From: Mouksassi Mohamad-Samer <*mohamad-samer.mouksassi*>

Date: Sun, 19 Oct 2008 14:15:56 -0400

Dear Michael,

You may want to have a look at :

Laporte-Simitsidis S, Girard P, ;Mismetti P, Chabaud S, Decousus H, =

Boissel JP

Inter-study variability in population pharmacokinetic meta-analysis: =

when and how to estimate it?

J Pharm Sci. 2000 Feb;89(2):155-67. Review.

PMID: 10688745 [PubMed - indexed for MEDLINE]

Inter-Study variability was implemented in NONMEM ( and it is detailed =

in the appendix of the reference) using a similar trick to the IOV one.

Hope this helps,

Bests,

Samer

-----Original Message-----

From: owner-nmusers

Michael.J.Fossler

Sent: Fri 10/17/2008 10:26

To: Leonid Gibiansky

Cc: nmusers; Nick Holford; owner-nmusers

Subject: Re: [NMusers] More Levels of Random Effects

I suppose it really comes down to what you are going to do with the =

model.

Many times I have checked the SAME assumption when modeling

inter-occasional variability, and found that sometimes, removing it does =

indeed improve the fit significantly. In almost every case I've =

retained

it (despite the better fit) for the exact reasons Leonid cites: it makes =

your model completely data-dependent. I suppose if the model was meant =

as

a description or summary of the data, then it would not matter, but I =

make

all of my models work for a living...

There is a related topic which I'd be interested in hearing from the =

group

about. Many times, we take several Phase 1 studies and put them together =

in order to develop a population model early in development. I've =

learned

through experience to be careful when doing this, because often, one or

more studies will appear to have a different mean response for some

parameter, e.g., CL or V2. Of course, you can introduce study as a

covariate, but this intrduces the same problem as above; in a simulation =

context, which CL value is correct? There is a work-around for this (use =

both values) but this doubles the number of simulations you have to do,

and from a scientific stand-point it is not very satisfying. What we =

need

is another level of random effects at the STUDY level, similar to what =

is

routinely done when performing hierarchical modeling in something like

WinBUGS. I'd love to see this feature in a future version of NONMEM.

"Leonid Gibiansky" <LGibiansky

Sent by: owner-nmusers

17-Oct-2008 09:30

To

"Nick Holford" <n.holford

cc

"nmusers" <nmusers

Subject

Re: [NMusers] More Levels of Random Effects

Nick,

This is exactly what I meant. If you have a model for English, Irish and =

Welsh, you may at least extrapolate it to Australians and New Zealanders =

(of British descent :) ). With occasion treated as non-ordered

categorical covariate, you cannot extrapolate the model at all because

time cannot be repeated, so your covariate (occasion) will have

different value (level) at any future trial.

Leonid

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

Nick Holford wrote:

