Date: Thu, 4 Nov 2010 19:38:44 -0400
Reply-To: Jordan H <jihool3670@GMAIL.COM>
Sender: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From: Jordan H <jihool3670@GMAIL.COM>
Subject: Re: huge (>999.99) odds ratios: cause?
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Thank you, Peter, Dean, and Zack (who emailed me privately), for your
I believe I checked the cross tabs prior to sending out my original email
but I shall check again when I have the chance. The vast majority of the
variables are binary so it's likely the cross tab will only be a 4x4 matrix.
If it turns out that the odds ratios are being produced by near 0 cells, is
my only option to drop the variable, since collapsing levels of the variable
isn't a possibility with binary variables?
As per usual, thanks for the consideration. This listserv is truly a
On Tue, Nov 2, 2010 at 2:56 PM, Bross, Dean S <firstname.lastname@example.org> wrote:
> An odds ratio of 999.99 usually means to me that everybody died in that
> So the odds in that group are infinity.
> -----Original Message-----
> From: email@example.com [mailto:firstname.lastname@example.org]
> On Behalf Of Jordan H
> Sent: Tuesday, November 02, 2010 2:17 PM
> To: email@example.com
> Subject: huge (>999.99) odds ratios: cause?
> Hello, all.
> First, a little background. I've been asked to help with a project in
> the goal to develop a model that predicts high cost pharmacy
> based on a variety of variables, such co-morbidities, demographics, etc.
> do this, a multivariate regression model was used. My client is also
> interested in trying to model poor prediction within the multiple
> model. To do this, they saved the residuals from PROC REG, made an
> indicator variable for those observations with residuals greater than
> and ran a PROC LOGISTIC with the new indicator variable as the response
> variable and the original independent variables, plus additional cost
> variables, as predictors.
> The model converges and most coefficients/odds ratios look reasonable
> some appear to be errors (odds ratios of >999.99, confidence intervals
> (<0.001 - >999.99). We've checked things like multicollinearity but
> doesn't seem to be an issue.
> Does anyone have an idea as to what could be going on?
> Thank you for your consideration!