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RE: More Levels of Random Effects

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
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>>>>
>>>>
>>>
>>
>


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

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