Pascal,
I had the same issue a while ago with time-invariant covariates. Back then with
NM6.2, adding an EVID column to the data set and setting EVID=2 for additional
records preserved the ETAs of the original estimation (while only setting MDV=1
for additional records did not).
Herbert
Herbert Struemper, Ph.D.
Clinical Pharmacology, Modeling & Simulation
GlaxoSmithKline, RTP, 17.2230.2B
Tel.: 919.483.7762 (GSK-Internal: 7/8-703.7762)
-----Original Message-----
From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On
Behalf Of Bauer, Robert
Sent: Sunday, November 25, 2012 9:11 PM
To: Leonid Gibiansky; pascal.gir...@merckgroup.com
Cc: nmusers@globomaxnm.com
Subject: RE: [NMusers] Different EBE estimation between original and enriched
dataset with MDV=1
Pascal:
There is one more consideration. If your model depends on the use of covariate
data, then during the numerical integration from time t1 to t2, where t1 and t2
are times of two contiguous records, which have values of the covariate c1 and
c2, respectively, NONMEM uses the covariate at time t2 (call it c2)during the
interval from t>t1 to t<=t2. During your original estimation, your data records
were, perhaps, as an example:
Time covariate MDV
1.0 1.0 0
1.5 2.0 0
With the filled in data set, perhaps you filled in the covariates as follows:
Time covariate MDV
1.0 1.0 0
1.25 1.0 1
1.5 2.0 0
Or perhaps you made an interpolation for the covariate at the inserted time of
1.25, to be 1.5. But NONMEM made the following equivalent interpretation
during your original estimation:
Time covariate MDV
1.0 1.0 0
1.25 2.0 1
1.5 2.0 0
That is, when the time record 1.25 was not there, it supplied the numerical
integrater with the covariate value of 2.0 for all times from >1.0 to <=1.5, as
stated earlier.
Even though MDV=1 on the inserted records, NONEMM simply does not include the
DV of that record in the objective function evaluation, but will still use the
other information for simulation, by simulation I mean, for the numerical
integration during estimation.
In short, your model has changed regarding the covariate pattern based on the
expanded data set.
By the way, there is a utility program called finedeata, that actually
facilitates data record filling, with options on how to fill in covariates, in
nonmem7.3 beta. I will send the e-mail to this shortly.
If you are not using covariates in the manner I described above, then please
ignore my lengthy explanation.
Robert J. Bauer, Ph.D.
Vice President, Pharmacometrics, R&D
ICON Development Solutions
7740 Milestone Parkway
Suite 150
Hanover, MD 21076
Tel: (215) 616-6428
Mob: (925) 286-0769
Email: robert.ba...@iconplc.com
Web: www.iconplc.com
-----Original Message-----
From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On
Behalf Of Leonid Gibiansky
Sent: Friday, November 23, 2012 12:15 PM
To: pascal.gir...@merckgroup.com
Cc: nmusers@globomaxnm.com
Subject: Re: [NMusers] Different EBE estimation between original and enriched
dataset with MDV=1
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. * Geneva
> Tel: +41.22.414.3549
> Cell: +41.79.508.7898
>
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