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Re: questions about IOV

From: Emmanuel Chigutsa <Emmanuel.Chigutsa>
Date: Wed, 11 Nov 2009 10:52:29 +0200

Hi Jia
 
Two things:
1. Do you have any reason to think that the CL (or other parameters) are =
dose dependent e.g. saturable kinetics? You will need a column in the =
dataset (DS, for dose) that specifies the AMT in each record for an =
individual. In your parameter output table, you can include DS so that =
you can see whether the parameter values are correlated with the DS as =
well. If so, then you should put the dose in your model as a fixed =
effect, rather than a random effect. You can code for an effect of dose =
on parameters (PAR) as follows:
 
TVPAR=THETA(1)*DSEFF ; Dose effect
DSEFF=THETA(2)*DS ; This assumes a linear effect of the dose on the =
typical value of the parameter
 
2. IOV is a separate issue you can investigate regardless of your dose. =
I would try the above first and then add IOV to the model. You can code =
your IOV as follows:
OCC1=0
OCC2=0
IF (TIME.LT.100) OCC1=1
IF(TIME.GE.100) OCC2=1
IIV=ETA(1)
IOV=ETA(2)*OCC1 + ETA(3)*OCC2
TVPAR=THETA(1)*EXP(IIV+IOV)
 
$OMEGA
0.1 ; IIV
0.1 ; IOV_OCC1
SAME ; IOV_OCC2 constrained to be the same as that for OCC1
 
ETA1 above will be your IIV whilst ETA2 and ETA3 will be your IOV.The =
parameters that should be estimated with IOV are the ones that you think =
can change between 2 different occasions (which may be most of your =
parameters). You need to investigate them one at a time.
 
Further reading on IOV can be found in the paper below:
Karlsson and Sheiner. The importance of modeling interoccasion variability =
in population pharmacokinetic analyses. Journal of pharmacokinetics and =
biopharmaceutics. 1993. vol 21, No. 6.
 
Regards,
 
Emmanuel
 
Emmanuel Chigutsa (BPharm. Hons)
Research Fellow, Pharmacometrics Group
Division of Clinical Pharmacology, University of Cape Town
K-45 Old Main Building, Groote Schuur Hospital
Anzio Road, Observatory, 7925
Cape Town, South Africa
Telephone: +27 214066758
Fax: +27 214066759
Mobile: +27 782826538
Email: emmanuel.chigutsa


>>> Jia Ji <jackie.j.ji
Dear All,
I am a new NONMEM user and now trying to model clnical PK data with a =
two-compartment model. More than half of patients in our trial had =
escalated dose in the second cycle. So I should have inter-occasion =
variability. But I got a couple of questions here.
First, what parameters should be estimated with IOV? I have seen some =
models with IOV on CL, but not on others (maybe because I have seen too =
few models). Now I have IOV on CL, V1 and Q and it run successfully. But =
when I run with either two of them, I got minimization problem. But, am I =
having too many IOVs in the model?
Second, I was wondering how to model IOV. Now I have the code as
; Define IOV, DESC = DOSE ESCALATION
DESC=0
IF (TIME.GE.100) DESC=1
ETCL = ETA(1) + DESC*ETA(5)
ETV1 = ETA(2) + DESC*ETA(6)
ETQ = ETA(3) + DESC*ETA(7)
ETV2 = ETA(4)
So IOV is modeled as additive relationship to IIV. But what about =
multiplying relationship? Like
; Define IOV, DESC = DOSE ESCALATION
DESC=0
IF (TIME.GE.100) DESC=1
ETCL = ETA(1)*(1+DESC*ETA(5))
ETV1 = ETA(2)*(1+DESC*ETA(6))
ETQ = ETA(3)*(1+DESC*ETA(7))
ETV2 = ETA(4)
When I run with this multiplying relationship, I got increased OFV and =
minimization terminated due to rounding error. But I didn't understand why =
it is not working.
Thank you so much for your patience and time!
Jia


 
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Received on Wed Nov 11 2009 - 03:52:29 EST

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