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


From   "roger webb" <roger.webb@man.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: -binreg- (was: ORs for non-rare outcomes)
Date   Wed, 14 Apr 2004 12:01:41 +0100

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). This entails 
fitting a Poisson model with a robust variance estimator, i.e.:

poisson y x1 x2..., robust irr

This produces the same Risk Ratio (and SE) as the binomial 
model, without failing to converge with covariate adjustment.

Roger Webb
University of Manchester (UK) 


On 14 Apr 04, at 11:43, Ronán Conroy wrote:

on 09/04/2004 02:18, jmpsouza@usp.br at jmpsouza@usp.br wrote:

> Dear people:
> I agree. Anyway, the -binreg- command would be really a good solution, if it
> would run more often than not run. Indeed, it seems that the odds of binreg be
> able to run is less than 1.

I agree, but don't be afraid of mixing -binreg- regressions with -logistic-.
I reviewed a good paper a while back that used -binreg- for about two thirds
of the models and, where it couldn't find a plausible solution, -logistic-
for the rest. 

Ronan M Conroy (rconroy@rcsi.ie)
Lecturer in Biostatistics
Royal College of Surgeons
Dublin 2, Ireland
+353 1 402 2431 (fax 2764)

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