Dear all,
I would like to share with you and get people's opinions on a recent issue I
had.
I have a data set of 46 patients, orally dosed, with very dense sampling
during absorption (0.25h, 0.5h, 0.75h, 1h, 1.5h, 2h, 3h, 4h, 6h, 8h, 12h,
24h, 36h), Cmax at around 4 hours.
During modelling, I
Dear Jon,
As you point out the concept of residual error magnitude being dependent on
anything else than the prediction itself is a straightforward. Yet it is, I
think underused and that is why you may not see it much in the literature. In
addition to what you mention, a large component is that
Dear Jon,
We routinely use separate residual errors during absorption and later
(say, a larger error in the first X hours), although I do not remember
whether we published any of those models.
Regards,
Katya
Ekaterina Gibiansky, Ph.D.
CEO&CSO, QuantPharm LLC
Web:www.quantpharm.com
Email:egib
The code cannot be used when any WT values are missing.
In order to interpolate appropriately in $DES, WT should be present
on every event record.
$PK sees only the current and previous record.
If WT is missing from the current record, $PK has no way of knowing
what WT will be on the next record.
Hi Steve and Andre,
No, nothing fancy, I did not even estimate the time of the switch.
Something like this:
ITAD=1
IF(TAD.LE.4) ITAD=THETA(10)
Y=F + (F*ITAD*ERR(1)+ERR(2))
ITAD can also include differences in populations, e.g. Phase 1 versus
Phase 2-3 data, or healthy versus patients, etc.
Steve,
In papers 3 and 4 below, we outlined and tested a few different models for
separating error models for absorption and disposition based on time or partial
derivatives versus time or Ka. Overall message is *that* you do it is more
important than *how* you do it. I typically choose a simpl
