```Date: Wed, 15 Sep 2010 15:37:12 -0400 Reply-To: Art@DrKendall.org Sender: "SPSSX(r) Discussion" From: Art Kendall Organization: Social Research Consultants Subject: Re: Generalized Estimating Equations (Clustering) Comments: To: R B In-Reply-To: Content-type: text/html; charset=ISO-8859-1 I read the OP a little differently.  I believe the should be an additional IV nZygotes to distinguish dizyotic and monozygotic twins.
Art

On 9/15/2010 10:38 AM, R B wrote:
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
.
.
.

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
/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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