Please don't forget us Scots.
Paul
Leonid Gibiansky wrote:
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: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED]] On
Behalf Of Johan Wallin
Sent: Wednesday, October 15, 2008 9:17 AM
To: [email protected]
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: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED]] On
Behalf Of Denney, William S.
Sent: den 15 oktober 2008 14:39
To: [email protected]
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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