Bioequivalence and Bioavailability Forum

Dear Detlew!

❝ Your pictures are a good example of the inherent difficulties in determining the distribution form empirical.

I would rather say: Impossibility in any given study due to limited sample size, but good chances if a wealth of historical data is available.

❝ Thus I'm a fan of the arguments for the log-normal distribution via theoretical PK considerations.

Absolutely. I have no problems with AUC (and C_max as well) but fail to derive a reasonable justification for others. Walnut brain. At the first Bio-International there was this poll amongst participants about transformations. Result was ⅓ always, ⅓ never, ⅓ case-by-case. My idea was to study the empirical distributions of less common metrics to derive a suggestion (of course applicable only to a specific drug/formulation).

❝ ❝ I used to analyze HVD, t_75%, and MRT untransformed and C_max/AUC logtransformed ...

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❝ IMHO this is a good choice .

OK, but how did you conclude that?

❝ Although the residuals (and these count at least I think) don't show a very distinct picture.

Yes, the sample size I assessed is yet inconclusive. See here for the final outcome.

❝ BTW: Since these metrics (the ones you have shown) are usually not primaries then the question of their (their residuals) distribution is not so much of concern I think. I would handle them only in a descriptive way (mean, sd, median and ... and ...). Or do you analyze those metrics also via ANOVA and (1-2*alpha) CI's in a standard fashion?

These data come from MR products. I calculated the CIs – but only descriptively.

❝ My originally question was more in the direction of swing metrics. Do you have similar data for PTF or swing? As for ratios of two terms deemed as log-normally distributed I at least questioning a log-normal distribution. On the other hand one may argue with your results for C_max/AUC ...

Well, that’s the reason I started a new thread. I never calculated Swing. Though I have a lot of MD studies the pooled sample size / drug is too small to assess the empiric distributions. I would expect PTF to have a similar distribution as C_max/AUC (at least if C_min ⇒ LLOQ).

❝ Normality is a myth; there never was, and never will be, a normal distribution.

Nice quote.