Transform or not transform [PK / PD]

posted by d_labes  – Berlin, Germany, 2012-03-22 10:31 (4027 d 20:34 ago) – Posting: # 8310
Views: 6,861

Dear Helmut!

First many thanks for sharing this information.
Your pictures are a good example of the inherent difficulties in determining the distribution form empirical.

Thus I'm a fan of the arguments for the log-normal distribution via theoretical PK considerations (@EM: even if regulators have written down them in guidances :-D).

❝ I used to analyze HVD, t75%, and MRT untransformed and Cmax/AUC logtransformed ...

IMHO this is a good choice :cool:.
Although the residuals (and these count at least I think) don't show a very distinct picture.

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?

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 Cmax/AUC ...

Geary 1947, Biometrika
Normality is a myth; there never was, and never will be, a normal distribution.



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