**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
**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.
>>>

David

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?

Peter

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
www.peterflom.com
New York, NY 10010
(212) 845-4485 (voice)
(917) 438-0894 (fax)