Hi Pascal,
I think the problem is in the precision of the integration routine. With
extra points, you change the ODE integration process and the results. I
would use TOL=10 or higher in the original estimation. I have seen cases
when changing TOL from 6 to 0 or 10 changed the outcome quite
significantly.
Leonid
--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566
On 11/23/2012 11:08 AM, pascal.gir...@merckgroup.com wrote:
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
Head of Modeling & Simulation - Oncology
Global Exploratory Medicine
Merck Serono S.A. ยท Geneva
Tel: +41.22.414.3549
Cell: +41.79.508.7898
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