Bioequivalence and Bioavailability Forum

Hi ElMaestro,

❝ Good input. I have no idea myself. I live in a cage. Whether or not something is relevant to real life is of no particular interest to me

Not unfamiliar to me.

❝ At this point in principle any distribution would suffice for starters as long as:

❝ 1. I can control the geometric mean.

❝ 2. I can control the CV.

❝ 3. The dist. is not log normal.

❝ 4. I can derive random numbers via pts 1+2.

Quick shot about distributions of ~700 concentrations. Drug with quite high between-sub­ject variability in C_max (~50%). I looked at concentrations in the absorption phase, around C_max, and in late elimination.

Raw scale



Log scale

Lognormal seems to “work” close to C_max, but otherwise? Look at the crowded first bin.

❝ Having said all that I need to air another thought:

❝ We usually say that the error of the linear model is IID and normal with mean zero but actually Chow and Liu's formulation in that regard is not crystal clear.

Yes.

❝ You showed some beautiful plots -can't find the thread right now- which demonstrated that the errors are correlated within subject.

I know. Will search for it later (have to find the nearest lake now and dive in).

❝ I am inclined to think that we do not strictly need IID for the normal linear model. We can relax it a bit:

❝ we only need IID for the difference of test-ref in xovers, and that's why the Potvin/Montague considerations actually work.

Yep. I remember an article by Carl Metzler where he pointed out that the distribution of metrics is irrelevant. We are only interested in model’s residuals which approximate true error.

❝ Parallel is another matter of course.

Yessir.

❝ Hell, I wish I had studied statistics at the university.

Quoting Ohlbe: Don’t worry, it’s too late.

❝ I think the

What?