Dear nm-users,
I'm trying to construct a NONMEM control file to be used in a cross-validation
study.
In a first problem statement I run an estimation step on a subset of my data.
In a subsequent problem statement (within the same control file) I am trying to
predict the PK of the subset that was not included in part 1.
I managed to do this by use of the MSFO option (in the first part of the
control file) and the $MSFI in de second part.
However, it appears that time-varying covariates (defined under $PK in the
first problem statement) are not evaluated when performing the predictions for
the second problem statement.
Does anyone know of a workaround for this or is there another way of combining
a fit and predict action (both on different data) within the same control-file?
Kind regards,
Pieter
--
Pieter Colin, Pharm.D., Ph.D.
Post-Doctoral researcher
(Faculty of Pharmaceutical Sciences - Ghent University)
Associate Professor
(Department of Anesthesiology - UMCG)
Cross-validation script in NM
Dear Pieter,
If your end goal is to perform k-fold cross-validation, you can use
PsN's crossval program. It is run with commands like
crossval mymodel.mod -groups=5
where mymodel.mod is any regular estimation-type control stream.
This will perform 'groups'-fold cross-validation. The prediction
models are copies of the estimation models, except that $DATA is
changed, initial estimates are automatically set to final estimates
from the estimations, and MAXEVAL is set to 0 (or corresponding for
non-classical estimation methods). $PK will be identical, so there
should be no problem with time-varying covariates. Just make sure to
delete the run folder after you have retrieved the results, because
it will be very large.
Best regards,
Kajsa
On 12/04/2014 06:03 PM, Pieter Colin
wrote:
Dear nm-users,
I’m trying to construct
a NONMEM control file to be used in a cross-validation
study.
In a first problem
statement I run an estimation step on a subset of my data.
In a subsequent problem
statement (within the same control file) I am trying to
predict the PK of the subset that was not included in part
1.
I managed to do this by
use of the MSFO option (in the first part of the control
file) and the $MSFI in de second part.
However, it appears that
time-varying covariates (defined under $PK in the first
problem statement) are not evaluated when performing the
predictions for the second problem statement.
Does anyone know of a
workaround for this or is there another way of combining a
fit and predict action (both on different data) within the
same control-file?
Kind regards,
Pieter
--
Pieter Colin, Pharm.D.,
Ph.D.
Post-Doctoral researcher
(Faculty of
Pharmaceutical Sciences – Ghent University)
Associate Professor
(Department of
Anesthesiology – UMCG)
--
-----------------------------------------------------------------
Kajsa Harling, PhD
System Developer
Department of Pharmaceutical Biosciences
Uppsala University
[email protected]
+46-(0)18-471 4308
http://www.farmbio.uu.se/research/researchgroups/pharmacometrics/
Dear Kajsa and Dennis
Thank you for your thoughts on this.
I know of (and have used several times in the past) the mentioned
functionalities in PsN and PLT-tools.
However, due to the specific nature of my problem, I'm afraid these will not
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 parameter
estimates in a subsequent run to predict the plasmaconcentrations of the
validation group.
I failed to clarify this in my previous message, but I'm predicting the
plasmaconcentrations for the validation group according to a TDM setting.
This means that for the validation group MAXEVAL=0 and only the first through
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 other
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 the
post-hoc corrected final parameter estimates of the training group.
However, when I combine these in a single control stream (as shown below) the
time-varying covariates are not taken into account for the validation group.
More specifically, the following statement (under $PK) is not evaluated for the
IDs in the validation group (statement used to switch on/off an additional 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
problem is associated with the use of MSFO=... and $MSFI in the training and
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=@ 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=@
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
...
hi Pieter,
It is not exactly clear from the NONMEM manuals what information is
included in the MSFO file, or the exact mechanism that is used to
re-implement the model, so it's hard to say if this should really work the
way you expect it to. To my knowledge (and the manual), model specification
files are intended for restarting the estimation process, and not really
for analyses where you use a different dataset in the second estimation (or
simulation) step. For an analysis such as yours, or in fact any simulation
or re-estimation analysis, I would always do the data mangling outside of
NONMEM, e.g. in R or Perl. That way you will have full control over what is
going on, and you'll use NONMEM only for what it is good at (i.e.
estimation).
hope this helps,
Ron
----------------------------------------------
Ron Keizer, PharmD PhD
Dept. of Bioengineering & Therapeutic Sciences
University of California San Francisco (UCSF)
----------------------------------------------
Quoted reply history
On Fri, Dec 5, 2014 at 11:03 AM, Pieter Colin <[email protected]> wrote:
> Dear Kajsa and Dennis
>
>
>
> Thank you for your thoughts on this.
