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From |
"Kieran McCaul" <kamccaul@meddent.uwa.edu.au> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: Autocorrelation in Poisson regression |

Date |
Fri, 8 Aug 2008 05:16:24 +0800 |

>>>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. _________________________________________________________________ Your PC, mobile phone, and online services work together like never before. http://clk.atdmt.com/MRT/go/108587394/direct/01/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: RE: RE: Autocorrelation in Poisson regression***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

**Re: st: RE: Autocorrelation in Poisson regression***From:*David Greenberg <dg4@nyu.edu>

**References**:**st: Autocorrelation in Poisson regression***From:*Antonio Silva <asilva100@live.com>

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