Date:         Wed, 15 Sep 2010 10:38:35 -0400
Reply-To:     R B <ryan.andrew.black@gmail.com>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         R B <ryan.andrew.black@gmail.com>
Subject:      Re: Generalized Estimating Equations (Clustering)
In-Reply-To:  <201009150344.o8ELtkrX021893@willow.cc.uga.edu>
Content-Type: multipart/alternative;

Simon,

I do not have time to read your entire post carefully, but I think I have
read enough to provide some [hopefully] useful feedback to help you get
started. Suppose your data set is structured as:

ID    Twin   X1   Y
1       1      24   0
1       2      36   1
2       1      16   1
2       2      14   1
3       1      22   0
3       2      10   1
.
.
.

ID = Dyad Identification Number
Twin = Twin Indicator
X1 = Continuous Predictor
Y = Binary Dependent Variable

If you wanted to test for the effect of X1 on the binary dependent variable,
Y, while accounting for correlation of residuals obtained from Twins, then
you could fit a generalized linear model using the following code:

GENLIN Y (REFERENCE=FIRST) WITH X1
  /MODEL X1 INTERCEPT=YES
 DISTRIBUTION=BINOMIAL LINK=LOGIT
   /REPEATED SUBJECT=ID WITHINSUBJECT=Twin SORT=YES CORRTYPE=EXCHANGEABLE
ADJUSTCORR=YES COVB=ROBUST
  /PRINT CPS DESCRIPTIVES MODELINFO FIT SUMMARY SOLUTION.

Note that ID is the SUBJECT variable, Twin is the WITHINSUBJECT variable,
and the specified correlation type is EXCHANGEABLE. The EXCHANGEABLE type
assumes there is residual corrrelation, while the default INDEPENDENT type
does not assume any such correlation.

If your dependent variable is continuous, then I suggest you consider
fitting a linear mixed model via the MIXED procedure. I prefer not to
comment any further in this particular post.

HTH,

Ryan


On Tue, Sep 14, 2010 at 11:44 PM, Simon - slmartys@gmail.com <
slmartys@gmail.com> wrote:

> * Brief review of my project:  I'm investing MZ & DZ twin pairs and using
> two dichotomous categorical variables to examine differences on several
> different IVs (some categorical).  My analytic strategy is to use SPSS GEE
> to account for non-independence of twin pairs.
>
> * Question 1: What is the appropriate "working correlation matrix"?
>
> I have a variable that identifies each individual as belonging to one dyad.
> I am using this variable as the "Subject" variable on the GEE "Repeated"
> tab.  I have another variable that arbitrarily designates one twin as Twin
> 1
> and one as Twin 2.  I have added this variable to "Within-Subjects."  What
> should I specify as the working correlation matrix?  Someone advised me
> that
> "robust estimator" is appropriate for covariance matrix, as well as the
> "independent working correlation matrix", but I am not certain that this is
> correct.  Obviously, the "within-subjects" variable is arbitrarily
> designating one half of the sample as "1" and one half as "2".  Does the
> independent working correlation matrix account for this arbitrary
> designation?  (In simple terms, what assumptions does it make?)
>
> * Question 2:  Would I need to consider changing the nature of the working
> correlation matrix depending on the type and distribution of my IV?
>
> * Question 3: The distribution of several of my IVs is extremely, extremely
> skewed and is comparable to the Poisson  distribution.  Would selecting the
> Poisson log be more robust than log-transforming the IV and using a linear
> distribution?  (I realize this is a very general question; I just wondered
> how careful I need to be.)
>
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