*> Leonid,
*

*>
*

*> I dont understand what you mean by "we lose predictive power of the
*

*> model: we do not know what will be
*

*> the variability on the next occasion.".
*

*>
*

*> Or are you concerned about the situation where you have say 3 =
*

occasions

*> and the IOV seems to be different on each occasion but you now want to =
*

*> predict the IOV for a future study on the 4th occasion?
*

*>
*

*> I agree it is hard to extrapolate to future occasions but this seems =
*

to

*> be just like any other non-ordered categorical covariate - e.g. if we
*

*> see differences between English, Irish and Welsh what difference would =
*

*> you expect for Russians? :-)
*

*>
*

*> Nick
*

*>
*

*>
*

*> Leonid Gibiansky wrote:
*

*>> Hi Xia, Nick
*

*>> Technically, one can use different variances on different occasions =
*

but

*>> then we loose predictive power of the model: we do not know what will =
*

be

*>> the variability on the next occasion. One can use occasion-dependent
*

IOV

*>> variance to check for trends (for example, to investigate the time
*

*>> dependence of the IOV variability, or to check whether the first
*

*>> occasion (e.g., after the first dose of a long-term study) is for =
*

some

*>> reasons different from the others) but the final model should have =
*

some

*>> condition that specifies the relations of IOV variances at different
*

*>> occasion (SAME being the simplest, most reasonable and the most-often
*

*>> used option).
*

*>>
*

*>> Thanks
*

*>> Leonid
*

*>>
*

*>> --------------------------------------
*

*>> Leonid Gibiansky, Ph.D.
*

*>> President, QuantPharm LLC
*

*>> web: www.quantpharm.com
*

*>> e-mail: LGibiansky at quantpharm.com
*

*>> tel: (301) 767 5566
*

*>>
*

*>>
*

*>>
*

*>>
*

*>> Nick Holford wrote:
*

*>>> Xia,
*

*>>>
*

*>>> There is no requirement to use the SAME option. However, it is a
*

*>>> reasonable model for IOV that it has the same variability on each
*

*>>> occasion.
*

*>>>
*

*>>> If you dont use the SAME option then you just need to estimate an
*

*>>> extra OMEGA parameter for each occasion you dont use SAME. You can
*

*>>> test if the SAME assumption is supported by your data or not by
*

*>>> comparing models with and without SAME.
*

*>>>
*

*>>> Nick
*

*>>>
*

*>>> PS Your computer clock seems to be more than 2 years out of date.
*

*>>> Your email claimed it was sent in 17 Jan 2006.
*

*>>>
*

*>>> Xia Li wrote:
*

*>>>> Dear All,
*

*>>>> Do we have to assume the variability between all occasions are the
*

*>>>> same when
*

*>>>> we estimate IOV? What will happen if I don't use the 'same'
*

*>>>> constrain in the
*

*>>>> $OMEGA BLOCK statement? Any input will be appreciated.
*

*>>>>
*

*>>>> Best,
*

*>>>>
*

*>>>> Xia Li
*

*>>>>
*

*>>>> -----Original Message-----
*

*>>>> From: owner-nmusers *

*>>>> [mailto:owner-nmusers *

*>>>> Behalf Of Johan Wallin
*

*>>>> Sent: Wednesday, October 15, 2008 9:17 AM
*

*>>>> To: nmusers *

*>>>> Subject: RE: [NMusers] More Levels of Random Effects
*

*>>>>
*

*>>>> Bill,
*

*>>>> Is it really an eta you want, or is this rather solved by different =
*

*>>>> error
*

*>>>> models for the different machines?
*

*>>>>
*

*>>>> If still want etas, one way would be to model in the same way as
*

*>>>> IOV. In the
*

*>>>> case of intermachine-variability you would have to assume the
*

*>>>> variability
*

*>>>> between all machines are the same... Or would you rather assume
*

*>>>> interindividual variability is different with
*

*>>>> different machine, and you then would want one eta for TH(X) for
*

every

*>>>> machine...? It depends on what you mean by different slope every =
*

day,

*>>>> regarding on what your experiments like, but calibration =
*

differences

*>>>> should
*

*>>>> perhaps be taken care of by looking into your error model, eta on
*

*>>>> epsilon
*

*>>>> for starters...
*

*>>>>
*

*>>>> Without knowing your structure of data, a short example of IOV-like
*

*>>>> variability would be:
*

*>>>>
*

*>>>> MA1=0
*

*>>>> MA2=0
*

*>>>> IF(MACH=1)MA1=1
*

*>>>> IF(MACH=2)MA2=1
*

*>>>> ;Intermachine variability:
*

*>>>> ETAM = MA1*ETA(Y)+MA2*ETA(Z)
*

*>>>>
*

*>>>> PAR= TH(X) *EXP(ETA(X)+ETAM)
*

*>>>>
*

*>>>> $OMEGA value1
*

*>>>> $OMEGA BLOCK(1) value2
*

*>>>> $OMEGA BLOCK(1) same
*

*>>>>
*

*>>>> /Johan
*

*>>>>
*

*>>>>
*

*>>>> _________________________________________
*

*>>>> Johan Wallin, M.Sci./Ph.D.-student
*

*>>>> Pharmacometrics Group
*

*>>>> Div. of Pharmacokinetics and Drug therapy
*

*>>>> Uppsala University
*

*>>>> _________________________________________
*

*>>>>
*

*>>>>
*

*>>>> -----Original Message-----
*

*>>>> From: owner-nmusers *

*>>>> [mailto:owner-nmusers *

*>>>> Behalf Of Denney, William S.
*

*>>>> Sent: den 15 oktober 2008 14:39
*

