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Re: st: -binreg- (was: ORs for non-rare outcomes)


From   "Patrick Mcelduff" <Patrick.Mcelduff@man.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: -binreg- (was: ORs for non-rare outcomes)
Date   Thu, 15 Apr 2004 12:07:19 +0100

This debate has been very interesting and dear to my heart. I have 
found it very useful and have obtained some useful references. 
However, one of the worrying things for me as a Stata user is that 
ALL the GLM models that have failed for me in Stata have worked 
OK in SAS. 

Patrick



From:           	Ronán Conroy <rconroy@rcsi.ie>
To:             	"statalist hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Date sent:      	Thu, 15 Apr 2004 11:39:18 +0100
Subject:        	Re: st: -binreg- (was: ORs for non-rare outcomes)
Send reply to:  	statalist@hsphsun2.harvard.edu

> on 14/04/2004 12:01, roger webb at roger.webb@man.ac.uk wrote:
> 
> > I've found that the problems encountered in trying to fit
> > log-binomial models (using either 'binreg' or 'glm link(log)
> > family(binomial)') can be overcome by using the 'modified' Poisson
> > regression approach proposed by Zou (Am J Epidemiol 2004; 159:
> > 702-706).
> 
> And see also     
> 
> 
> Alternatives for logistic regression in cross-sectional studies: an
> empirical comparison of models that directly estimate the prevalence
> ratio
> 
> http://www.biomedcentral.com/1471-2288/3/21
> 
> The paper from Barros and Hirakata, which includes the Stata code for
> each of the models, is free (another cheer for open-access publishing)
> as against the 35 bucks that the Am J Epidemiol want in order for us
> to access research that the taxpayer has already paid for once...
> 
> Ronan M Conroy (rconroy@rcsi.ie)
> Lecturer in Biostatistics
> Royal College of Surgeons
> Dublin 2, Ireland
> +353 1 402 2431 (fax 2764)
> 
> --------------------
> Just say no to drug reps
> http://www.nofreelunch.org/
> 
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