Re: Describing Omega that includes both BSV and IOV
Dear SoJeong Yi,
Hyeong-Seok Lim is right, and indeed that ETA in your model is probably
accounting for both BOV and BSV.
On the other hand, if your best OFV is obtained with BOV alone (as opposed to
BSV alone or both BOV+BSV), this is telling you that the BOV differences are
more important than the BSV differences.
In my experience, this is very common for absorption, whose large variability
is often driven by accidental factors (food intake, pH in the stomach, stomach
emptying time, co-medications, moon phase :) ), rather than real differences
between the patients.
I would say that in this case modellers simply report in the paper that the
OMEGA was BOV, without any need for further explanation.
I hope this reassures you.
Paolo
Quoted reply history
On 2015/09/04 04:49, Hyeong-Seok Lim wrote:
Dear SoJeong Yi,
Sometime we cannot estimate the variances for IIV and IOV separately, although
we have multiple dosing data within each subject.
At that time, the alternative could be to combine the IIV and IOV to a single
random effect parameter, which is the way you have done.
In this case, the random effect parameter can be described as “random effect
parameter reflecting both IIV and IOV”, and so on.
Best regards,
=============================================================================================
Hyeong-Seok Lim MD PhD
Associate Professor
Department of Clinical Pharmacology and Therapeutics, Asan Medical Center,
University of Ulsan
88, Olympic-ro 43-gil, Songpa-gu, Seoul 138-736, Republic of Korea
Tel: +82-2-3010-4613
Fax: +82-2-3010-4623
LinkedIn: http://kr.linkedin.com/pub/hyeong-seok-lim/28/926/848
http://kr.linkedin.com/pub/hyeong-seok-lim/28/926/848
Email: <mailto:[email protected]>
[email protected]<mailto:[email protected]>,
[email protected]<mailto:[email protected]>
=============================================================================================
From: [email protected]<mailto:[email protected]>
[mailto:[email protected]] On Behalf Of 이소정
Sent: Friday, September 4, 2015 11:13 AM
To: [email protected]<mailto:[email protected]>
Subject: [NMusers] Describing Omega that includes both BSV and IOV
Dear all,
Currently I’m summarizing the NONMEM estimates of population PK for writing a
manuscript.
However, I wonder how to describe the omega which includes both between-subject
variability and inter-occasional variability.
The code of ‘variability’ is followed below,
$PK
IF(OCC.EQ.1) IOV = ETA(8)
IF(OCC.EQ.2) IOV = ETA(9)
IF(OCC.EQ.3) IOV = ETA(10)
….
KA = THETA(9) * EXP(IOV) (--> In final model, PK parameter was estimated like
this)
; KA = THETA(9) * EXP(ETA(4)+IOV) (--> When I used this code, there were some
problems (boundary error, large RSE (>80%), very small estimate of BSV and so
on) )
; KA = THETA(9) * EXP(ETA(4)) (--> when I used BSV only, the OFV is quite
higher than upper two cases, so I thought that IOV should be considered. )
$OMEGA BLOCK(1) SAME
$OMEGA BLOCK(1) SAME
$OMEGA BLOCK(1) 0.3
In this case, the individual eta was re-calculated in one subject according to
occasion, isn’t it?
Then, how should I describe this ‘variability’ and estimate of omega in a
manuscript?
(i.e., between subject variability containing inter-occasional variability? Or
any other appropriate term?)
I will appreciate if someone give any advice. Thanks in advance.
Best regards,
SoJeong Yi
SoJeong Yi, Ph.D
Department of Clinical Pharmacology and Therapeutics,
Seoul National University College of Medicine and Hospital
101 Daehak-ro, Jongno-gu, Seoul 110-744, Korea
Tel: 82-2-740-8291
Fax: 82-2-742-9252
C.P: 82-10-3178-4133
E-mail: <mailto:[email protected]> [email protected]<mailto:[email protected]>
--
------------------------------------------------
Paolo Denti, PhD
Pharmacometrics Group
Division of Clinical Pharmacology
Department of Medicine
University of Cape Town
K45 Old Main Building
Groote Schuur Hospital
Observatory, Cape Town
7925 South Africa
phone: +27 21 404 7719
fax: +27 21 448 1989
email: [email protected]<mailto:[email protected]>
------------------------------------------------
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