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List:       sas-l
Subject:    Re: Cost analyses using PROC GENMOD using link=log and dist=gamma
From:       Sigurd Hermansen <HERMANS1 () WESTAT ! COM>
Date:       2008-04-30 23:04:27
Message-ID: CA8F89971ADA9F47A6C915BA2397844207B4271E () MAILBE2 ! westat ! com
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Amy:

See http://www.ats.ucla.edu/STAT/sas/output/sas_negbin_output.htm ,

especially,
> > 
As assumed for a negative binomial model our response variable is a count variable, \
and each subject has the same length of observation time. Had the observation time \
for subjects varied, the model would need to be adjusted to account for the varying \
length of observation time per subject. This point is discussed later in the page. \
Also, the negative binomial model, as compared to other count models (i.e., poisson \
or zero-inflated models), is assumed to be the appropriate model. In other words, we \
assume that the dependent variable is ill-dispersed (either under- or over- \
dispersed) and does not have an excessive number of zeros.
> > 

Overriding everything else, though, would be the risk of finding ostensibly \
significant (Type 2 error) but small differences in means or other statistics in two \
partitions of a large sample. The distributions of costs related to the two cohorts \
could very likely result from typical variations in samples. The confidence intervals \
of the means of costs for the two cohorts overlap. More complex models would have to \
include other covariates, not more complex assumptions about distributions, to \
explain a benefit to the intervention in some groups within the population. S

-----Original Message-----
From: owner-sas-l@listserv.uga.edu [mailto:owner-sas-l@listserv.uga.edu] On Behalf Of \
                Amy Smith
Sent: Tuesday, April 29, 2008 9:41 AM
To: SAS-L@listserv.uga.edu
Subject: Re: Cost analyses using PROC GENMOD using link=log and dist=gamma


Does anyone know a theoretical reason why it is right or wrong to use the negbin link \
and log distribution in PROC GENMOD for analysis of cost data if it seems to produce \
nice dev/df ratios?  Is there any other way to assess goodness of fit?  The gamma \
distribution is booting out my zero values. A


----- Original Message ----
From: Sigurd Hermansen <HERMANS1@WESTAT.COM>
To: SAS-L@LISTSERV.UGA.EDU
Sent: Friday, April 25, 2008 8:42:41 AM
Subject: Re: Cost analyses using PROC GENMOD using link=log and dist=gamma

Amy:
See http://www.listserv.uga.edu/cgi-bin/wa?A2=ind0601A&L=sas-l&P=R30742&D=1&
H=0&O=D&T=1&m=187776 as a starting point.
S

-----Original Message-----
From: owner-sas-l@listserv.uga.edu [mailto:owner-sas-l@listserv.uga.edu]
On Behalf Of Amy Smith
Sent: Thursday, April 24, 2008 11:43 PM
To: sas-l@listserv.uga.edu
Subject: Cost analyses using PROC GENMOD using link=log and dist=gamma


I am trying to analyze medical cost data that has about 25% zeroes and then a lot of \
low values and some extremely high values (extremely long tail on the high end).  My \
predictor is simply a cohort (control group vs intervention group).  I read that cost \
data can sometimes be analyzed with PROC GENMOD using link=log and dist=gamma, but \
the log tells me that it is removing my zero values and I do not want to delete these \
valuable observations.

proc genmod;
class cohort;
model cost = cohort / link=log dist=gamma;
lsmeans cohort / cl;

Can I add $1 to all my cost data so I don't lose my zero values or could I use a \
negative binomial link or what is recommended?  My dev/df ratio is quite close to 1 \
with the negative binomial but I don't know if it is wrong to use something that \
seems to be associated with count data.  I'm guessing that dollars don't really \
qualify as count data.

I read something about a zero-inflated gamma using a different procedure (NLMIXED \
perhaps) but I am totally lost about how to code something so complex.  NLMIXED \
baffles me.  If you recommend the zero-inflated gamma in a different procedure then \
please provide actual code that I can test.

Thank you very much, Amy



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