RE: IIV on res error
Hi Yuma,
My experience is that some model modifications can greatly reduce objfn but
make prediction actually worse. I like to use repeated 2-fold cross-validation
since I am usually interested in accurate predictions for out-of-sample data.
This may or may not be what you want your model to do. Once you have decided
what you actually want your model to do then test for whatever that thing is
along with objective function, accepting into your model what improves both
measures.
Look closely as to why some individuals get higher residual error. You can put
it into or omit it from your model but you should have a good reason why. Do
you trust the doses? Are there outlier data? Are all the covariates correct?
Did people simply write down incorrect things? Look at the individuals who get
assigned high residual error. Are the data reasonable? Did somebody write down
a wrong weight or age or height or BMI?
One danger is that you mask model misspecification with IIV on residual error.
If residual error correlates with say, obesity and your model works poorly in
the obese then you get improved model fit to the data by effectively reducing
the impact of obese on the model fit by assigning them higher residual error.
You dont want to mathematically reduce the impact of those individuals that
demonstrate real shortcomings of the structural model.
Warm regards,
Douglas Eleveld
Quoted reply history
________________________________________
From: [email protected] [[email protected]] on behalf of
Y.A. Bijleveld [[email protected]]
Sent: Friday, January 16, 2015 3:09 PM
To: [email protected]
Subject: [NMusers] IIV on res error
Dear all,
I am modeling multi-center log-transformed neonatal data and have constructed a
2-compartment model with ETA’s on Cl, V1 and V2. However, when introducing
interindividual variability on the residual error the MOFV drops >150 points,
while previously significant relationships between clearance and covariates
disappear. I find it strange that the introduction of the IIV has such an
impact and don't fully understand. I have already checked the data for
(extreme) outliers.
Can anyone shed some light?
Thank you so much.
Yuma Bijleveld.
$PK
F1=(BIO1**FDS12) * (BIO2**FDS34)
TVV1=THETA(1)*(WT/70000)
V1=TVV1*EXP(ETA(1))
TVCL=THETA(2)*(WT/70000)**0.75*(GA/281)**THETA(6)
CL=TVCL*EXP(ETA(2))
TVQ=THETA(4)*(WT/70000)**0.75
Q=TVQ
TVV2=THETA(5)*(WT/70000)
V2=TVV2*EXP(ETA(3))
S1=V1
$ERROR
IPRED=LOG(0.0001)
IF(F.GT.0)IPRED=LOG(F)
IRES = DV-IPRED
W=1
IF(F.GT.0)W = SQRT(THETA(3)**2)
IWRES = IRES/W
Y = IPRED+W*EPS(1)*EXP(ETA(4))
$THETA
(0, 75.7) ;1 V1
(0, 2.09) ;2 CL
(0, 0.376) ;3 add
(0, 3) ;4 Q
(0, 31.8) ;5 V2
(0, 3.3) ;6 GA
$OMEGA BLOCK(2)
0.167 ;1 V1
0.0824 0.12 ;2 Cl
$OMEGA
0.1 ;3 V2
$OMEGA
0.1 ;4 RES
$SIGMA
1 FIX
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