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RE: st: Overdispersed poisson regression


From   Lynsey Patterson <Lynsey.Patterson@hscni.net>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Overdispersed poisson regression
Date   Mon, 5 Sep 2011 11:41:40 +0000

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Thanks Nick. I appreciate your time.



-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox
Sent: 05 September 2011 12:33
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Overdispersed poisson regression

Overdispersion means higher variance than expected on some model. With
a Poisson, variance is equal to the mean.

I think you need advice from experts in models for counts (not me).

Nick

On Mon, Sep 5, 2011 at 12:16 PM, Lynsey Patterson
<Lynsey.Patterson@hscni.net> wrote:

> Sorry Nick,
>
> I am new to this list and I got several bounces saying the message had not been posted. I tried to find it to check and thought to be safe I would try again.
>
> I was using the term over dispersed as I know this has been applied to data from other administrations.
>
> Basically, I am trying to look at the trend in MRSA rates - hence the choice of Poisson. I then need to decide which model best fits the data - sorry for the confusion, evidently I'm not a statistician!!
>
Nick Cox

> You posted this earlier. As your variance is much less than the mean,
> why do you call the data overdispersed?
>
> There are several possible reasons why your earlier mail did not get a
> reply, ranging from many people being on vacation to the possibility
> that this is not enough information to provide well-grounded advice on
> modelling. Perhaps many of your zeros belong in a different group.
>
> Nick
>
> On Mon, Sep 5, 2011 at 11:55 AM, Lynsey Patterson
> <Lynsey.Patterson@hscni.net> wrote:
>
>> I am interested in analysing the trend in rates of meticillin resistant S. aureus (MRSA) for my region. I have several variables to include in the model – time (the year (1) and the quarter (2)), the number of episodes (3), the number of occupied bed days (the denominator (4)) and the Trust (a measure of geography (5)).
>>
>> When I summarize the rate I get a mean of 0.127/1000 bed days with a variance of 0.008. So I think a normal poisson model cannot be fitted and I need to use an over dispersed? I have read about a command for negative binomial regression – does anyone know if this is correct and what syntax I should use?
>>
>> E.G. nbreg number of episodes (3) year(1) quarter(2) trust(5), exp(occupied bed days (4))

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