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Date:         Sun, 31 Jul 2005 07:48:40 -0400
Reply-To:     Peter Flom <flom@NDRI.ORG>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Peter Flom <flom@NDRI.ORG>
Subject:      Re: Logistic Regression with a Random Effect
Comments: To: davidlcassell@MSN.COM
Content-Type: text/plain; charset=US-ASCII

hein0106@UMN.EDU wrote: <<<< I have some binary data on cattle stillbirths (0 or 1) and I am looking to do a logistic regression with the data. Some cows have more than one calving so I would like to put cow in my model as a random effect.

snip >>>>> David L Cassell <davidlcassell@MSN.COM> 07/29/05 8:51 PM replied

<<<< snip

Third, if you don't have a survey sample but just observational data, then PROC GLIMMIX might be the right choice. Or not. It depends on your data.

How many cows (your independent subjects I assume) do you have?

If you have a large sample of cows, then you could do this using GEE (Generalized Estimating Equations) through PROC GENMOD. For a small number of cows, this is not a good choice.

Are you likely to have strong within-subject correlations? Then PROC GLIMMIX may not be a good choice either. (Dale will corect me if I'm wrong on this, but I seem to recall this applied to the original %GLIMMIX macro at least.)

That may leave you with PROC NLMIXED to model your process. >>>


It seemed to me from the OP that this was a situation where many cows had one calving, and some had more, but with relatively few, if any, having a lot of calvings. I admit I am reading into his/her words, but suppose the situation were as I think.....

It's my understanding that neither GEE nor NLMIXED nor GLIMMIX would not would work here. Am I wrong?


PS Welcome back from vacation

Peter L. Flom, PhD Assistant Director, Statistics and Data Analysis Core Center for Drug Use and HIV Research National Development and Research Institutes 71 W. 23rd St New York, NY 10010 (212) 845-4485 (voice) (917) 438-0894 (fax)

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