*>>>> To: nmusers *

*>>>> Subject: [NMusers] More Levels of Random Effects
*

*>>>>
*

*>>>> Hello,
*

*>>>>
*

*>>>> I'm trying to build a model where I need to have ETAs generated on
*

*>>>> separately for the ID and another variable (MACH). What I have is =
*

a

PD

*>>>> experiment that was run on several different machines (MACH). Each
*

*>>>> machine appears to have a different slope per day and a different
*

*>>>> calibration. I still need to keep some ETAs on the ID column, so I
*

*>>>> can't just assign MACH=ID.
*

*>>>>
*

*>>>> I've heard that there are ways to do similar to this, but I have =
*

been

*>>>> unable to find examples of how to set etas to key off of different
*

*>>>> columns.
*

*>>>>
*

*>>>> Thanks,
*

*>>>>
*

*>>>> Bill
*

*>>>> Notice: This e-mail message, together with any attachments, =
*

contains

*>>>> information of Merck & Co., Inc. (One Merck Drive, Whitehouse
*

Station,

*>>>> New Jersey, USA 08889), and/or its affiliates (which may be known
*

*>>>> outside the United States as Merck Frosst, Merck Sharp & Dohme or
*

*>>>> MSD and in Japan, as Banyu - direct contact information for
*

*>>>> affiliates is
*

*>>>> available at http://www.merck.com/contact/contacts.html) that may =
*

be

*>>>> confidential, proprietary copyrighted and/or legally privileged. It =
*

is

*>>>> intended solely for the use of the individual or entity named on =
*

this

*>>>> message. If you are not the intended recipient, and have received
*

this

*>>>> message in error, please notify us immediately by reply e-mail and
*

*>>>> then delete it from your system.
*

*>>>>
*

*>>>>
*

*>>>>
*

*>>>
*

*>>
*

*>
*

Received on Sun Oct 19 2008 - 14:15:56 EDT

Date: Sun, 19 Oct 2008 14:15:56 -0400

Dear Michael,

You may want to have a look at :

Laporte-Simitsidis S, Girard P, ;Mismetti P, Chabaud S, Decousus H, =

Boissel JP

Inter-study variability in population pharmacokinetic meta-analysis: =

when and how to estimate it?

J Pharm Sci. 2000 Feb;89(2):155-67. Review.

PMID: 10688745 [PubMed - indexed for MEDLINE]

Inter-Study variability was implemented in NONMEM ( and it is detailed =

in the appendix of the reference) using a similar trick to the IOV one.

Hope this helps,

Bests,

Samer

-----Original Message-----

From: owner-nmusers

Michael.J.Fossler

Sent: Fri 10/17/2008 10:26

To: Leonid Gibiansky

Cc: nmusers; Nick Holford; owner-nmusers

Subject: Re: [NMusers] More Levels of Random Effects

I suppose it really comes down to what you are going to do with the =

model.

Many times I have checked the SAME assumption when modeling

inter-occasional variability, and found that sometimes, removing it does =

indeed improve the fit significantly. In almost every case I've =

retained

it (despite the better fit) for the exact reasons Leonid cites: it makes =

your model completely data-dependent. I suppose if the model was meant =

as

a description or summary of the data, then it would not matter, but I =

make

all of my models work for a living...

There is a related topic which I'd be interested in hearing from the =

group

about. Many times, we take several Phase 1 studies and put them together =

in order to develop a population model early in development. I've =

learned

through experience to be careful when doing this, because often, one or

more studies will appear to have a different mean response for some

parameter, e.g., CL or V2. Of course, you can introduce study as a

covariate, but this intrduces the same problem as above; in a simulation =

context, which CL value is correct? There is a work-around for this (use =

both values) but this doubles the number of simulations you have to do,

and from a scientific stand-point it is not very satisfying. What we =

need

is another level of random effects at the STUDY level, similar to what =

is

routinely done when performing hierarchical modeling in something like

WinBUGS. I'd love to see this feature in a future version of NONMEM.

"Leonid Gibiansky" <LGibiansky

Sent by: owner-nmusers

17-Oct-2008 09:30

To

"Nick Holford" <n.holford

cc

"nmusers" <nmusers

Subject

Re: [NMusers] More Levels of Random Effects

Nick,

This is exactly what I meant. If you have a model for English, Irish and =

Welsh, you may at least extrapolate it to Australians and New Zealanders =

(of British descent :) ). With occasion treated as non-ordered

categorical covariate, you cannot extrapolate the model at all because

time cannot be repeated, so your covariate (occasion) will have

different value (level) at any future trial.

Leonid

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

Nick Holford wrote:

occasions

to

but

be

IOV

some

some

every

day,

differences

a

PD

been

contains

Station,

be

is

this

this

Received on Sun Oct 19 2008 - 14:15:56 EDT