RE: st: iccvar

[megan rossi <megan_rossi@msn.com>]

Thankyou.
Yes I am after the Pearson's r however the trouble 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 which is why I need all three timepoints in together? How do I correlate var0a, var1a, var2a with the var0b, var1b, var2b ie. toxinA at basline, year 1, year 2 with toxinB at baseline, year1, year2??
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: megan_rossi@msn.com 
"Let food be thy medicine and medicine be thy food." — Hippocrates (ca. 460 – 377 BC)

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> From: robert.fornango@f1analytics.com
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: iccvar
> Date: Wed, 12 Dec 2012 09:03:45 -0700
> 
> Megan,
> 
> -iccvar- produces the intraclass correlation coefficient (ICC) for a random
> intercept model after -xtmixed-. The ICC is the proportion of variation in
> the outcome that is attributable to differences between clusters. In this
> case, the ICC refers to the variation due to between patient differences in
> your data.
> 
> However, the ICC can also be thought of as a measure of within-cluster
> correlation. For example, when there is no between-cluster heterogeneity,
> then there are no differences across patients, and observations do not
> cluster within patients. The ICC in this case would be 0. But, as the
> between-patient differences increase, so does the correlation of values
> within patients, and the ICC increases as well.
> 
> The ICC is not the same as Pearson's r, as it calculates the covariance
> between pairs of observations using the model estimates for the mean (B),
> and standard deviations sd_(cons) and sd_(Residual). Pearson's r would be
> calculated by obtaining the sample means and standard deviations for
> different time points (in your case) to compute the covariance. Obviously,
> if you wanted a strict Pearson's correlation, you could -reshape- your data
> into wide format and obtain the correlation of observations across time
> points.
> 
> I recommend pp.58-61 in: 
> Rabe-Hesketh, Sophia, and Anders Skrondal. 2008. Multilevel and Longitudinal
> Modeling Using Stata. 2nd Edition. College Station: Stata Press. 
> The third edition of this text was released in 2012. While I'm sure the
> discussion is likely to be the same on this topic, I am unsure of the page
> numbers.
> 
> As a final note on -iccvar-: please be aware that the command only estimates
> the ICC for random intercept models, up to 4 levels. If you have a random
> intercept and random slope model, you should use -quickicc-, also by Eric C
> Hedberg.
> 
> Robert J Fornango, PhD
> Chief Executive Officer
> F1 Analytics LLC
> www.f1analytics.com
> 
> 
> 
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