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Re: More Levels of Random Effects

From: Leonid Gibiansky <LGibiansky>
Date: Thu, 16 Oct 2008 21:02:57 -0400

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).


Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
e-mail: LGibiansky at
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 PS Your computer clock seems to be more than 2 years out of date.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:
>> $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
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Received on Thu Oct 16 2008 - 21:02:57 EDT

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