Hello all,
My apologies for responding to this thread late. I agree with using the BQL
method when data are censored to a certain value due to assay or measurement
restrictions. What I am not clear about is the actual suitability of the
approach for this case. I am not sure I understand the
Jacob,
I am guilty of having performed such a bootstrap (but didn't do any of the
testing you describe). Anyway, here's an opinion: By using prior in nonmem
you are trying to get an approximation of what the pooled data fit would be and
get an OFV that is theoretically the same as if you did
Jacob,
Bootstrap procedure vary only the new data. The information in priors is
still the same. Therefore, results will be as dependent on the priors as
the parameter estimates of the final model. When the priors are strong
(informative) it would require a lot of new data to move the parameter
Dear nmusers,
Merry Holiday! (you may change the variable 'Holiday' to an arbitrary value)
First, accept my humble excuse - I can't share any code for this example.
I'm assessing a model where $PRIOR is used to stabilize the fit of an 'old'
model to sparse data using NONMEM. It seems to be the
Dear all,
The Pharmacometrics research group at Uppsala University, Sweden, have
openings for 8 Postdoctoral positions in Pharmacometrics in the following
areas:
. Oncology
. Tuberculosis
. Antibacterial resistance
.
