RE: [NMusers] Interindividual variability in residual variance

Dear Alison,

The addition of EXP(ETA(.)) is to provide a scaling for the residual error
magnitude. This scaling factor is 1 for the typical individual (ETA=0) and
the sigma estimate is thus the residual error magnitude for the typical
subject. Other subjects will have scaling factors larger or smaller than 1,
but all will be >0. I see no problem in the "exp of exp" - it does exactly
what is desired.

With the alternative model

    Y = LOG(F) + ETA(.)*EPS(1)

the typical individual (with ETA=0) is having a zero residual error
magnitude. Clearly that model does not work.

Best regards,
Mats


Mats Karlsson, PhD
Professor of Pharmacometrics
Div. of Pharmacokinetics and Drug Therapy
Dept. of Pharmaceutical Biosciences
Faculty of Pharmacy
Uppsala University
Box 591
SE-751 24 Uppsala
Sweden
phone +46 18 471 4105
fax +46 18 471 4003
mats.karlsson_at_farmbio.uu.se


        
        

        



-----Original Message-----
From: owner-nmusers_at_globomaxnm.com [mailto:owner-nmusers_at_globomaxnm.com] On
Behalf Of Alison Boeckmann
Sent: Thursday, July 12, 2007 02:04
To: Samtani, Mahesh [PRDUS]; nmusers_at_globomaxnm.com
Subject: Re: [NMusers] Interindividual variability in residual variance

1) The original email and responses can be found at
http://www.cognigencorp.com/nonmem/nm/99feb172004.html

2) Your model does not make sense. In effect
    Y = LOG(F) + EXP(ETA(.))*EPS(1)
    This is equivalent to
    EXP(Y)=EXP(LOG(F))*EXP(EXP(ETA(.))*EPS(1))
    EXP(Y)=F*EXP(EXP(ETA(.))*EPS(1))
    Note the exp of exp.

    When working with logs,an additive error term makes more sense
    Y = LOG(F) + ETA(.)*EPS(1)
    LOG(Y)=EXP(LOG(F) + ETA(.)*EPS(1))=EXP(LOG(F))*EXP(ETA(.)*EPS(1))
    LOG(Y)=F*EXP(ETA(.)*EPS(1))
    This is now a proportional error model.


On Wed, 11 Jul 2007 08:26:54 -0400, "Samtani, Mahesh [PRDUS]"
<MSamtani_at_prdus.jnj.com> said:
> Dear NMusers,<?xml:namespace prefix = o ns =
> "urn:schemas-microsoft-com:office:office" />
>
> I have run in to an old problem that Vladimir once described here on the
> users net. I am trying to implement inter-individual variability in
> residual variance and the corresponding OMEGA is not being iterated.
> Usually, in Dr. Karlsson's work this error structure is implemented on a
> proportional or proportional+additive EPS model. I am wondering if my
> problem is because I am trying to implement ETA on EPS using the
> transform both sides approach as follows?
>
>
>
> $ERROR
>
> CALLFL=0
>
> IPRED = -5
>
> IF (F.GT.0) IPRED = LOG(F)
>
> IRES=DV-IPRED
>
> W=1
>
> IWRES=IRES/W
>
> Y = IPRED + EXP(ETA(.))*EPS(1)
>
>
>
> Kindly advice...MNS
>
>
>
> --------Cut here
>
> From: "Piotrovskij, Vladimir [PRDBE]" - VPIOTROV_at_PRDBE.jnj.com
>
> Subject: [NMusers] Implementation of interindividual variability in
> residual variance
>
> Date: 2/17/2004 9:37 AM
>
>
>
> Dear NONMEM users,
>
>
>
> I am trying to implement an interidividual variability in
>
> the residual variance using an additional random effect:
>
>
>
> $ERR
>
> Y = F + EXP(ETA(.))*EPS(1)
>
>
>
> It turned out the corresponding OMEGA was not iterated, and the final
> estimate
>
> did not differ from the initial value. Below is an example control stream
>
> and the output illustrating the problem (Note, the actual model I work
> with is more complicated).
>
>
>
> Thanks in advance.
>
>
>
> Best regards,
>
> Vladimir
>
> --------Cut here
>
>
--
  Alison Boeckmann
  alisonboeckmann_at_fastmail.fm


Received on Thu Jul 12 2007 - 04:02:03 EDT