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Date:   Mon, 2 Aug 2010 17:50:31 -0500
Reply-To:   Andrew Agrimson <jagrimsasl@GMAIL.COM>
Sender:   "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:   Andrew Agrimson <jagrimsasl@GMAIL.COM>
Subject:   Re: Random Effects question
Comments:   To: Bhupinder Farmaha <>
In-Reply-To:   <000601cb328c$69942d80$3cbc8880$>
Content-Type:   text/plain; charset=ISO-8859-1

Hi Bhupinder,

Thanks for the response.

I think I understand somewhat. In you're first sentence you say that if you think there's is a random effect it should be specified. Can't the argument be made that there is always an subject specific random effect? If that's the case I guess my question is when is meaningful to specify it?

I not quite sure what you meant by "if the estimates of the parameters are big enough". Would you please clarify?

Thanks, Andy

On Mon, Aug 2, 2010 at 4:48 PM, Bhupinder Farmaha <>wrote:

> Hi Andy > > Based on my knowledge, if you have random components then specific it > otherwise they will get pooled into error terms. It might make your model > more complex but make sense if the estimates of the parameters are big > enough. Otherwise you can exclude that from the random statement. > > Does it make sense ? > Bhupinder > > -----Original Message----- > From: SAS(r) Discussion [mailto:SAS-L@LISTSERV.UGA.EDU] On Behalf Of > Andrew > Agrimson > Sent: Monday, August 02, 2010 4:41 PM > To: SAS-L@LISTSERV.UGA.EDU > Subject: Random Effects question > > Hello all, > > I have a question regarding random effects. > > Originally I was building a logistic regression model with multiple > observations per subject. To account for this I included a random intercept > term to induce correlation within subjects. My main focus though was the > fixed effects and the inclusion of the random intercept term was only to > account for the within subject correlation. > > I have recently decided to role up the observations to the subject level > and fit a binomial model instead. As I was preparing to do this I began to > wonder if including a random intercept in the binomial would still be > appropriate, i.e., most similiar to the random intercept logistic model. I > guess my thoughts are that a random intercept will induce the needed > correlation within subject, but it's also a bit more than that. It's also > going to estimate the unmeasured effects. It seems that this approach would > be similar to a frailty model in survival analysis. Is this appropriate > given that the main focus is on the fixed effects? Does anybody have any > thoughts on this? > > Thanks Andy > >

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