RE: Parameterization!!!
From: "Gibiansky, Leonid" <gibianskyl@globomax.com>
Subject: RE: Parameterization!!!
Date: Thu, 10 May 2001 08:02:12 -0400
I have a related question:
As Nick mentioned, CL and V are often correlated. In the model development process, we start with the base model, look for the best base model, and then add covariates. For the base model, we have a choice of the correlated OMEGA and uncorrelated. Correlated OMEGA accounts for hidden (due to covariates) correlation. Later, on the covariate step, this correlation can be partly or fully explained by the common covariates (in CL and V). So the question what is better:
1. To use uncorrelated OMEGA for the base model, add all the covariates, and then try correlated OMEGA to explain remaining correlation;
2. To use full OMEGA for the base model, and then take correlation out if it is not needed on the base model step or for the final covariate model.
The concern is that with variant (1) correlation due to, say, WT could be more difficult to recover on the covariate step because correlation would be already "explained" by the OMEGA structure. On the other hand, the common practice is to get the best base model, and this includes the best structure of the random effects.
The other related problem is that with the correlated OMEGA addition of covariate to CL will affect (due to the correlated OMEGA) V as well. I tried to add a covariate to all correlated parameters in this situation, is there a better way to handle it ?
Thanks,
Leonid
Quoted reply history
-----Original Message-----
From: Nick Holford [mailto:n.holford@auckland.ac.nz]
Sent: Wednesday, May 09, 2001 5:08 PM
To: nmusers
Subject: Re: Parameterization!!!
I agree with Leonid's suggestion. I would also point out that if you do not use a BLOCK to allow correlation between your parameters your model is almost certainly more wrong than usual. I cannot imagine a realistic circumstance where CL and V would not be correlated (e.g. both will increase with increasing body size, or both will increase if F increases if the dose is oral) so always start with a BLOCK and only take it off if your data or other priors inform you that the assumption of no correlation is reasonable.
"Gibiansky, Leonid" wrote:
>
> I would guess that the difference is due to the correlation of ETAs (you may
> check it by plotting individual estimates of ETA1 vs ETA2 or CL vs V2). Try
> block structure of the OMEGA matrix. Example:
> CL=THETA(1)*EXP(ETA(1))
> V2=THETA(2)*EXP(ETA(2))
> $OMEGA BLOCK(2)
> 1
> 0.1
> 1
>
> If you use correlated structure with TRANS1 and TRANS4 (correlating pairs of
> alternative parameters) you may get closer results.
> Leonid
>
> -----Original Message-----
> From: Bachman, William [mailto:bachmanw@globomax.com]
> Sent: Wednesday, May 09, 2001 4:26 PM
> To: 'Sreenivasa Rao Vanapalli'
> Cc: 'nmusers@c255.ucsf.edu'
> Subject: RE: Parameterization!!!
>
> Sometimes one parameterization can be more stable for a given set of data
> than another parameterization. It may be related to how the random errors
> enter into the model rather than the fixed effect parameters. You could try
> reparameterizing within TRANS1 to get the more important parameters like CL.
> (You already have KA AND V2). It's less likely the peripheral parameters
> will be important in your model. They are typically poorly defined. Also,
> once you begin to explain more of the variability in your data through
> addition of covariates, it may be possible to go back and try the TRANS4
> parameterization (this time incorporating the covariates you've discovered)
> and obtain a successful minimization comparable to the TRANS1 fit.
>
> $SUB ADVAN4 TRANS1
> $PK ;reparam for CL
> CL =THETA(1)*EXP(ETA(1))
> KA =THETA(2)*EXP(ETA(2))
> K23=THETA(3)*EXP(ETA(3))
> K32=THETA(4)*EXP(ETA(4))
> V2 =THETA(5)*EXP(ETA(5))
> K=CL/V2
> S2=V2
>
> William J. Bachman, Ph.D.
> GloboMax LLC
> 7250 Parkway Dr., Suite 430
> Hanover, MD 21076
> Voice (410) 782-2212
> FAX (410) 712-0737
> bachmanw@globomax.com
>
> -----Original Message-----
> From: Sreenivasa Rao Vanapalli [mailto:svanapal@blue.weeg.uiowa.edu]
> Sent: Wednesday, May 09, 2001 3:51 PM
> To: Bachman, William
> Subject: RE: Parameterization!!!
>
> Yes I did try as you said. But the result is same. I'm really wondering what
> is going on behind the screen. TRANS1 fit gives better estimates. With
> TRANS4 I tried fixing the VD value. But V3 esimate became astronomical so
> was KA. And corresponding predicted values (more than 100 times the observed
> values!!!). I'm really not sure what to do. I need to do some covariate
> effect studies once the this model issue is settled.
>
> Sreenivasa Vanapalli
>
> -----Original Message-----
> From: Bachman, William [mailto:bachmanw@globomax.com]
> Sent: Wednesday, May 09, 2001 2:47 PM
> To: 'Sreenivasa Rao Vanapalli'
> Subject: RE: Parameterization!!!
>
> How did the objective function values and the goodness of fit plots compare
> between TRANS1 and TRANS4? Are you sure you have a global minimum in both
> fits? (try different initial estimates to verify). If TRANS1 fit is
> better, try calculating new initial estimates for TRANS4 based on the TRANS1
> final estimates and the relationships between the two parameterizations.
> Also be aware of potential for flip-flop with your model.
>
> William J. Bachman, Ph.D.
> GloboMax LLC
> 7250 Parkway Dr., Suite 430
> Hanover, MD 21076
> Voice (410) 782-2212
> FAX (410) 712-0737
> bachmanw@globomax.com
>
> -----Original Message-----
> From: Sreenivasa Rao Vanapalli [mailto:svanapal@blue.weeg.uiowa.edu]
> Sent: Tuesday, May 08, 2001 5:51 PM
> To: nmusers@c255.ucsf.edu
> Subject: Parameterization!!!
>
> Hello NMUsers
>
> I have data obtained after oral administration and trying to fit to a two
> compartment model with NONMEM. I know that the data fit to two comaprtment.
> Intitially I fitted the data with WinNonlin and now trying population
> compartmental model. When I tried with microconstants (KA, K12, K21, K, Vd)
> I could compare these estimates with WinNonlin values. But with clearance
> parameters, the estimates are quite different. The estimate for central
> compartment with clearance parameter (ADVAN4 TRANS4) was only 0.5 liters.
> Where as VD estimate with microconstant parameters (ADVAN4)was 13 liters.
> Same case with KA also. Can some one explain why this is happening?
>
> Regards
>
> Sreenivasa Rao Vanapalli, Ph.D,
> Janssen Postdoctoral Research Scholar,
> S411 PHAR, College of Pharmacy,
> University of Iowa, Iowa City, IA-52242
> 319-353-5157 (Office)
> 319-337-2687 (Home)
--
Nick Holford, Divn Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand
email:n.holford@auckland.ac.nz tel:+64(9)373-7599x6730 fax:373-7556
http://www.phm.auckland.ac.nz/Staff/NHolford/nholford.htm