NONMEM Users Network Archive

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RE: Questions on FOCE and log transformed data

From: Gibiansky, Ekaterina <Ekaterina.Gibiansky>
Date: Wed, 25 Feb 2009 14:21:23 -0500


 Actually with CALLFL=0 in the $ERROR block, this (guarding against =
F=0
at dosing records with FLAG variable) is not needed as the code in
$ERROR is called only at observation records.

Regards,
Katya

-------------------
Ekaterina Gibiansky
Senior Director, PKPD, Modeling & Simulation
ICON Development Solutions
Ekaterina.Gibiansky

-----Original Message-----
From: owner-nmusers
On Behalf Of Luann Phillips
Sent: Wednesday, February 25, 2009 11:57 AM
To: Huali Wu
Cc: nmusers
Subject: Re: [NMusers] Questions on FOCE and log transformed data

Huali,

A quick note on item number 2. If the model is predicting F=0, the
selection of IPRED=-3 could be altering the fit of the model.

Try the following:

$ERROR
CALLFL=0

FLAG=0
IF(AMT.NE.0) FLAG=1 ;set flag=1 for dose records

;prevents log of 0 for dose records only
;changing IPRED (or F) for dose records does not change the computation
;of the objective function value.

IPRED=LOG(F+FLAG)
W=1 ;additive error model
IRES=DV-IPRED
IWRES=RES/W

Y=IPRED +EPS(1)


Changing IPRED (or F) on concentration records alters the computation of

the objective function value. This should only be used as a last resort.

If you actually predict a zero for a concentration record, I suggest
evaluating the data first. Does the data make sense or is there an error

in sample collection time or dose times (especially check for a missing
dose or an incorrect ADDL value)?

If everything is good with the data, then you may not have any other
option than to alter the predicted concentration. If this is the case,
then I suggest testing different values of IPRED using your code. Run
the model using IPRED=-3 then IPRED=-4 then IPRED=-5, etc. until =
two
runs have the same MVOF (Since the log(0)=-infinity, IPRED=-3 may =
not be

small enough). I would then use the smallest IPRED that you tested to
minimize the impact of changing a predicted concentration on your
modeling results.


Regards,

Luann Phillips
Director PK/PD
Cognigen Corporation

Huali Wu wrote:
> Dear NMusers:
>
> I have two questions regarding model fitting.
> 1. FOCE vs. FOCE with INTERACTION. I have a rich data from phase I
> study. Drug was administered by iv infusion. I used a one-compartment
> model with nonlinear clearance (Michaelis-Menten kinetics) to fit this

> data. And I tried both FOCE and FOCE with INTERACTION. The FOCE
method
> generated a reasonable fit, while FOCE with INTERACTION generated a
> biased prediction (underpredict) of concentration. I thought FOCE
> with INTERACTION usually generate better result than FOCE. Does this
> mean my model is just not good enough? I used a proportional plus
> additional residual error model.
> 2. I also tried to fit log transformed data, but in the PRED vs. DV
> plot, the points at lower concentrations are much more scattered than
> those at higher concentrations. And this forms a trend that points are

> getting closer and closer to the line as the concentration goes up.
Does
> that mean log transformation of my data is not appropriate or
something
> is wrong with my residual error model? The concentration ranges from 2

> ng/ml to 1600 ng/ml. The residual error model I used is listed as
below:
>
> $ERROR
> CALLFL=0
> IPRED=-3
> IF(F.GT.0)IPRED=LOG(F); to avoid LOG(0)run-time error
> Y=IPRED+EPS(1)
>
> Any suggestion will be highly appreciated!
>
> Huali
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Received on Wed Feb 25 2009 - 14:21:23 EST

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