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# Re: st: Poisson Regression

 From Alexandra Boing To statalist@hsphsun2.harvard.edu Subject Re: st: Poisson Regression Date Mon, 14 Feb 2011 03:30:57 -0800 (PST)

```Dear Carlos and Maarten,

thanks. I send paper for you. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC521200/
Whato do you think?

Then, with this depvar and prevalence greater than 10% can do Poisson regression? Our only I can do Poisson regression with depvar= discrete .

Maarten,

I dont understand,"curve still look resonable" and "curve from a -logit- regression, which would be the obvious" "alternative when -poisson- leads to unrealistic predictions".

Thanks, Alexandra

--- Em seg, 14/2/11, Maarten buis <maartenbuis@yahoo.co.uk> escreveu:

> De: Maarten buis <maartenbuis@yahoo.co.uk>
> Assunto: Re: st: Poisson Regression
> Para: statalist@hsphsun2.harvard.edu
> Data: Segunda-feira, 14 de Fevereiro de 2011, 9:20
> --- On Sun, 13/2/11, 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?
>
> I agree with Carlo that you need to give a more complete
> reference to the article you just refered to.
>
> The -poisson- model for binary variables is used when one
> wants to interpret coeficients as risk ratios. The problem
> is that when the prevalence is high, the predicted risks
> can easily become higher than 1. Even if the predicted
> risk
> remain less than 1, but are still high, the relationship
> between a continuous explanatory variable and your outcome
> variable can have a shape that is just too unrealistic.
> The
> 10 percent strikes me as a reasonable "rule of thumb", but
>
> there is no such thing as a "correct rule of thumb", they
> are always approximate.
>
> the other covariates at such values that the predicted
> probability will be as high as possible and plot the
> resulting curves. If the curve still look reasonable, then
> there is probably no problem. It may also help to plot the
>
> curve from a -logit- regression, which would be the
> obvious
> alternative when -poisson- leads to unrealistic
> predictions.
>
> *--------------------- begin example
> -------------------------
> sysuse nlsw88, clear
> gen byte highocc = occupation < 3 if
> !missing(occupation)
> gen byte black = race == 2 if race <=2
>
> poisson union south grade highocc black
> gen(pr_poiss)
>
> logit union south grade highocc black
> gen(pr_logit)
>
> twoway line pr* grade, sort
>     ///
>        ytitle("predicted
> probability") ///
>        legend(order( 1
> "poisson"       ///
>
>      2 "logit" ))
> *---------------------- end example
> ---------------------------
> (For more on examples I sent to the Statalist see:
> http://www.maartenbuis.nl/example_faq )
>
> Hope this helps,
> Maarten
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
> http://www.maartenbuis.nl
> --------------------------
>
>
>
>
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```