Date: Wed, 25 Feb 1998 12:44:35 -0600
Reply-To: "Seltzer, Jon D." <SeltzerJD@PHIBRED.COM>
Sender: "SAS(r) Discussion" <SAS-L@UGA.CC.UGA.EDU>
From: "Seltzer, Jon D." <SeltzerJD@PHIBRED.COM>
Subject: Re: GENMOD v GLM, redux
Content-Type: text/plain
Actually it has been my experience that the Glimmix Macro with proc
mixed falls apart with small sample size and the Power becomes very
poor. I have also found that it can give some very strange results when
doing a split plot logistic regression.
I would only recommend using the Genmod proc on count data that is
Poisson or Negative Binomally distributed. If the count data is
normally distributed then proc mixed is fine.
> ----------
> From: Louise Lawson[SMTP:llawson@MRG.SOPH.UAB.EDU]
> Sent: Wednesday, February 25, 1998 4:19 AM
> To: SAS-L@VM.MARIST.EDU
> Subject: GENMOD v GLM, redux
>
> Thanks to everyone who set me straight on GLM. I have to admit that
> I think in terms of repeated measures analysis, and thus evaluated
> GLM from that perspective. I apologize if I confused any of my
> fellow students.
>
> Here's a second try, using all the helpful information y'all sent.
> Again, I would appreciate your corrections if I still have
> it wrong.
>
>
> GLM uses least squares with an identity link to fit a generalized
> linear model. The assumptions are that the dependent variable is
> normally distibuted (the jury is divided on how and when this
> assumption can be violated) and that residuals are i.i.d. random
> variables and sum to zero. GENMOD is also a generalized linear
> model, which uses maximum likelihood estimation and allows you to
> specify the distribution of your dv and the link function you want
> to use. Jon uses it for any type of count data, and I tend to use it
> if I need to actually calculate a prevalence rate ratio (as opposed
> to estimating it with proc LOGISTIC).
>
> Repeated measures analysis with GLM is an ANOVA model that expects
> the dv to be continuous and normally distributed, the predictors to
> be categorical and requires that the cluster sizes be equal. It is
> computationally the least expensive, and what you want to use when
> the above requirements are met (which has never happened for me,
> but I can dream). GLM has a random statement, but you really
> shouldn't use it if you have important random effects (see below).
>
> In GENMOD, the repeated statement invokes a marginal generalized
> linear model (aka GEE, generalized estimating equations) that treats
> within subject effects as nuisance parameters. You are not required
> to have a balanced design, and this is theoretically the best model
> to use if you have a few repeated measures with a large number of
> subjects . GENMOD cannot model random effects.
>
> If you have random effects, you need to use proc MIXED, which also
> requires a continuous, normal dv. If this isn't true in your case,
> but you do have random effects, you can model them with the GLIMMIX
> macro, which invokes proc MIXED. You also want to use proc MIXED
> or GLIMMIX if you have repeated measures with a small sample size or
> a large number of observations relative to the number of
> experimental units (subjects), or if you're specifically interested
> in testing within subject (repeated statment) or between subject
> (random statement) effects.
>
> M. Louise Lawson, MPH
> Progammer/Analyst II
> University of Alabama at Birmingham
> Department of Epidemiology
> Mortimer Jordan Hall, Room 108
> Birmingham, AL 35294-2010
> (205)975-5716
>
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