Xiaofeng,
It always amazes me how many parameters we try to estimate based on the
limited amount of data available. This is specially the case when a rather
complex event is taking place, such as autoinduction in this case, and that
based on sparse samples. In your case, there are a total of 15 pa
Hi all, I am having some issues when estimating two residual error parameters
with the SAEM algorithm (NM7.1.2, PsN 3.2.4).
Below is my example, where each EPS is turned on/off by an indicator variable,
not sure what the behavior is like for error models such as Y=IPRD*EXP(EPS(1))
+ EPS(2), b
Hi Toufigh,
Thank you for the suggestion. I tried the simplest model with 2 ETAs on the
pre- and post-induction clearance. The result gave very small omega estimate
for the pre-induction clearance (2.50e-005). But I don't believe that there is
no between subject variability for the pre-inductio
Whenever I start a modeling process, my first objective is to get the
structural model, i.e., your PK model in this case, well defined and in
place. If you start from there and you are pretty confident that you have
the right model, finding what parameters differ between subjects should not
be diff
Dear Xiaofeng,
Presumably the CL at the start and at SS are correlated. If you in the model
assumed a lack of correlation, that may well be the cause of a variance
driven to zero.
I agree with Toufigh that starting simple is usually a good idea. Given your
data sparsity, I would start very si
