = \sqrt{0.9528 \times 1.4601} = 1.1795 \; {\color{Red} \neq 1.697} \). Are your numbers correct?

❝ The drug is not HVD.

Correct if we believe that the CV is “carved in stone”.
By means of R / package PowerTOST:

library(PowerTOST)
n  <- c(6, 5) # my assumption: 6 in one sequence and 5 in the other
LL <- 0.9528
UL <- 1.4601
PE <- sqrt(LL*UL)
CV <- CVfromCI(lower=LL, upper=UL, design="2x2x2", n=n)
cat(sprintf("%s %.2f%%%s", "estimated CV:", 100*CV, "\n"))
estimated CV: 27.70%

But the CV is an estimate and its precision depends on the sample size. The “true” CV might as well cross the border of a HVD(P). Let’s assume a “producer’s risk” of 20%:

CVupper <- CVCL(CV=CV, df=sum(n)-2, side="upper", alpha=0.20)[["upper CL"]]
cat(sprintf("%s %.2f%%%s", "upper CL of the CV:", 100*CVupper, "\n"))
upper CL of the CV: 36.29%

❝ Is there any sence to go forward with pivotal study in case of such GMR? If so which GMR to use: 0,95 or 1,1697?

0.95 is already outside the 90% CI of the pilot study! The 90% CI means that the true (but unknown) T/R-ratio lies with p ≤ 0.05 below the lower CL and with p ≤ 0.05 above the upper CL. In other words your odds would be less than 1:20. Very bad idea. See also Fuglsang¹ for the transition from pilot to pivotal studies in BE and some old stuff² for background.
On the other hand, if you assume that both the PE and CV from the pilot are “true” values sample sizes would be extreme (274 for 80% power in a 2×2×2 for ABE and still 136 in a 2×2×4 for ABEL). I would be very wary to proceed. Think about reformulation.

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1. Fuglsang A. Pilot and Repeat Trials as Development Tools Associated with Demonstration of Bioequivalence. AAPS J. 2015;17(3):678–83. doi:10.1208/s12248-015-9744-6.
   
2. Browne RH. On the use of a pilot sample for sample size determination. Stat Med. 1995;14(17):1933–40. doi:10.1002/sim.4780141709.