Bioequivalence and Bioavailability Forum

Hi Angus,

❝ […] I am having difficult getting access to the site and posting. It is a battle and I am distracted by it and making many errors. I have now switched to Firefox to see if that will work.

Sorry about that. I checked my server’s log-files and it seems that some script-kiddies are trying to hack into the database. Low chances, but side-effects due to high server load.
My suggestion: Regularly copy the entire text-area to the clipboard (Ctrl-A Ctrl-C). If you get disconnected you could insert the stuff into a new reply.

❝ I am focusing on Table 4 second part (FDA approach)…

I guess you mean Table A4 on p84.

❝ …and I see that at 90% power for GMR=1.1 {as recommended by authors} the sample size needed is 38 subjects for a CV of 30%.

Contrary to 2×2 crossovers (5% deviation) in HVDs/HVDPs PEs “jump around” between studies. There­fore, the Lászlós recommend a 10% deviation. If you have no idea about the direction, always use the lower (<1) one. The upper one will be covered as well. Example 10% deviation. Assuming 90% we will get the same power for 1/0.9=1.1111% (110% is covered as well). This doesn’t work the other way ’round. If you start with 110%, you will get the same power for 1/1.1=0.9090. 90% is not covered!

But you expect the ratio at 0.93, right? Therefore, according to Table A4 the sample size will be between 23 (→24!) for 0.95 and 44 for 0.90. PowerTOST suggests 30 for 0.93.

❝ I note that your program in R estimates sample size for RSABE.

It also allows sample size estimation for cases where CV_WR ≠ CV_WT. This reduces the sample size if you know that the reference is lousy and the test shows lower variability (an effect commonly seen in studies of PPIs). From a 2×2 cross­over you only get CV_intra (pooled from CV_WR and CV_WT). However, in the backyard you can play around with assump­tions. Let’s say got CV_intra 30% in a 2×2 cross­over and assume T/R-ratios of intra-subject variances to be 1:1, 3:4, and 1:2. Try this code:
CVs <- CVp2CV(0.3, ratio=c(1, 3/4, 1/2))
CVs
You will get decomposed per-treatment CVs:
          CVwT      CVwR
[1,] 0.3000000 0.3000000
[2,] 0.2768811 0.3217173
[3,] 0.2431753 0.3489488
Now you can feed the rows to sampleN.RSABE in order to assess their impact on sample size. Example for CV_intra 30%, target power 90%, T/R-ratio 90%, equal and dif­fe­rent CVs, 2×2×4 RSABE:
for (j in 1:3) {
  sampleN.RSABE(CV=CVs[j, ], theta0=0.90,
  targetpower=0.9, design="2x2x4", details=F)
}

───────────────────────────
CVWT %   CVWR %   n  % power
───────────────────────────
30.00   30.00   44   90.02 
27.69   32.17   38   90.90 
24.32   34.89   30   90.30 
───────────────────────────
This is one of the reasons why it makes sense to perform already the pilot study in a fully replicated design. It may pay off in a smaller pivotal.

❝ I will see if I can download and see what it provides.

Some hints about installation in this post.

❝ I will switch back to Explorer and see if I can get it to work.

I’m afraid the problems are on my side of the pond.