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st: RE: Poisson Regression
st: RE: Poisson Regression
Tue, 15 Feb 2011 11:28:51 -0500 (EST)
I want to caution you on the use of the oddsrisk program. I wrote it so that it provided software support for the algorithm proposed by Zhang and Yu in 1998. The confidence intervals of the produced risk ratios appear to be baised as the number of predictors in the model increases. Other caveats exist as well, which I discuss at some length in my Logistic Regression Models, Chapter 5.5 (Odds ratios as approximations to risk ratios), pages 106-132.
Date: Mon, 14 Feb 2011 17:15:05 -0200
From: _Maria_Pacheco_de_Souza?= <firstname.lastname@example.org>
Subject: st: RES: RE: Poisson Regression
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>
the risk factor or exposure..."
Jos? Maria Pacheco de Souza
Professor Titular, aposentado; Colaborador S?nior
Departamento de Epidemiologia/Faculdade de Sa?de P?blica/Universidade
Av. Dr. Arnaldo, 715 - S?o 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
outcome is common. I've attached some of the literature below. Just a
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
approaches to log-binomial: -glm- with the family and link specified,
- -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
converging. Zou (2004) suggests its use (as do Barros & Hirakata 2003)
cohort studies where the relative risk is of interest and the base
Spiegelman & Hertzmark (2005) in a commentary go as far as to recommend
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
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
studies and clinical trials of common outcomes. Am J Epidemiol. May 15
McNutt LA, Hafner JP, Xue X. Correcting the odds ratio in cohort
common outcomes. JAMA. Aug 11 1999;282(6):529.
Zou G. A modified Poisson regression approach to prospective studies
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.
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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