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Re: More Levels of Random Effects

From: Michael.J.Fossler
Date: Mon, 20 Oct 2008 11:18:49 -0400

Hi Nick;

In your example, I would also do the latter, since I would have a rough
idea of the proportion of each of those groups living in the UK. But, I am
not sure your example is analagous to IOV, principally because, in your
example of Scots, Irish and Welsh, time is not involved. If you have 4
occasions, and you fit 4 variability terms as separate (no BLOCK SAME)
don't they now become ordered categories? If there is a definite pattern
to the variability changes as a function of time, wouldn't that change be
better modeled explicitly as a time-dependent change, rather than in an
implicit way?

I think your suggestion of running simulations based on the 1-4th occasion
when extrapolating is reasonable; however, the number of simulations that
might have to be performed may, in certain cases quickly add up.

Mike Fossler

"Nick Holford" <n.holford
Sent by: owner-nmusers
17-Oct-2008 15:38
"nmusers" <nmusers

Re: [NMusers] More Levels of Random Effects


So how do you deal with other non-ordered categorical variables? Suppose
you do your studies in Scotland, Ireland and Wales then need to predict
what will happen in England? Assuming you found 'significant'
differences in between subject variability in clearance between the
Scots, Irish and Welsh and wanted to predict a population in England do
you think it would better to take the average of the Scots, Irish and
Welsh (equivalent to using SAME) or do you think it would be better to
randomly choose from the 3 groups knowing that representatives of these
3 groups might be found living in England?

I would think the latter approach would be more realistic. I would
consider doing something similar for between occasion variability (aka
IOV) if I find 'significant' differences across 3 occasions and need to
predict a study which has 4 occasions. Rather than assume the 4th
occasion is the average of the other 3 I would consider randomly
assigning the 4th occasion data item to 1, 2 or 3.


> 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...

Nick Holford, Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New

Received on Mon Oct 20 2008 - 11:18:49 EDT

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