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Date:   Wed, 29 Aug 2007 15:02:27 -0500
Reply-To:   Mary <mlhoward@avalon.net>
Sender:   "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:   Mary <mlhoward@AVALON.NET>
Subject:   Re: AIC versus c statistic in Proc Logistic
Content-Type:   text/plain; charset="iso-8859-1"

Thanks very much, all, for your suggestions. I reran the original model and it comes much closer to the new model once the observations are the same (base model=944, additional variable model=940); I'll take a look at the values of the observations dropped as to why it changed so much.

-Mary ----- Original Message ----- From: Zack, Matthew M. (CDC/CCHP/NCCDPHP) To: Mary Sent: Wednesday, August 29, 2007 12:47 PM Subject: RE: AIC versus c statistic in Proc Logistic

Cf., comments below.

Matthew Zack

-----Original Message----- From: SAS(r) Discussion [mailto:SAS-L@LISTSERV.UGA.EDU] On Behalf Of Mary Sent: Wednesday, August 29, 2007 1:24 PM To: SAS-L@LISTSERV.UGA.EDU Subject: Re: AIC versus c statistic in Proc Logistic

Kevin,

Thanks for your reply.

Yes, 3 additional observations were dropped in the analysis- the first model used 876 of 890 observations, and the second with the additional variable used 873 of 890.

>>Redo the first analysis with fewer variables but deleting the three observations >>with missing values of the additional variable (for example, use a WHERE statement). >>Then, the AIC statistic and the c-statistic will be based on the same 873 observations. >>This probably won't change their values that much from those based on 876 observations.

Still, wouldn't one compare models using the AIC statistic?

>>Yes.

If the Type 3 statistic for the variable added is also significant, shouldn't I still conclude that the second model is the better model?

>>Yes. >> >>However, because the AIC statistic and the c-statistic are calculated differently, >>changes in their values due to adding an additional variable apparently are not linearly >>proportional, as you have shown.

>>The only "easy" way I know of to calculate the statistical significance of a c-statistic >>is indirectly through its usually asymmetrical 95% confidence interval generated from >>multiple bootstrap replications. As I recall, though only vaguely, other methods for calculating >>its statistical significance rely on either assuming bivariate normal distributions >>for the sensitivity and (1-specificity) or using a Wilcoxon statistic.

-Mary ----- Original Message ----- From: Kevin Roland Viel To: SAS-L@LISTSERV.UGA.EDU Sent: Wednesday, August 29, 2007 12:11 PM Subject: Re: AIC versus c statistic in Proc Logistic

On Wed, 29 Aug 2007, Mary wrote:

> Hi, > > I'm working on Proc Logistic and have a question about the AIC statistic versus the c statistic. > > I did take the SAS Categorical Data Analysis class, and was taught there to look for the smallest AIC statistic, and the largest c statistic. > > I'm now comparing two models: > > 1. AIC= 983.21, c statistic= .74 > 2. AIC= 940.17, c statistic= .757 > > > Model 2 has the same variables as model 1, with one additional variable, whose type 3 effect is significant at .01. > > My question is, why don't I see a bigger jump in the c statistic (about > a 2% rise) when the AIC statisitic is dropping substantially (about a 5% > drop)?

Have you verified that the same observations contribute to both models? If that one additional variable has missing data, the use of AIC is not straight forward.

Kevin

Kevin Viel, PhD Post-doctoral fellow Department of Genetics Southwest Foundation for Biomedical Research San Antonio, TX 78227


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