>
> I know of (and have used several times in the past) the mentioned
> functionalities in PsN and PLT-tools.
>
> However, due to the specific nature of my problem, I’m afraid these will
> not 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
> parameter estimates in a subsequent run to predict the plasmaconcentrations
> of the validation group.
>
> I failed to clarify this in my previous message, but I’m predicting the
> plasmaconcentrations for the validation group according to a TDM setting.
>
> This means that for the validation group MAXEVAL=0 and only the first
> through 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
> other 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
> the post-hoc corrected final parameter estimates of the training group.
>
>
>
> However, when I combine these in a single control stream (as shown below)
> the time-varying covariates are not taken into account for the validation
> group.
>
> More specifically, the following statement (under $PK) is not evaluated
> for the IDs in the validation group (statement used to switch on/off an
> additional 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
> problem is associated with the use of MSFO=… and $MSFI in the training and
> 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=@ 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=@
>
> 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
>
>
>
> …
>
>
>
Dear Pieter, I think there is a mistake in the control stream. (Thank
you for including it.) The two problems use two different data sets:
RawdataCFP_cov_ext.csv RawdataCFP_xval_ext.csv
Nothing is wrong with that. However, the $INPUT statements are
not the same.
For the first problem: $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 For the second problem: $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
Notice CP and TDM are listed in the second problem but not the first.
When the problems are part of the same NONMEM run, the $PK abbreviated
code is specified only once. For the first problem, the variables of
interest are obtained this way in FSUBS:
EVID=EVTREC(NVNT,007)
UVOL=EVTREC(NVNT,009)
IND_DIA=EVTREC(NVNT,011)
DIALYSIS=EVTREC(NVNT,013)
CMT=EVTREC(NVNT,004)
If I run the second problem by itself, the variables of interest are:
EVID=EVTREC(NVNT,008)
UVOL=EVTREC(NVNT,010)
IND_DIA=EVTREC(NVNT,012)
DIALYSIS=EVTREC(NVNT,014)
CMT=EVTREC(NVNT,005)
The presence of CP causes them to be shifted over. If you have two
separate runs, then the code in FSUBS can be different, and IND_DIA
and DIALYSIS etc. will be in the right places. But when there is only
one run, the variables of interest have the wrong values in the
second problem.
The use of $MSFO/$MSFI has nothing to do with it.
I suggest that you change the second $INPUT record to specify CP=DROP.
You probably don't need TDM=DROP in the second problem; NM-TRAN will
find XVAL during the data pre-processor step, and will IGNORE/ACCEPT the
appropriate records.
As general approach to complicated models and trouble shooting: there is
no point running $ESTIM till you are sure you've got the model right!
Instead, *each* problem should display items of interest $TABLE ID TIME
IND_DIA DIALYSIS TDM etc. Now you can make sure you are getting the
correct data items from the correct records.
Alison Boeckmann
Quoted reply history
On Fri, Dec 5, 2014, at 02:03 AM, Pieter Colin wrote:
> Dear Kajsa and Dennis
>
> Thank you for your thoughts on this.
> I know of (and have used several times in the past) the mentioned
> functionalities in PsN and PLT-tools.
> However, due to the specific nature of my problem, I’m afraid these
> will not 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
> parameter estimates in a subsequent run to predict the
> plasmaconcentrations of the validation group.
> I failed to clarify this in my previous message, but I’m predicting
> the plasmaconcentrations for the validation group according to a TDM
> setting.
> This means that for the validation group MAXEVAL=0 and only the first
> through 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
> other 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 the post-hoc corrected final parameter estimates of the
> training group.
>
> However, when I combine these in a single control stream (as shown
> below) the time-varying covariates are not taken into account for the
> validation group.
> More specifically, the following statement (under $PK) is not
> evaluated for the IDs in the validation group (statement used to
> switch on/off an additional 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 problem is associated with the use of MSFO=… and $MSFI in the
> training and 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=@ 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
> .387112 ; IIV_V1
> .186287 ; IIV_V2
> .371892 ; IIV_CLOTHER
>
> $SIGMA
> 0.090199 ; Proportional plasma
> .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=@
> 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
>
> …
>
--
Alison Boeckmann
[email protected]