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From |
Ronan Conroy <rconroy@rcsi.ie> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Binomial regression |

Date |
Mon, 6 Aug 2007 13:07:51 +0100 |

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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**References**:**Re: st: Binomial regression***From:*rgutierrez@stata.com (Roberto G. Gutierrez, StataCorp LP)

**Re: st: Binomial regression***From:*Richard Goldstein <richgold@ix.netcom.com>

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