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Date:         Wed, 26 Mar 2008 15:17:03 -0500
Reply-To:     Tom White <tw2@MAIL.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Tom White <tw2@MAIL.COM>
Subject:      Re: PROC LOGISTIC MODEL--Standardize vars?
Comments: To: Chang Chung <chang_y_chung@HOTMAIL.COM>
Content-Type: text/plain; charset="iso-8859-1"

Chang writes below:

"Forgoing a lot of data by aggregating observations into doctor (or provider) level -- this will surely eliminate biases and minimize information loss if you aggregate sensibly"

Chang, do you mean aggregate all the claims from a provider for a specific patient? (So if I have been a patient of Dr. XYZ for the past 5 years and I have about 10 claims in my file, you mean aggregate these claims?)

or do you mean aggragte all the claims from a provider? (So if provider ABC had 20 patients over the past 5 years who have generated 50 claims, do you mean aggregate these 50 claims?)

If I manage to do one or the other, then is it ok to use LOGISTIC?

But then the proble is that we don't have a FRAUD or NO_FRAUD claim status since the claims have been aggregated.

That's why i wroye to Sig becuase hw was talking about providers whereas I was talking about claims.

I have historical claimd data and the fraud status on those claims.

I don't have fraud status on a provider since a provider will not always be fraudulent.

Therefore at this point, someone please help me understand how I can aggregate the claims (and their associated fraud or no_fraud status) and still being able to predict the probability that the next CLAIM (not PROVIDER) that comes through the door is fraudulent or not?

Right now, I just don't see this aggregation concept?

I think we are mixing provider fraud (the prob. a provider is fraudulent) and claims fraud (the prob. a claim coming from a provider is fraudulent). I am interested in the later not the former (which doesn't make sense to me right atvthis moment).



But if I do this Chang, i.e. somehow take all the claims coming from a provider and aggregate them so that I have one piece of information

----- Original Message ----- From: "Chang Chung" To: SAS-L@LISTSERV.UGA.EDU, "Tom White" Subject: Re: PROC LOGISTIC MODEL--Standardize vars? Date: Wed, 26 Mar 2008 15:38:26 -0400

On Wed, 26 Mar 2008 13:42:43 -0500, Tom White wrote:

> Chang writes below: > > "you have data that are not independent observations because > a same doctor can submit multiple claims over time." > > Chang is absolutely correct. That's the nature of claim analysis. > Doctors do submit multiple claims on the same patients over time. > > Therefore, what do I do now? > > Do I toss out PROC LOGISTIC? > What do I replace it with given what I am trying to do here?

hi, Tom,

I am not sure what is the best way to approach this. You may get a better luck consulting a qualified statistician or a fraud detection expert. You can probably start with a logistic regression models and see how it performs, then try other ways and see if any of them improves the prediction performance.

Some of the other ways are: Including lagged dependent variable(s) as predictors will help; Forgoing a lot of data by aggregating observations into doctor (or provider) level -- this will surely eliminate biases and minimize information loss if you aggregate sensibly; utilizing adjustments provided by "robust" estimators; Modeling the clustering directly with mixed or hierarchical models, and so on.

On the other hand, you can go totally different ways. see if SASĀ® Fraud Management solution ( can help, which seems to be training a neural network to make predictions. hope this helps a bit.

cheers, chang

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