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Re: More Levels of Random Effects

From: Nick Holford <n.holford>
Date: Fri, 17 Oct 2008 17:51:20 +1300


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? :-)


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:
> 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 PS Your computer clock seems to be more than 2 years out of date.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:
>>> ETAM = MA1*ETA(Y)+MA2*ETA(Z)
>>> $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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Nick Holford, Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand
Received on Fri Oct 17 2008 - 00:51:20 EDT

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