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.
>>>

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
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