Re: st: Binomial regression

[Ronan Conroy <rconroy@rcsi.ie>]

Further to Rich's reference, here's another interesting paper on the  
same subject. All examples are illustrated with Stata commands. The  
full text is free (link below abstract)


Barros AJ, Hirakata VN. Alternatives for logistic regression in cross- 
sectional studies: an empirical comparison of models that directly  
estimate the prevalence ratio. BMC Med Res Methodol. 2003 Oct 20;3:21.



BACKGROUND: Cross-sectional studies with binary outcomes analyzed by  
logistic regression are frequent in the epidemiological literature.  
However, the odds ratio can importantly overestimate the prevalence  
ratio, the measure of choice in these studies. Also, controlling for  
confounding is not equivalent for the two measures. In this paper we  
explore alternatives for modeling data of such studies with  
techniques that directly estimate the prevalence ratio. METHODS: We  
compared Cox regression with constant time at risk, Poisson  
regression and log-binomial regression against the standard Mantel- 
Haenszel estimators. Models with robust variance estimators in Cox  
and Poisson regressions and variance corrected by the scale parameter  
in Poisson regression were also evaluated. RESULTS: Three outcomes,  
from a cross-sectional study carried out in Pelotas, Brazil, with  
different levels of prevalence were explored: weight-for-age deficit  
(4%), asthma (31%) and mother in a paid job (52%). Unadjusted Cox/ 
Poisson regression and Poisson regression with scale parameter  
adjusted by deviance performed worst in terms of interval estimates.  
Poisson regression with scale parameter adjusted by chi2 showed  
variable performance depending on the outcome prevalence. Cox/Poisson  
regression with robust variance, and log-binomial regression  
performed equally well when the model was correctly specified.  
CONCLUSIONS: Cox or Poisson regression with robust variance and log- 
binomial regression provide correct estimates and are a better  
alternative for the analysis of cross-sectional studies with binary  
outcomes than logistic regression, since the prevalence ratio is more  
interpretable and easier to communicate to non-specialists than the  
odds ratio. However, precautions are needed to avoid estimation  
problems in specific situations.


Full text is available free from:

http://www.pubmedcentral.nih.gov/articlerender.fcgi? 
tool=pubmed&pubmedid=14567763



P    Before printing, think about the environment
=================================
Ronan Conroy
rconroy@rcsi.ie
Royal College of Surgeons in Ireland
120 St Stephen's Green, Dublin 2, Ireland
+353 (0)1 402 2431
+353 (0)87 799 97 95





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