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From: Pieter Colin <Pieter.Colin_at_UGent.be>

Date: Fri, 5 Dec 2014 11:03:12 +0100

Dear Kajsa and Dennis

Thank you for your thoughts on this.

I know of (and have used several times in the past) the mentioned functiona=

lities in PsN and PLT-tools.

However, due to the specific nature of my problem, I'm afraid these will no=

t work for me.

Allow me to further clarify my problem. (For clarity, I've included a piece=

of my control stream at the bottom of this message.)

As Dennis pointed out, I'm fitting a training group and use the final param=

eter estimates in a subsequent run to predict the plasmaconcentrations of t=

he validation group.

I failed to clarify this in my previous message, but I'm predicting the pla=

smaconcentrations for the validation group according to a TDM setting.

This means that for the validation group MAXEVAL=0 and only the first thr=

ough sample per ID is included in the dataset as an observation event(EVID=

=0 and MDV=0).

It goes without saying that the objective is to accurately predict the othe=

r plasmaconcentrations (EVID=2 and MDV=1) for the IDs in the validation=

group.

Now to get to the problem. I tried this approach with two separate control =

streams and it works.

I.e. plasmaconcentrations are predicted for the validation group based on t=

he post-hoc corrected final parameter estimates of the training group.

However, when I combine these in a single control stream (as shown below) t=

he time-varying covariates are not taken into account for the validation gr=

oup.

More specifically, the following statement (under $PK) is not evaluated for=

the IDs in the validation group (statement used to switch on/off an additi=

onal CL due to hemodialysis).

CL_DIA = 0

IF(DIALYSIS.EQ.1) CL_DIA = THETA(6)

IND=0

IF(IND_DIA.EQ.1) IND=1

This causes the hemodialysis moments to be ignored by NM in the validation =

group when using the control stream as shown below.

Since it worked for me using separate control streams, it seems that the pr=

oblem is associated with the use of MSFO=... and $MSFI in the training an=

d validation set, respectively.

Do any of you have a specific solution for this problem or could shed some =

light on specific behavior of the $MSFI option in NM which might be causing=

this?

Kind regards,

Pieter Colin

$PROBLEM No covariates

;; 1. Based on:

;; COMMENT:

;--------------------------------------------------------------------------=

-------

;----------------------- FIT XVAL -------------------------------------=

-------

;--------------------------------------------------------------------------=

-------

$INPUT ID TIME DV CMT AMT RATE EVID MDV UVOL EXTRA IND_DIA OCC

DIALYSIS ANALYSIS BV MISSING AGE WGT HGT BMI BSA SOFA M1F2 GFR =

XVAL

$DATA RawdataCFP_cov_ext.csv

IGNORE=_at_ IGNORE(MISSING.EQ.1) ;Exclude missing values

IGNORE(CMT.GT.3) ;Exclude CSF sample

IGNORE(XVAL.EQ.1) REWIND

$SUBROUTINE ADVAN13 TOL=12

$MODEL COMP(CENTRAL,DEFOBS,DEFDOSE) COMP(PERIPH)

COMP(URINE,INITIALOFF)

$PK

;------------- Calculation of Time After Dose ------------

IF (EVID.EQ.1.OR.EVID.EQ.4) THEN

TDOS=TIME

TAD=0.0

ENDIF

IF (EVID.NE.1.AND.EVID.NE.4) TAD=TIME-TDOS

TVCLOTHER =THETA(1)

CLOTHER =TVCLOTHER*EXP(ETA(4))

TVCL = THETA(2)

CL = TVCL*EXP(ETA(1))

TVV1 = THETA(3)

V1 =TVV1*EXP(ETA(2))

TVV2 =THETA(4)

V2 =TVV2*EXP(ETA(3))

TVQ =THETA(5)

Q =TVQ

;------------- Dialysis submodel ------------------------

CL_DIA = 0

IF(DIALYSIS.EQ.1) CL_DIA = THETA(6)

IND=0

IF(IND_DIA.EQ.1) IND=1

S1=V1

S3=UVOL

K10=CLOTHER/V1

K12=Q/V1

K21=Q/V2

K13=CL/V1

K11=CL_DIA/V1

$DES

DADT(1)=-K12*A(1)+K21*A(2)-K10*A(1)-K13*A(1)-K11*A(1)*IND

DADT(2)=K12*A(1)-K21*A(2)

