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From |
José Maria Pacheco de Souza <jmpsouza@usp.br> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RES: RE: Poisson Regression |

Date |
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; jhilbe@aol.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 weights are allowed in order to calculate odds and risk ratios from 2 x 2 tables. The response must be binary, as does the first predictor, which is considered to be 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 Paulo Av. Dr. Arnaldo, 715 - São Paulo, Capital - cep 01246-904 Fones: FSP= (11)3061-7747 Res= (11)3714-2403; (11)3768-8612 www.fsp.usp.br/~jmpsouza Alexandra, 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 overview: 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 is common. 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 2003;157(10):940-943. 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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: RE: RES: RE: Poisson Regression***From:*"Visintainer, Paul" <Paul.Visintainer@baystatehealth.org>

**References**:**st: Poisson Regression***From:*Alexandra Boing <alexandraboing@yahoo.com.br>

**st: RE: Poisson Regression***From:*"Visintainer, Paul" <Paul.Visintainer@baystatehealth.org>

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