Bioequivalence and Bioavailability Forum

Hi Ghannam,

Please!

❝ Futility design is a type of two stage design studies. In this design you may dose a group of volunteers then based on the results of the first group you may complete the study by adding a second group of subjects. alpha value will be 0.05 % and will not be changed to 0.0294 %.

Splendid. Do you have a reference?

The EMA’s GL states “[…] the choice of how much alpha to spend at the interim analysis is at the company’s discretion”. ‘Splitting’ α to 0, 0.05 is tough. Of course you need a futility criterion in the interim. Which one do you have in mind? The GMR, the CI, an upper total sample size? Even more moderate splits (Haybittle/Peto or O’Brien/Fleming) lead to an inflation of the type I error (see this post).

library(Power2Stage)
power.2stage.fC(alpha=c(0, 0.05), n1=12, CV=0.24, fCrit="PE",
                fClower=0.8, theta0=1.25, powerstep=FALSE)

TSD with 2x2 crossover
Method B: alpha (s1/s2) = 0 0.05
No interim power monitoring step used
Target power in sample size est. = 0.8
Power calculation via non-central t approx.
CV1 and GMR = 0.95 in sample size est. used
Futility criterion PE outside 0.8 ... 1.25
BE acceptance range = 0.8 ... 1.25

CV = 0.24; n(stage 1) = 12; GMR= 0.95

1e+06 sims at theta0 = 1.25 (p(BE)='alpha').
p(BE)    = 0.058721
p(BE) s1 = 0.010094
Studies in stage 2 = 46.59%

Distribution of n(total)
- mean (range) = 19.3 (12 ... 108)
- percentiles
 5% 50% 95%
 12  12  40

I changed the category of the thread. Before we can discuss regulatory acceptance, you should tell us the exact background of your statistical method.