Bioequivalence and Bioavailability Forum

dear dlabes !

here is my personal point of view for the classical complete data designs (i.e. one concentration time profile per subject):

• for visualizing individual concentration time profiles I treat values < LLQ as missing.
  
• for visualizing average concentration time profiles I use values < LLQ as zero – otherwise the visualized profile may be biased when omitting values below LLQ.

for average concentration time profiles I use also arithmetic means as we showed (Jaki and Wolfsegger) that the AUC calculated based on arithmetic means is an asymptotically unbiased estimator for the true AUC. I’am not quite sure if this is true when using geometric means (for visualizing average concentration time profiles, e.g. Källen 2008, figure 3.4). note that values of zero are not possible when using geometric means.

handling missing values for calculation of AUCs depend on the definition. you can define the truncated AUC as 1) AUC from 0 to the last quantifiable concentration or 2) AUC from 0 to x hours. for calculation of AUC from 0 to x hours I set values < LLQ as zero.

hope this helps

Martin

Jaki T and Wolfsegger MJ. A theoretical framework for estimation of AUCs in complete and incomplete sampling designs. Statistics in Biopharmaceutical Research, in press.

Anders Källen (2008). Computational Pharmacokinetics. Chapman and Hall / CRC, Boca Ration.