Instead of estimating the within-subject slopes, I'd do a repeated measures ANOVA. In the ANOVA, you will want subject, group, time, and a group-by-time interaction term. The reason for doing it this way is to avoid the assumption that the change is linear with time. 

On Wed, Mar 18, 2009 at 10:01 PM, Kyle Kern <[log in to unmask]> wrote:
Hi,
I am trying to set up a longitudinal tbss analysis over 4 time points contrasting a patient
group to a control group. I'm interested in change over time rather than the effect of any
specifc time point. I was planning to model the slope for each subject and then put this
into a group t-test. Would modelling the slope for each subject be the same approach as a
simple correlation where my design matrix might look like this?:

-3
-1
1
3

Would this reflect the magnitude of the slope or does it only reflect the linearity like a
correlation coefficient? I would hypothesize that patients show a steeper slope than
controls though they may show more variability, so a least squares regression slope
might detect differences but a simple correlation coefficient might not.

Also, to perform a 2 level analysis with tbss is it preferable to use randomise and feed
the within subject t-stat into the group comparison or should I use the feat gui and treat
each within subject analysis as a timeseries and feed that into a higher level mixed
effects analysis?

thanks for all the help,

Kyle Kern



--
Best Regards, Donald McLaren
=====================
D.G. McLaren
University of Wisconsin - Madison
Neuroscience Training Program
Office: (608) 265-9672
Lab: (608) 256-1901 ext 12914
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