DADT(3)=K13*A(1)

$ERROR

IPRED = 1E-3

IF(F.GT.0) IPRED=F

Y = IPRED*(1+EPS(1))

IRES = DV-IPRED

IWRES = IRES/(IPRED*SQRT(SIGMA(1,1)))

IF(CMT.EQ.3) THEN

Y = IPRED*(1+EPS(2))

IRES = DV-IPRED

IWRES = IRES/SQRT(IPRED*IPRED*SIGMA(2,2))

ENDIF

$THETA

(1E-9,1.097450) ; CLOTHER; L/h

(1E-9,2.124530) ; CL; L/h

(1E-9,8.640870) ; V1; L

(1E-9,18.58180) ; V2; L

(1E-9,34.13580) ; Q; L/h

(1E-9,4.046690) ; CL_DIA; L/h

$OMEGA

1.265890 ; IIV_CL

0.387112 ; IIV_V1

0.186287 ; IIV_V2

0.371892 ; IIV_CLOTHER

$SIGMA

0.090199 ; Proportional plasma

0.106711 ; Proportional urine

$ESTIMATION SIG=2 MAX=9999 METHOD=1 SORT INTERACTION POSTHOC PRINT==

1

MSFO=run61.msf

;--------------------------------------------------------------------------=

-------

;----------------------- POST HOC -------------------------------------=

-------

;--------------------------------------------------------------------------=

-------

$PROBLEM PREDICT XVAL1

$INPUT ID TIME DV CP CMT AMT RATE EVID MDV UVOL EXTRA IND_DIA OCC

DIALYSIS ANALYSIS BV MISSING AGE WGT HGT BMI BSA SOFA M1F2 GFR =

TDM XVAL

$DATA RawdataCFP_xval_ext.csv

IGNORE=_at_

IGNORE(MISSING.EQ.1) ;Exclude missing values

IGNORE(CMT.GT.3) ;Exclude CSF sample

IGNORE(XVAL.NE.1) REWIND

$MSFI run61.msf

$ESTIMATION SIG=2 MAX=0 METHOD=1 SORT INTERACTION POSTHOC PRINT=1

...

Received on Fri Dec 05 2014 - 05:03:12 EST

Date: Fri, 5 Dec 2014 11:03:12 +0100

Dear Kajsa and Dennis

Thank you for your thoughts on this.

I know of (and have used several times in the past) the mentioned functiona=

lities in PsN and PLT-tools.

However, due to the specific nature of my problem, I'm afraid these will no=

t work for me.

Allow me to further clarify my problem. (For clarity, I've included a piece=

of my control stream at the bottom of this message.)

As Dennis pointed out, I'm fitting a training group and use the final param=

eter estimates in a subsequent run to predict the plasmaconcentrations of t=

he validation group.

I failed to clarify this in my previous message, but I'm predicting the pla=

smaconcentrations for the validation group according to a TDM setting.

This means that for the validation group MAXEVAL=0 and only the first thr=

ough sample per ID is included in the dataset as an observation event(EVID=

=0 and MDV=0).

It goes without saying that the objective is to accurately predict the othe=

r plasmaconcentrations (EVID=2 and MDV=1) for the IDs in the validation=

group.

Now to get to the problem. I tried this approach with two separate control =

streams and it works.

I.e. plasmaconcentrations are predicted for the validation group based on t=

he post-hoc corrected final parameter estimates of the training group.

However, when I combine these in a single control stream (as shown below) t=

he time-varying covariates are not taken into account for the validation gr=

oup.

More specifically, the following statement (under $PK) is not evaluated for=

the IDs in the validation group (statement used to switch on/off an additi=

onal CL due to hemodialysis).

CL_DIA = 0

IF(DIALYSIS.EQ.1) CL_DIA = THETA(6)

IND=0

IF(IND_DIA.EQ.1) IND=1

This causes the hemodialysis moments to be ignored by NM in the validation =

group when using the control stream as shown below.

Since it worked for me using separate control streams, it seems that the pr=

oblem is associated with the use of MSFO=... and $MSFI in the training an=

d validation set, respectively.

Do any of you have a specific solution for this problem or could shed some =

light on specific behavior of the $MSFI option in NM which might be causing=

this?

