Be Wary of Using Poisson Regression to Estimate Risk and Relative Risk

Zhu C, Blizzard L, Stankovich J, Wills K and Hosmer Dw
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Zhu C: Menzies Institute for Medical Research, University of Tasmania, Australia
Blizzard L: Menzies Institute for Medical Research, University of Tasmania, Australia
Stankovich J: School of Medicine of Tasmania, Australia
Wills K: Menzies Institute for Medical Research, University of Tasmania, Australia
Hosmer Dw: Menzies Institute for Medical Research, University of Tasmania, Australia

Biostatistics and Biometrics Open Access Journal, 2018, vol. 4, issue 5, 120-122

Abstract: Fitting a log binomial model to binary outcome data makes it possible to estimate risk and relative risk for follow-up data, and prevalence and prevalence ratios for cross-sectional data. However, the fitting algorithm may fail to converge when the maximum likelihood solution is on the boundary of the allowable parameter space. Some authorities recommend switching to Poisson regression with robust standard errors to approximate the coefficients of the log binomial model in those circumstances.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:4:y:2018:i:5:p:120-122

DOI: 10.19080/BBOAJ.2018.04.555649

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