Mike,
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.
Nick
[EMAIL PROTECTED] wrote:
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 Zealand
[EMAIL PROTECTED] tel:+64(9)923-6730 fax:+64(9)373-7090
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
