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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zhu C & Blizzard L & Stankovich J & Wills K & Hosmer DW, 2018. "Be Wary of Using Poisson Regression to Estimate Risk and Relative Risk," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 4(5), pages 120-122, February.
  • Handle: RePEc:adp:jbboaj:v:4:y:2018:i:5:p:120-122
    DOI: 10.19080/BBOAJ.2018.04.555649
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    References listed on IDEAS

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    1. Greenland, S., 1989. "Modeling and variable selection in epidemiologic analysis," American Journal of Public Health, American Public Health Association, vol. 79(3), pages 340-349.
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