RE: [NMusers] Reporting Modeling Results

Hi Leonid,

The aspect of the results that John describes as being dependent on
scaling are the (bootstrap) confidence intervals, not the parameter
estimates. I actually agree with John's original statements about not
being surprised by the inflated standard errors when the model is not
centered. Removal of normalization induces covariance in the estimation
of affected parameters. This covariance naturally leeds to inflated
standard errors (w/o bootstrapping). And as we all know, high
correlation between parameters can result in matrix singularity and
consequently failing covariance steps, consistent with John's
statements.

Best regards,
Jeroen

J. Elassaiss-Schaap
Scientist PK/PD
Organon NV
PO Box 20, 5340 BH Oss, Netherlands
Phone: + 31 412 66 9320
Fax: + 31 412 66 2506
e-mail: jeroen.elassaiss_at_organon.com

-----Original Message-----
From: owner-nmusers_at_globomaxnm.com [mailto:owner-nmusers_at_globomaxnm.com]
On Behalf Of Leonid Gibiansky
Sent: Thursday, 25 October, 2007 23:30
To: John Mondick
Cc: nmusers_at_globomaxnm.com
Subject: Re: [NMusers] Reporting Modeling Results

Hi John,
The code seems to be good, and the results, in my opinion, should not
depend on scaling. One idea: if you started both sets of problems (with
and without scaling) from the same initial conditions (while the
solutions differ by factor 5 or so), nonmem could have difficulties
finding the correct minimum when started far from optimum. If your
initial conditions were coming from 10.4 kg-normalized solution, then it

could explain wider CI for the non-normalized problem: some of those
runs did not converged or converged to a local minima. If this is true,
you may want to repeat the non-normalized set with the initial
conditions closer to the solution (if you choose to use non-normalized
problem as the final model).
Leonid

--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566




John Mondick wrote:
>
>
> $PK
>
> TVCL = THETA(1)*(WT/10.4)**0.75
> BETA = THETA(5)
> TCL = THETA(6)
> FCL = 1-BETA*EXP(-(AGE-1)*0.693/TCL)
> TVCL2 = TVCL*FCL
> CL = TVCL2*EXP(ETA(1))
>
> TVV1 = THETA(2)*(WT/10.4)
> V1 = TVV1*EXP(ETA(2))
>
> TVV2 = THETA(3)*(WT/10.4)
> V2 = TVV2*EXP(ETA(3))
>
> TVQ = THETA(4)*(WT/10.4)**0.75
> Q = TVQ
>


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Received on Thu Oct 25 2007 - 18:37:01 EDT