Dear NONMEM Users,
I'd like to get some advice from you with regard to how to handle the pre-first
dose PK observation when the drug is not an endogenous substance.
I tried too different approaches, one approach is treating them as missing
values (DV=0, EVID=0, MDV=1), another is treating them
Yaming,
As you pointed-out DV=Prediction. Including these data-points biases your
estimate of the additive component of variability.My opinion is just to
exclude the observations to get a better estimate of additive variability.
On a side note Additive+Proportortional is similar to a logno
Yaming, Matt,
I would do exactly what Yaming has done already. Treat the pre-dose
measurements as true observations for when the predicted conc is zero.
It is not true to say they provide no information about model
parameters. They are the best way to improve your estimate of the
additive er
Hi Nick,
I disagree with the suggestion to use exact zero for predose. Residual error
models are, for a perfect structural model, a model for assay uncertainty. By
including many observations at predose as precise zero, you are modeling non
assay observations with an assay model. Result is your
Daniel
Sorry -- thanks for picking that up! It seems I didn't read what Yaming
wrote properly. Perhaps because I couldn't imagine why anyone would
replace a real measurement with zero! Beal pointed out the folly of
replacing BLQ values with zero in 2001. Notice that I said to use the
pre-do
Dear all,
I guess to know what is the best way to treat these data you need to know
something more about the assay than what has been told. If the additive
error component is related in any way to drug being administrated (such as
when metabolites may interfere), then Matt's position may more rele
