RE: st: Poisson Regression

["Visintainer, Paul" <Paul.Visintainer@baystatehealth.org>]

It's important to distinguish the method of analysis (ie., log-binomial and Poisson with robust standard errors) vs. the outcome measure and its interpretation, given the study design and assumptions.   IMO, the Reichenheim and Coutinho (2010) article is not about the appropriateness log-binomial or Poisson modeling of prevalence ratios, per se.  Rather, it's more about the appropriateness of prevalence ratios given the underlying study design and the desired measure of effect. It's important to note that the article takes a decidedly epidemiologic perspective; that is, prevalence (of a disease) is a product of incidence and time.  They seem to imply that there is little reason for computing prevalence ratio estimators, if the goal is not ultimately to make a causal statement (no matter how limited this statement may be due to the cross-sectional design). 

I don't think the article negates using log-binomial and Poisson methods for common outcomes.  I think rather the article is pointing out the importance thinking through the outcome measure: how will it be interpreted and analyzed, given the underlying study design -- always a good suggestion.

-p

 
________________________________________________
Paul F. Visintainer, PhD
Baystate Medical Center
Springfield, MA 01199

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Brendan Halpin
Sent: Monday, February 21, 2011 6:55 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Poisson Regression

On Sun, Feb 13 2011, Alexandra Boing wrote:

> I would like to know how to proceed and the justication Mathematical and
> Statistical. My dependent variable is spent on health (0=No   1=Yes).
> The prevalence was higher than 10 percent. Can I do Poisson regression?
> According to this paper published in BMC on line in 2003, registred  
> PMC521200 I can do Poisson regression with variable (0=No  1=Yes) and
> with prevalence higher than 10 percent, but other authors report that
> only I can do Poisson regression with the dependent variable= discrete
> variable and prevalence under 10 percent.
> Which is correct? And what is the explanation Mathematical and Statistical?Thanks, Alexandra

To come back belatedly to your initial question, I think this paper:

  Reichenheim and Coutinho (2010) "Measures and Models for Causal
  Inference in Cross-Sectional Studies: Arguments for the
  Appropriateness of the Prevalence Odds Ratio and Related Logistic
  Regression", BMC Medical Research Methodology

throws a lot of cold water on the idea that one should use poisson or
log-binomial instead of logistic when the baseline probability is above
0.10. They point out that while there are circumstances where log-bin,
poisson or Cox regression are correct, there is a wide range of
circumstances where logistic regression is better (and the baseline
probability is not one of the relevant criteria). 

Regards,

Brendan
-- 
Brendan Halpin,  Department of Sociology,  University of Limerick,  Ireland
Tel: w +353-61-213147 f +353-61-202569 h +353-61-338562; Room F1-009 x 3147
mailto:brendan.halpin@ul.ie  http://www.ul.ie/sociology/brendan.halpin.html

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