Date:         Thu, 20 Dec 2007 06:09:57 -0800
Reply-To:     Ryan <Ryan.Andrew.Black@GMAIL.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Ryan <Ryan.Andrew.Black@GMAIL.COM>
Organization: http://groups.google.com
Subject:      Re: Deviance Test?
Comments: To: sas-l@uga.edu
Content-Type: text/plain; charset=ISO-8859-1

On Dec 19, 3:43 pm, stringplaye...@YAHOO.COM (Dale McLerran) wrote:
> > > Well, yes, that would be the correct approach.  In a group
> > randomized
> > > trial with a non-Gaussian response, one might use the GLIMMIX
> > > procedure and with CLASS, MODEL, and RANDOM statements such as
>
> > >     class group trt <covariates>;
> > >     model y = trt <covariates> / s;
> > >     random intercept / subject=group(trt);
>
> > > In the above code, groups are nested within treatment.  This has
> > > consequence both for the appropriate denominator as well as df
> > > for an F-test examining the treatment effect.
>
> > Thank you.
>
> > Dale, I'm curious. What if you incorporated time as measured by weeks
> > into the model? How would that look? Would you need a nested variable
> > within an interaction term?
>
> > Thanks again,
>
> > Ryan
>
> In a group randomized trial with groups observed at multiple time
> points, subjects are always the groups.  And the groups are nested
> within treatment, not within treatment by time.
>
> The group randomized trial with multiple observation periods can
> get a little difficult, especially with a non-Gaussian response.
> You actually have a within-group correlation structure over time
> as well as within-individual correlation structure over time to
> contend with.  It is probably not unreasonable to assume that
> the between-group variance is the same at times i and j.  Ignoring
> for the moment within-person correlations over time, I would code
> the following:
>
>     class group trt time <covariates>;
>     model y = trt|time <covariates> / s;
>     random time / subject=group(trt) type=cs;  /* or type=ar(1) */
>
> Note that I have suggested the type=cs or type=ar(1) covariance
> structures because they give the same variance at all time points.
> Other covariance structures could be used, but one of these two would
> probably be reasonable in most situations.
>
> Now, if the response were Gaussian and we were using the MIXED
> procedure, then I would add a repeated statement which models
> the within-individual covariance structure.  Code would then be
> something like
>
>     class group trt time person <covariates>;
>     model y = trt|time <covariates> / s;
>     random time / subject=group(trt) type=cs;       /* or type=ar(1) */
>     repeated time / subject=person(group) type=cs;  /* or type=ar(1) */
>
> Code similar to this can be written for the generalized linear
> mixed models.  But there is no repeated statement in the GLIMMIX
> procedure.  In the GLIMMIX procedure, we might write
>
>     class group trt time person <covariates>;
>     model y = trt|time <covariates> / s;
>     random time / subject=group(trt) type=cs;       /* or type=ar(1) */
>     random time / subject=person(group) type=cs residual;
>
> You may wish to constrain the person variance estimates to 1 if you
> do not want a multiplicative overdispersion parameter included in
> the model.  The subject of overdispersion parameters is beyond where
> I wish to go with this response.  You can look at the GLIMMIX
> documentation for much more on the subject of overdispersion
> parameters.
>
> Dale
>
> ---------------------------------------
> Dale McLerran
> Fred Hutchinson Cancer Research Center
> mailto: dmclerra@NO_SPAMfhcrc.org
> Ph:  (206) 667-2926
> Fax: (206) 667-5977
> ---------------------------------------
>
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Thank you, Dale. This was a wonderful educational experience! -Ryan