Dear Pascal,

Here is an idea.  You may want to do 1 simulation by having your POSTHOC 
estimates in the dataset (like a covariate), let's say your parameters are CL 
and V.  You will need to fix to zero the ETAs and EPSs (or remove them from the 
model) and perhaps have a dummy variable not used in the model for the 
simulation.  This way NONMEM would simulate directly your individual curves 
from your POSTHOC estimates (up to the precision of these PK estimates from 
your output file).  Note I have not tried this, but I think it should work.

Best regards,

Jean

From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On 
Behalf Of pascal.gir...@merckgroup.com
Sent: Friday, November 23, 2012 11:09 AM
To: nmusers@globomaxnm.com
Subject: [NMusers] Different EBE estimation between original and enriched 
dataset with MDV=1

Dear NM-User community,

I have a model with 2 differential equations and I use ADVAN6 TOL=5. In $DES, I 
am using T the continuous time variable. The run converges, $COV is OK, and the 
model gives a reasonable fit. In order to compute some statistics which cannot 
be obtained analytically, I need to compute individual predictions based on 
individual POSTHOC parameters and an extended grid of time for interpolating 
the observed times.

So I have
1) added to my original dataset extra points regularly spaced with MDV=1. To 
give you an idea, my average observation time is 25, with a range going from 5 
to 160. So my grid was set so that I have a dummy observation every 1 unit of 
time.
2) rerun my model using $MSFI to initialize the pop parameters, with MAXEVAL=0 
and POSTHOC options so that individual empirical Bayes estimates (EBE) 
parameters for each patient would be first re-estimated, then the prediction 
would be computed.

Then I
3)  checked that my new predictions computed from the extended dataset match 
the predictions of the original dataset at observed time points. I had the 
surprise to see that for some individuals those predictions match, for some 
others they slightly diverge, and for few others they are dramatically 
different. I checked the EBEs and they were clearly different between the 
original dataset and the one with the dummy points.
4) I decided to redo the grid with only one dummy point every 1/4 of time unit. 
The result was less dramatic, but still for most of my individuals the EBEs 
predictions were diverging from the original ones computed without the dummy 
times.

Of course the solution for me is to estimate the EBEs from the original 
dataset, export them in a table and reread them to initialize the parameter of 
my individuals using only dummy time points and no observations.

This problem reminds me something that was discussed previously on nm-user, but 
I could not recover the source in the archive.

Anyway is this something known and predictable that when adding dummy points 
with MDV=1 to your original dataset you sometimes get very different EBEs ? Are 
there cases/models/ADVAN  where the problem is likely to happen? Is their a way 
to fix it it in NONMEM other than the trick I used?

Thanks for your replies!

Kind regards,

Pascal Girard, PhD
pascal.gir...@merckgroup.com<mailto:pascal.gir...@merckgroup.com>
Head of Modeling & Simulation - Oncology
Global Exploratory Medicine
Merck Serono S.A. * Geneva
Tel:  +41.22.414.3549
Cell: +41.79.508.7898

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