Date:         Sat, 8 Nov 2008 11:23:51 +0100
Reply-To:     Marta García-Granero <mgarciagranero@gmail.com>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Marta García-Granero <mgarciagranero@gmail.com>
Subject:      Re: evaluating differences in count data
In-Reply-To:  <97D6F0A82A6E894DAF44B9F575305CC90640AB25@HCAMAIL03.ochca.com>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed

Pirritano, Matthew wrote:
> Sorry, I did leave a little info out. The intervention was intended to
> increase the use of preventive medical procedures. The increased use was
> encouraged by rewarding their use with monetary incentives. The
> intervention also provided money to increase the population size
> overall, which contains a diabetic population. Lipid assays are a
> preventive measure for diabetics. So, we'd expect the number of lipid
> assays to go up if doctors are taking advantage of the monetary
> incentives. Hence the two hypothetical counts I gave below. The number
> of lipid assays went up, but is increase greater than would be expected
> if the intervention had no effect ( = the null hypothesis).
>

See chapter 6 of Statistics at Square One (section "Standard error of a
total")
http://www.bmj.com/collections/statsbk/6.dtl

They describe a simple method to compare two counts. Using your data:

* Standard error of a total (& difference between two totals) *.
DATA LIST LIST/n1 n2 (2 F8.0).
BEGIN DATA
280 365
END DATA.
COMPUTE zvalue = (n1-n2)/SQRT(n1+n2).
COMPUTE pvalue = 2*(1-CDF.NORMAL(ABS(zvalue),0,1)).
LIST.

HTH,
Marta García-Granero

>
> Thanks
> matt
>
> Matthew Pirritano, Ph.D.
> Research Analyst IV
> Medical Services Initiative (MSI)
> Orange County Health Care Agency
> (714) 834-3566
> -----Original Message-----
> From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of
> Gene Maguin
> Sent: Thursday, November 06, 2008 12:19 PM
> To: SPSSX-L@LISTSERV.UGA.EDU
> Subject: Re: evaluating differences in count data
>
> Matthew,
>
>
>>> I'm trying to find out if there is a way to do inferential tests on
>>>
> count
> data. I am comparing the number of individuals who have had one lipid
> assay
> in the year prior to an intervention with the number of individuals who
> have
> had one lipid assay and in the year after the intervention. So I have
> two
> numbers which are counts. Let's say 280 individuals with one lipid assay
> prior to the intervention and 365 individuals with an assay after the
> intervention.
>
>
> I'm confused by your description. Are you saying that you had a pool of
> people (say, N=500) who were 'assigned' to a pre-intervention lipid
> assay,
> an intervention and a post-intervention lipid assay? 280 of 500 did the
> pre-intervention assay and 365 of the 500 did the post-intervention
> assay?
> And you want to compare the proportion who completed the pretest to the
> proportion who completed the posttest?
>
> Or, do I misunderstand and you are interested in something else?
>
> Gene Maguin
>
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--
For miscellaneous statistical stuff, visit:
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