Bioequivalence and Bioavailability Forum

Dear Detlew!

❝ Blindly following somebody is not always the best strategy to not falling into a trap.

Agree.

❝ Two-sided is identical with one-sided calculated with alpha/2, I think.

This is what I thought (and still think). These formulas are so simple that I cannot imagine what’s going on here.

❝ Further I think that you don't remember your own habit correctly. See your post dating back to the year 2006:

❝ "Since we are interested only in the upper limit of CV – if CV in the main study is lower than predicted, we are only gaining power – it may be better to apply a one-side approach instead."

OK, OK.

❝ Thus I think the code is validated and the effect we see is true.

Ha! In my presentations on bioanalytics I use to make a distinction between “validated” and “valid”. Whilst the former means following a set of instructions (the validation plan) the latter means getting the “correct” results. That’s why I’m also skeptical about software vendor’s “validation packages”. If both the software and the validation package contains the same bug (2×2=5) we are proud about getting the expected results (validated) – which is still wrong… Following our private conversation your source was SA Julious. Now we followed different guide-dogs and got the same results. The code is validated. But is it valid? Would need a professional statistician to sort that out. Martin? Jack?

❝ May be that my belief that a CI is bracketing the point estimate is relying on CIs for means.

Duno. IMHO valid for any kind of point estimator (the mean being just one of them). Now I’m lost. Like you I always thought the two-sided CI gives the range containing the expected value at 1–α/2. In the extreme cases of α=0 the CI should be -∞,+∞^* and for α=1 CL_lo=PE=CL_hi. As our Indian friends use to say: Correct me if I’m wrong.

❝ Of course no one rational thinking human being, except nitpickers like you and me , will set the error probability to such values >0.5.

Right. Sorry opening another can of worms.

---

• For the normal distribution. Here \(]0,+\infty]\ldots\)