Kind regards,

Pieter Colin

$PROBLEM No covariates

;; 1. Based on:

;; COMMENT:

;--------------------------------------------------------------------------=

-------

;----------------------- FIT XVAL -------------------------------------=

-------

;--------------------------------------------------------------------------=

-------

$INPUT ID TIME DV CMT AMT RATE EVID MDV UVOL EXTRA IND_DIA OCC

DIALYSIS ANALYSIS BV MISSING AGE WGT HGT BMI BSA SOFA M1F2 GFR =

XVAL

$DATA RawdataCFP_cov_ext.csv

IGNORE=_at_ IGNORE(MISSING.EQ.1) ;Exclude missing values

IGNORE(CMT.GT.3) ;Exclude CSF sample

IGNORE(XVAL.EQ.1) REWIND

$SUBROUTINE ADVAN13 TOL=12

$MODEL COMP(CENTRAL,DEFOBS,DEFDOSE) COMP(PERIPH)

COMP(URINE,INITIALOFF)

$PK

;------------- Calculation of Time After Dose ------------

IF (EVID.EQ.1.OR.EVID.EQ.4) THEN

TDOS=TIME

TAD=0.0

ENDIF

IF (EVID.NE.1.AND.EVID.NE.4) TAD=TIME-TDOS

TVCLOTHER =THETA(1)

CLOTHER =TVCLOTHER*EXP(ETA(4))

TVCL = THETA(2)

CL = TVCL*EXP(ETA(1))

TVV1 = THETA(3)

V1 =TVV1*EXP(ETA(2))

TVV2 =THETA(4)

V2 =TVV2*EXP(ETA(3))

TVQ =THETA(5)

Q =TVQ

;------------- Dialysis submodel ------------------------

CL_DIA = 0

IF(DIALYSIS.EQ.1) CL_DIA = THETA(6)

IND=0

IF(IND_DIA.EQ.1) IND=1

S1=V1

S3=UVOL

K10=CLOTHER/V1

K12=Q/V1

K21=Q/V2

K13=CL/V1

K11=CL_DIA/V1

$DES

DADT(1)=-K12*A(1)+K21*A(2)-K10*A(1)-K13*A(1)-K11*A(1)*IND

DADT(2)=K12*A(1)-K21*A(2)

DADT(3)=K13*A(1)

$ERROR

IPRED = 1E-3

IF(F.GT.0) IPRED=F

Y = IPRED*(1+EPS(1))

IRES = DV-IPRED

IWRES = IRES/(IPRED*SQRT(SIGMA(1,1)))

IF(CMT.EQ.3) THEN

Y = IPRED*(1+EPS(2))

IRES = DV-IPRED

IWRES = IRES/SQRT(IPRED*IPRED*SIGMA(2,2))

ENDIF

$THETA

(1E-9,1.097450) ; CLOTHER; L/h

(1E-9,2.124530) ; CL; L/h

(1E-9,8.640870) ; V1; L

(1E-9,18.58180) ; V2; L

(1E-9,34.13580) ; Q; L/h

(1E-9,4.046690) ; CL_DIA; L/h

$OMEGA

1.265890 ; IIV_CL

0.387112 ; IIV_V1

0.186287 ; IIV_V2

0.371892 ; IIV_CLOTHER

$SIGMA

0.090199 ; Proportional plasma

0.106711 ; Proportional urine

$ESTIMATION SIG=2 MAX=9999 METHOD=1 SORT INTERACTION POSTHOC PRINT==

1

MSFO=run61.msf

;--------------------------------------------------------------------------=

-------

;----------------------- POST HOC -------------------------------------=

-------

;--------------------------------------------------------------------------=

-------

$PROBLEM PREDICT XVAL1

$INPUT ID TIME DV CP CMT AMT RATE EVID MDV UVOL EXTRA IND_DIA OCC

DIALYSIS ANALYSIS BV MISSING AGE WGT HGT BMI BSA SOFA M1F2 GFR =

TDM XVAL

$DATA RawdataCFP_xval_ext.csv

IGNORE=_at_

IGNORE(MISSING.EQ.1) ;Exclude missing values

IGNORE(CMT.GT.3) ;Exclude CSF sample

IGNORE(XVAL.NE.1) REWIND

$MSFI run61.msf

$ESTIMATION SIG=2 MAX=0 METHOD=1 SORT INTERACTION POSTHOC PRINT=1

...

Received on Fri Dec 05 2014 - 05:03:12 EST

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