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Estimation of relative risk using a log-binomial model with constraints

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  • Ji Luo
  • Jiajia Zhang
  • Han Sun

Abstract

The relative risk/prevalence ratio and odds ratio are very popular in medical research and epidemiological studies. The odds ratio is overused in practice due to its direct relation with the logistic regression. Interpreting the odds ratio in terms of “relative risk” may lead to incorrect inference on the prevalence of certain event. In this paper, we propose to estimate the relative risk using the log-binomial model by maximizing the likelihood with a linear constraint, which can be easily implemented by an existing function, such as “constrOptim” in R. Furthermore, we review other existing methods and compare their performance under a variety of settings using simulated data. We suggest the proposed and COPY methods in practice, based on its performance in the simulations. For illustration, we investigate the vigorous physical activity effects on obesity using data from the National Health and Nutrition Examination Survey, and the treatment effects on lung cancer mortality using data in R. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Ji Luo & Jiajia Zhang & Han Sun, 2014. "Estimation of relative risk using a log-binomial model with constraints," Computational Statistics, Springer, vol. 29(5), pages 981-1003, October.
    • Handle: RePEc:spr:compst:v:29:y:2014:i:5:p:981-1003
    DOI: 10.1007/s00180-013-0476-8
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    References listed on IDEAS

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    1. Jun-Mo Nam, 1999. "Power and Sample Size for Testing Homogeneity of Relative Risks in Prospective Studies," Biometrics, The International Biometric Society, vol. 55(1), pages 289-293, March.
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    Cited by:

    1. Florian Schwendinger & Bettina Grün & Kurt Hornik, 2021. "A comparison of optimization solvers for log binomial regression including conic programming," Computational Statistics, Springer, vol. 36(3), pages 1721-1754, September.

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