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From: Eleveld, DJ <d.j.eleveld_at_umcg.nl>

Date: Fri, 16 Jan 2015 19:27:44 +0000

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-valida=

tion since I am usually interested in accurate predictions for out-of-sampl=

e data. This may or may not be what you want your model to do. Once you hav=

e decided what you actually want your model to do then test for whatever th=

at thing is along with objective function, accepting into your model what i=

mproves 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 wh=

y. Do you trust the doses? Are there outlier data? Are all the covariates c=

orrect? Did people simply write down incorrect things? Look at the individu=

als who get assigned high residual error. Are the data reasonable? Did some=

body write down a wrong weight or age or height or BMI?

One danger is that you mask model misspecification with IIV on residual err=

or. If residual error correlates with say, obesity and your model works poo=

rly 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 res=

idual error. You dont want to mathematically reduce the impact of those ind=

ividuals that demonstrate real shortcomings of the structural model.

Warm regards,

Douglas Eleveld

________________________________________

From: owner-nmusers_at_globomaxnm.com [owner-nmusers_at_globomaxnm.com] on behalf=

of Y.A. Bijleveld [y.a.bijleveld_at_amc.uva.nl]

Sent: Friday, January 16, 2015 3:09 PM

To: nmusers_at_globomaxnm.com

Subject: [NMusers] IIV on res error

Dear all,

I am modeling multi-center log-transformed neonatal data and have construct=

ed a 2-compartment model with ETA’s on Cl, V1 and V2. However, when intro=

ducing interindividual variability on the residual error the MOFV drops >15=

0 points, while previously significant relationships between clearance and =

covariates disappear. I find it strange that the introduction of the IIV ha=

s such an impact and don't fully understand. I have already checked the dat=

a 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

________________________________

AMC Disclaimer : http://www.amc.nl/disclaimer

________________________________

________________________________

De inhoud van dit bericht is vertrouwelijk en alleen bestemd voor de geadr=

esseerde(n). Anderen dan de geadresseerde(n) mogen geen gebruik maken van d=

it bericht, het niet openbaar maken of op enige wijze verspreiden of vermen=

igvuldigen. Het UMCG kan niet aansprakelijk gesteld worden voor een incompl=

ete aankomst of vertraging van dit verzonden bericht.

The contents of this message are confidential and only intended for the eye=

s of the addressee(s). Others than the addressee(s) are not allowed to use =

this message, to make it public or to distribute or multiply this message i=

n any way. The UMCG cannot be held responsible for incomplete reception or =

delay of this transferred message.

Received on Fri Jan 16 2015 - 14:27:44 EST

Date: Fri, 16 Jan 2015 19:27:44 +0000

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-valida=

tion since I am usually interested in accurate predictions for out-of-sampl=

e data. This may or may not be what you want your model to do. Once you hav=

e decided what you actually want your model to do then test for whatever th=

at thing is along with objective function, accepting into your model what i=

mproves 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 wh=

y. Do you trust the doses? Are there outlier data? Are all the covariates c=

orrect? Did people simply write down incorrect things? Look at the individu=

als who get assigned high residual error. Are the data reasonable? Did some=

body write down a wrong weight or age or height or BMI?

One danger is that you mask model misspecification with IIV on residual err=

or. If residual error correlates with say, obesity and your model works poo=

rly 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 res=

idual error. You dont want to mathematically reduce the impact of those ind=

ividuals that demonstrate real shortcomings of the structural model.

Warm regards,

Douglas Eleveld

________________________________________

From: owner-nmusers_at_globomaxnm.com [owner-nmusers_at_globomaxnm.com] on behalf=

of Y.A. Bijleveld [y.a.bijleveld_at_amc.uva.nl]

Sent: Friday, January 16, 2015 3:09 PM

To: nmusers_at_globomaxnm.com

Subject: [NMusers] IIV on res error

Dear all,

I am modeling multi-center log-transformed neonatal data and have construct=

ed a 2-compartment model with ETA’s on Cl, V1 and V2. However, when intro=

ducing interindividual variability on the residual error the MOFV drops >15=

0 points, while previously significant relationships between clearance and =

covariates disappear. I find it strange that the introduction of the IIV ha=

s such an impact and don't fully understand. I have already checked the dat=

a 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

________________________________

AMC Disclaimer : http://www.amc.nl/disclaimer

________________________________

________________________________

De inhoud van dit bericht is vertrouwelijk en alleen bestemd voor de geadr=

esseerde(n). Anderen dan de geadresseerde(n) mogen geen gebruik maken van d=

it bericht, het niet openbaar maken of op enige wijze verspreiden of vermen=

igvuldigen. Het UMCG kan niet aansprakelijk gesteld worden voor een incompl=

ete aankomst of vertraging van dit verzonden bericht.

The contents of this message are confidential and only intended for the eye=

s of the addressee(s). Others than the addressee(s) are not allowed to use =

this message, to make it public or to distribute or multiply this message i=

n any way. The UMCG cannot be held responsible for incomplete reception or =

delay of this transferred message.

Received on Fri Jan 16 2015 - 14:27:44 EST

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