R: st: Poisson Regression

["Carlo Lazzaro" <carlo.lazzaro@tin.it>]

Dear Alexandra,
thank you for providing the link to the article published on BMC.
However this article:
 - simply compares different regression and survival analysis models:
 - highlight the (well known) difference between risk ratio and odd ratios
and the possible misleading consequences of mistaking one for another;
- unfortunately (as it is often the caee), does not provide you with a hard
and fast rule for your query.

I do share Marteen's view that you are the only one who can judge over the
best fitting model for your research data, considering the hypotheses you
made (that should be consistent with the theory underlying the model) and
taking a look at previous reserch strategies on the same topic you are
dealing with.	

All the Best for you and for your reserch project.

Kindest Regards,
Carlo

-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Alexandra Boing
Inviato: lunedì 14 febbraio 2011 12.31
A: statalist@hsphsun2.harvard.edu
Oggetto: Re: st: Poisson Regression

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.
> 
> I would use -adjust- to get adjusted predictions, and set
> 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 
> adjust south=1 highocc=0 black=1, by(grade) exp
> gen(pr_poiss)
> 
> logit union south grade highocc black 
> adjust south=1 highocc=0 black=1, by(grade) pr
> 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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