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st: RES: RE: Poisson Regression
José Maria Pacheco de Souza <firstname.lastname@example.org>
st: RES: RE: Poisson Regression
Mon, 14 Feb 2011 17:15:05 -0200
Dear Alexandra and Paul:
The user written -oddsrisk- by Joseph M. Hilbe, Arizona State University
---- Hilbe@asu.edu; email@example.com may be a good approach:
"Conversion from Logistic Odds Ratios to Risk Ratios
oddsrisk y(1/0) riskfactor(1/0) varlist [fw=countvariable] <if> <in>
oddsrisk converts logistic regression odds ratios to relative risk ratios by
the formula described below. Source: Zhang and K. Yu, 1998. Frequency
are allowed in order to calculate odds and risk ratios from 2 x 2 tables.
response must be binary, as does the first predictor, which is considered to
the risk factor or exposure..."
José Maria Pacheco de Souza
Professor Titular, aposentado; Colaborador Sênior
Departamento de Epidemiologia/Faculdade de Saúde Pública/Universidade de São
Av. Dr. Arnaldo, 715 - São Paulo, Capital - cep 01246-904
Fones: FSP= (11)3061-7747 Res= (11)3714-2403; (11)3768-8612
There is a growing literature on alternatives to logistic regression if the
outcome is common. I've attached some of the literature below. Just a quick
In general, two approaches are suggested: log-binomial and Poisson
regression with robust standard errors. The log-binomial approach is
preferred, unless the model fails to converge (which if frequently does)
(see Petersen & Deddens 2008; Deddens & Petersen 2008). Stata provides two
approaches to log-binomial: -glm- with the family and link specified, and
-binreg-, with the rr option.
I think that Poisson regression with robust standard errors (the robust
option) will be used more often in practice because it seldom has problems
converging. Zou (2004) suggests its use (as do Barros & Hirakata 2003) for
cohort studies where the relative risk is of interest and the base incidence
Spiegelman & Hertzmark (2005) in a commentary go as far as to recommend that
logistic regression not be used for risk or prevalence ratios when the
outcome is common.
Wacholder S. Binomial regression in GLIM: estimating risk ratios and risk
differences. Am J Epidemiol. Jan 1986;123(1):174-184.
Skov T, Deddens J, Petersen MR, Endahl L. Prevalence proportion ratios:
estimation and hypothesis testing. Int J Epidemiol. Feb 1998;27(1):91-95.
McNutt LA, Wu C, Xue X, Hafner JP. Estimating the relative risk in cohort
studies and clinical trials of common outcomes. Am J Epidemiol. May 15
McNutt LA, Hafner JP, Xue X. Correcting the odds ratio in cohort studies of
common outcomes. JAMA. Aug 11 1999;282(6):529.
Zou G. A modified Poisson regression approach to prospective studies with
binary data. American Journal of Epidemiology. 2004;159:702-706.
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. Oct 20 2003;3:21.
Deddens JA, Petersen MR. Approaches for estimating prevalence ratios. Occup
Environ Med. Jul 2008;65(7):481, 501-486.
Petersen MR, Deddens JA. A comparison of two methods for estimating
prevalence ratios. BMC Med Res Methodol. 2008;8:9.
Spiegelman D, Hertzmark E. Easy SAS calculateons for risk or prevalence
ratios and differences. Am J Epidemiol. Aug 1 2005;162(3):199-200.
Paul F. Visintainer, PhD
Baystate Medical Center
Springfield, MA 01199
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