From:  "Justin Wilkins" 
Subject:  [NMusers] Poor PRED estimates
Date: Thu, 22 May 2003 18:16:11 +0200

Hi all,

I'm working on a model for rifampicin in healthy volunteers, using
ADVAN4, and I'm having a great deal of trouble getting credible
population predictions - while the population DVs often go as high as
25-30 ug/mL, population predictions never go above 15 ug/mL, giving a
truncated, cone-shaped DV vs PRED plot that suggests something
exponential is wrong. IPRED values seem all right though. The data is
rich - 300 individuals sampled at least 8 times on each occasion, and
each subject being sampled on at least 2 occasions - and I've been able
to get rough parameter estimates from a noncompartmental analysis of the
information.

I only have plasma data for V2, which means one wouldn't expect much joy
from V3 or Q, but I strongly suspect a two-compartment model is
appropriate, especially since ADVAN2 PRED results are extremely poor.

Being a relative NONMEM neophyte, I'm stumped. Does anyone have any
advice? Control stream follows.

Thanks in advance!

Justin
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From: "Gobburu, Jogarao V" 
Subject:  RE: [NMusers] Poor PRED estimates
Date:  Thu, 22 May 2003 13:04:23 -0400

Hello,

  It is going to be difficult to guess what the problem might be. But I will
anyways. It appears from the stumped pop predictions that the absorption
model might need improvement. You might want to test if the models in the
following article could be of help to you. Rich absorption phase data could
be challenging sometimes. 

Holford NH, Ambros RJ, Stoeckel K. Models for describing absorption rate and
estimating extent of bioavailability: application to cefetamet pivoxil.J
Pharmacokinet Biopharm 1992 Oct;20(5):421-42

Regards,
Joga Gobburu,
Pharmacometrics,
CDER/FDA.
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From:Leonid Gibiansky 
Subject: Re: [NMusers] Poor PRED estimates
Date:  Thu, 22 May 2003 13:13:01 -0400

I think you do not need DEL here, use
$ERROR
IPRED=F
W=SQRT(THETA(7)**2+THETA(8)**2*F*F)
Y=IPRED+W*EPS(1)

But this should not be a problem. I would try FOCE (METHOD=1 INTERACTION). 
Also, sometimes log-transformation of the data is helpful (DV ---> log(DV) 
in the data file,
$ERROR
BLQ=0.01          ; something close to the LLQ
IPRED = BLQ/2 ;
IF(F.GT.BLQ/2) IPRED= F
W=SQRT(THETA(7)**2/IPRED/IPRED+THETA(8)**2)
Y=LOG(IPRED)+W*EPS(1)
)
How WRES vs PRED plot looks like ? How large are OMEGAs and SIGMA ?
Leonid
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From: "Hutmacher, Matthew [Non-Employee/1820]" 
Subject:RE: [NMusers] Poor PRED estimates
Date: Thu, 22 May 2003 12:50:02 -0500

Justin,

In addition to the comment below, and without much knowledge at all about
you problem, my guess is to look at your starting values and maybe their
units.  You have your initial estimate of CL as 21 and V2 as 0.7.  This
translates into k20~30 and thus ln(2)/k20 ~ 1.4 minutes.

Matt
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From:Mats Karlsson 
Subject:   Re: [NMusers] Poor PRED estimates
Date: Fri, 23 May 2003 06:04:35 +0200

Dear Justin,

One possibility is that there is nothing wrong at all with your fit. If you
have no covariates in your model, you would expect the plot you describe.
There should always be observed concentrations higher than PRED. The
question is whether PRED is giving unbiased predictions of you observed
data. As a rough guide, if you plot DV versus PRED (or log(DV) versus
log(PRED) is your data spans several magnitudes) a regression line should
have a slope close to unity. As IPRED looks good, you can also inspect if
all your etas are centered around zero (which they should be).

Best regards,
Mats
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From: "Justin Wilkins" 
Subject: RE: [NMusers] Poor PRED estimates
Date:Fri, 23 May 2003 11:05:46 +0200


Thanks everyone for their input, I'll have another go today. Leonid, you
asked for PRED vs WRES and the OMEGAs and SIGMA - WFN output follows.
I'm sending a JPG of the Xpose plot directly to you - if anyone wants to
have a look please let me know and I'll post it to the list.

THETA:      KA          CL          V2          V3          Q
ALAG1       ADD         EXP         
ETA:        
ERR:        
rif_comm_27_e2.lst      19161.187       eval=1221 sig=+5.7 sub=300
obs=7291 CCIL=NNNN NV1.1 PIV1.1 
THETA     = 0.449       8.81        5.59        16.1        6.37
0.191       0.509       0.287
ETASD     = 0.505964    0.384708    0.766159    0.158745
ERRSD     = 1
THETA:se% = 10.9        2.4         7.0         9.3         13.1
6.1         12.8        4.4
OMEGA:se% = 33.6        12.2        32.7        67.9
SIGMA:se% = 0.0

MINIMIZATION SUCCESSFUL
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From: Leonid Gibiansky 
Subject:RE: [NMusers] Poor PRED estimates
Date:Fri, 23 May 2003 09:36:33 -0400

Justin
Looking on the WRES vs. PPED plot (slightly skewed that suggests that 
log-transformation may help) and OMEGAs, SIGMA below, I would agree with 
Mats that there is nothing really wrong with your fit. Everything looks 
reasonable, except too large OMEGA3 (an may be OMEGA1, but OMEGA1 describes 
KA, and it is reasonable for KA to have large variability). It does not 
mean that you cannot improve the model. I would try log transformation and 
FOCE INTERACTION,  often they improve variability estimates. FOCE is known 
to give better results if OMEGAs are high (as in your case) and with the 
rich data (again, this is your case). Certainly, I would check whether 
distributions of random effects are centered around zero. If not, then 
again, log-transformation and FOCE could help.
One the other note, if you have problems with Cmax, sometimes zero-order 
absorption may help to better estimate it (but you should be able to see 
this on the  DV vs. time plot)
Leonid.
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From:VPIOTROV@PRDBE.jnj.com
Subject: RE: [NMusers] Poor PRED estimates
Date:Mon, 26 May 2003 15:24:35 +0200

After log transformation the residual error often becomes additive (constant
variance). In this case just FOCE method is sufficient. ITERATION option in
$EST statement is not needed.


Best regards,
Vladimir
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From: Leonid Gibiansky 
Subject:  RE: [NMusers] Poor PRED estimates
Date:Tue, 27 May 2003 09:13:12 -0400

Actually, the ERROR model was more complicated:

W=SQRT(THETA(7)**2/IPRED/IPRED+THETA(8)**2)
Y=LOG(IPRED)+W*EPS(1)

so INTERACTION is still helpful. If W=const, then it can be removed (but it 
never hurts, as far as I understood, and does not add CPU time)

Leonid
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