Bioequivalence and Bioavailability Forum

Dear Ben,

❝ First, every time I want to use the helper functions CV2se and se2CV I am confused why it is called that way, and not „CV2sd“ and „sd2CV“, respectively. Standard error always reminds me of sd/sqrt(n) or sd*sqrt(2/n) which is not what we want to plug in here...?!

Ok, maybe that your naming proposal fits your needs better. Mine was driven from the term standard deviation of error (=square root of the error variance).
What you link with this symbol is usually written as SEM = standard error of mean by me (and many others).

❝ My second question refers to the option „logscale=FALSE“. When logscale=FALSE then I wonder how the argument CV looks like. CV is actually sd/mean, but then one also requires to know something about the mean. A better argument would be the standard deviation and a look into your code reveals that in fact you directly put CV <- se. But when looking into the manual, there is no reference that it will be handled this way and moreover, in the example from sampleN.TOST you write „total CV=20%“ when putting CV=0.2 as an argument. Shouldn‘t it then be „total SD = 0.2“ (not in percent)?

You may use the mean and standard deviation the way you describe, I think. Try it by yourself.
You may also use the mean ratio (theat0) as deviation from 1 and then the CV is the ratio sd/mean. The last was the way Kem Philips has described in his basic paper, the ancestor of all power/samplesize papers on the field of BE studies. I have this adopted in my description in the manual

I must confess that I hadn't thought too much about the description in the manual if you use „logscale=FALSE“. This was only implemented for reasons of completeness for comparison with historically tables from that times as it was custom to evaluate BE studies with both log-transformed and untransformed data. But nowadays only log-transformed evaluations are done.

If you prefer untransformed evaluations (f.i. for PD parameters) the use of the Fieller CI is IMHO the appropriate method. Thus use then sampleN.RatioF() for sample size estimations.
Using sampleN.TOST() with logscale=FALSE is only an approximation since it neglects the fact that the Reference mean is only obtained as an estimate.

Naming the input parameter other than CV would inflate the parameter list which is long enough already now, I think. And it would raise the programming burden for me which was already big enough (at east for me as an bloody dabbler).

If this all don't fit your needs: The open source idea is, amongst other, take my code and do it better than I was able to do.


BTW: There is the possibility to write to the maintainer of the PowerTOST package directly. The CRAN policy deemed him to publish his mail address for that purpose .
Having the side effect of getting some more spam than already usual.