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RE: st:iccvar

From   megan rossi <>
To   <>
Subject   RE: st:iccvar
Date   Fri, 14 Dec 2012 09:06:01 +1000

I appreciate the very thorough response! When I plotted the mean of toxin A and toxin B at each time point (baseline, year 1, year 2) they looked like they correlated which is why I went for the xtmixed...
When I reshaped the data to long format and used the correlate command there is an association however this does not adjust for the fact that they are repeated measures, which makes it very bias?! Even if I include 'timepoint' (categorical variable v0, v1, v2) in the correlatecommand ie. correlate toxinA toxinB timepoint
I stll feel that the repeated measures are not accounted for, or do you think that would be suffice??


Megan Rossi APD 
PhD Candidate School of Medicine, University of Queensland
Accredited Practicing Dietitian
Princess Alexandra Hospital Nutrition & Dietetic Department
m: 0402 931 441 | e: 
"Let food be thy medicine and medicine be thy food." — Hippocrates (ca. 460 – 377 BC)
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> From:
> To:
> Subject: RE:st:iccvar
> Date: Thu, 13 Dec 2012 09:21:32 -0700
> Megan,
> "I am having is that when I correlate these at each time point seperately
> ie. 'correlate var0a var0b' where var0a is toxinA serum level at baseline
> and var0b is toxinB serum level at baseline, there is no correlation.
> Therefore is it right to assume that the association is either weak (ie.
> requires all three time points for more data) or not linear...?"
> You are correct. Not having seen any of the results, I would venture to say
> that "no correlation" between the individual time points for Toxin A and
> Toxin B means either a) the association is very weak over time (if there is
> one at all), or b) it is a non-linear association.
> Not to be mean, but I'm surprised you got to the point of running -xtmixed-
> and -iccvar- without establishing this first.
> If you want to obtain Pearson's r for the pooled data, you can -reshape-
> your data into long format (the same used for -xtmixed- with longitudinal
> data), and use -correlate-.
> As you noted in the quote above, if the association is non-linear, then one
> of the assumptions of Pearson's r is violated and that statistic is not
> appropriate. However, if the relationship is non-linear, I would think you
> would detect something in the cross-sectional correlations (i.e. between
> var0a var0b, var1a var1b, etc.). 
> It might be useful to obtain a line plot of the relationship between toxin
> levels over time. For example, if you have the data in long form, with a
> single column for toxin A (var_a) and another for toxin B (var_b), and "id"
> is your patient identifier, then you could try:
> twoway (line var_a var_b)
> If you want to examine the plot for each patient individually, try:
> twoway (line var_a var_b), by(id)
> If there truly is no relationship between the toxin levels over time, your
> plots should not display any real pattern (e.g. the proverbial "mess").
> I hope this helps.
> Robert J Fornango, PhD
> Chief Executive Officer
> F1 Analytics LLC
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