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Re: st: RE: Autocorrelation in Poisson regression


From   David Greenberg <dg4@nyu.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: Autocorrelation in Poisson regression
Date   Thu, 07 Aug 2008 17:44:51 -0400

In the Poisson regression model, the measured independent variables provide a source of heterogeneity. The residuals from the estimation of such a model are assumed to be independent, and the test for autocorrelation of residuals is a test of that assumption. David Greenberg, Sociology Department, New York University

----- Original Message -----
From: Kieran McCaul <kamccaul@meddent.uwa.edu.au>
Date: Thursday, August 7, 2008 5:18 pm
Subject: st: RE: Autocorrelation in Poisson regression
To: statalist@hsphsun2.harvard.edu


> >>>I have tested for overdispersion and this is not a problem.
> I'm always a bit wary of tests of overdispersion, mainly because I don't
> have a clear idea of what the power of this test is.  In other words,
> how much overdispersion would there have to be before the test was
> significant.
> 
> >>>Second, in theory, there is an unlimited number of groups that could
> be formed in any given year, and thus there appears to be no
> heterogeneity of risk, if I understand that concept correctly.
> 
> Well there must be some heterogeneity, otherwise there would be no point
> in to modelling the data.  
> 
> When I'm modelling disease incidence in a cohort of people, I'm assuming
> that everyone in the cohort is at risk of the disease, but I'm not
> assuming that they are at the same level of risk (no heterogeneity).
> I'm assuming that there is heterogeneity in risk and I'm looking for
> factors that explain this - factors associated with an increase or
> decrease in risk.
> 
> >>>As for Stas's comments, I wholeheartedly agree that the reviewer in
> question is not much of a reviewer.
> 
> I publish in medical journals and it is still the case that many medical
> journals do not employ statistical reviewers.  Consequently you can get
> reviewers comments on the statistical analysis that are idiotic or
> frankly bizarre.  Often my colleagues (medical doctors) will want me to
> simply do what the reviewer wants in order to get the paper published
> thus turning a good paper into a bad paper (I wonder how often this
> happens), whereas my response is to say "No, the reviewer is an idiot
> and it is our obligation to point this out to them".  Heated discussions
> usually follow.
> 
> Without knowing exactly what your data looks like, it's difficult to
> give you any more advice, but checking the residuals along the lines
> that David suggested should enable you to check for autocorrelation.
> 
> 
> ______________________________________________
> Kieran McCaul MPH PhD
> WA Centre for Health & Ageing (M573)
> University of Western Australia
> Level 6, Ainslie House
> 48 Murray St
> Perth 6000
> Phone: (08) 9224-2140
> Phone: -61-8-9224-2140
> email: kamccaul@meddent.uwa.edu.au
> http://myprofile.cos.com/mccaul 
> _______________________________________________
> 
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Antonio Silva
> Sent: Thursday, 7 August 2008 5:01 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: st: Autocorrelation in Poisson regression
> 
> 
> I am very impressed with the quality of responses I have gotten, thank
> you so much.
> 
> In response, I have some comments and a few more questions. First, for
> Kieran, let me clarify: My dependent variable is "# of groups founded."
> What this means is that for each year of the study (there are 40 years)
> there is a number, which represents the number of new organizations that
> come into existence in that year. So, for example, in 1965, 3 new groups
> were formed, in 1966, 2 new groups were formed, and in 1967 0 new groups
> were formed. So I have a number for each year in the period under study.
> Does that make sense?  I have tested for overdispersion and this is not
> a problem.
> 
> Second, in theory, there is an unlimited number of groups that could be
> formed in any given year, and thus there appears to be no heterogeneity
> of risk, if I understand that concept correctly. Of course, Kieran may
> feel that this particular count variable is simply not appropriate for
> use in Poisson regression, and I am curious to hear your thoughts on
> this.
> 
> As for Stas's comments, I wholeheartedly agree that the reviewer in
> question is not much of a reviewer. In fact, he/she even included in
> his/her review that he/she "was not sure if autocorrelation is even a
> problem in Poisson regression," but that I should discuss it anyway. I
> have looked everywhere, and all the books and articles I read on Poisson
> basically imply (but do not explicitly state in a way that is quotable)
> what Kieran said-they say that if a process truly is Poisson,
> autocorrelation is not a problem. I think that Stas' suggestion (that 
> I
> include some language about there not being a standard test for
> autocorrelation) is a very good one, and may well work. Though I have 
> to
> be honest, I am not sure what he means by discretization. Could you
> indulge me a little more and tell me what you mean by this?
> 
> Finally, David, can you give me an idea of how I can generate the
> deviance residuals after using the Poisson command in Stata? I thought
> this option was available for other methods but not Poisson. And what
> should I look for in the correlogram?
> 
> I am sorry to be asking such basic questions, but this is the first time
> I have ever used Poisson regression. And to be honest, the reason I am
> using it is because some other reviewer told me to because my dependent
> variable was a count variable it was the best way to go.
> 
> Thanks again. This has been very helpful and useful to me.
> 
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