Re: $Error with log-transformation
what is your rationale to use rat data for population analysis with 6 etas?
Quoted reply history
On Mon, Apr 30, 2012 at 8:41 AM, siwei Dai <[email protected]> wrote:
> Hi, NM users:
>
> I am a new NONMEM user and wish to get help from NM experts.
>
> I have a data from rats with concentration measured in both plasma and
> cerebrospinal fluid . Compared to plasma concentration, the concentration
> in brain is relatively small (up to 30 times difference). Due to the big
> range of my data, I log-transformed my data. Below is the code I used. I
> was able to get NM to run, but obvious bias existed in goodness-of-fit
> plot. I worried that there are mistakes in my code. Could anyone take a
> look of my code especially the $ERROR code to see what is wrong?
> Also, I saw in an earlier discussion on how to get additive error with
> log-transformed data, but that was for simple models. Can anybody give some
> insights on how to do it with more complex data such as the data I have?
> Thank you in advance for your time.
>
> Siwei
>
> $SUBROUTINES ADVAN6 TOL=3
> $MODEL
> NCOMP=3
> COMP=(COMP1) ;Central compartment
> COMP=(COMP2) ;Peripheral compartment
> COMP=(COMP3) ;Brain compartment
> $PK
> TVCL=THETA(1)
> CL=TVCL*EXP(ETA(1))
> TVV1=THETA(2)
> V1=TVV1*EXP(ETA(2))
> TVQ1=THETA(3)
> Q1=TVQ1*EXP(ETA(3))
> TVV2=THETA(4)
> V2=TVV2*EXP(ETA(4))
> TVKEQ=THETA(5)
> KEQ=TVKEQ*EXP(ETA(5)) ; Equilibration rate constant trough BBB
> TVPC=THETA(6)
> PC=TVPC*EXP(ETA(6)) ; Partition coefficient at BBB
>
> K10=CL/V1
> K12=Q1/V1
> K21=Q1/V2
>
> S1=MV1
>
> $DES
> DADT(1)=-K10*A(1)-K12*A(1)+K21*A(2)-KEQ*(A(1)*PC-A(3))
> DADT(2)=K12*A(1)-K21*A(2)
> DADT(3)=KEQ*(A(1)*PC-A(3))
>
> $ERROR
>
> IF(AMT.NE.0) THEN
> IPRE=LOG(1)
> ELSE
> IPRE=LOG(F)
> ENDIF
>
> CM=0
> IF (CMT.LE.2) CM=1
> CF=0
> IF (CMT.GE.3) CF=1
> YM = IPRE+ERR(1) ; Plasma
> YF = IPRE+ERR(2) ; Brain
>
> Y=CM*YM+CF*YF
>
> $EST METHOD=1 PRINT=1 MAX=9999 SIG=3
> $THETA
> $OMEGA
> $SIGMA
>
> Here is how the data look like:
> ID TIME AMT DV_log MDV CMT
> 1 0 180 0 . 1 1
> 1 5 0 0 0.825 0 1
> 1 5 0 0 -0.127 0 3
> 1 15 0 0 0.954 0 1
> 1 15 0 0 -0.011 0 3
> 1 25 0 0 0.937 0 1
> 1 25 0 0 0.137 0 3
> 1 60 0 0 1.015 0 1
> 1 60 0 0 0.188 0 3
> 1 100 0 0 0.567 0 1
> 1 100 0 0 -0.311 0 3
> 1 150 0 0 0.378 0 1
> 1 150 0 0 -0.493 0 3
> 1 180 0 0 -0.159 0 1
> 1 180 0 0 -0.74 0 3
>
--
Indrajeet Singh,PhD
Sr. Clinical Pharmacokineticist
Abbott Labs, North Chicago, IL