title: Statistical Issues in Estimation of Adjusted Risk Ratio in Prospective Studies.
abstract: BACKGROUND
  Binary outcomes are common in prospective studies such as randomized controlled trials and cohort studies. Logistic regression is the most popular regression model for binary outcomes. Logistic regression yields an odds ratio that approximates the risk ratio when the risk of outcome is low. A consensus has been reached in an extensive argument in much of the literature that the risk ratio is preferred over the odds ratio for prospective studies. To obtain a model-based estimate of risk ratios, log-binomial regression has been recommended. However, this model may fail to converge and many methods have been provided as an alternative in these situations.
  
  
  METHODS
  In this paper, we discuss the methods to obtain adjusted risk ratios in settings with independent and clustered data and we will review the results of comparisons between these methods based on simulation studies, especially a large simulation study which was conducted by the authors. We use hypothetical examples to show how log-Poisson regression with modified standard errors can be used to estimate risk ratio in practice using popular statistical software.
  
  
  CONCLUSIONS
  The potential misinterpretation of odds ratios should be considered by researchers, especially when the risk of the outcome is high. When researchers want to estimate the effect of exposure or intervention by controlling potential covariates, the misinterpretation of odds ratios can be avoided using regression models that can estimate risk ratios instead of logistic regression. The log-Poisson regression with modified standard errors can be considered to estimate risk ratios in both independent and clustered data settings.
authors.affiliation: 1)Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.2)Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
authors.forename: Leila
authors.initials: L
authors.lastname: Janani
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Mohammad Ali
authors.initials: MA
authors.lastname: Mansournia
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Keramat
authors.initials: K
authors.lastname: Nourijeylani
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Mahmood
authors.initials: M
authors.lastname: Mahmoodi
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Kazem
authors.initials: K
authors.lastname: Mohammad
mesh_terms: D016015:Logistic Models; D016017:Odds Ratio; D011446:Prospective Studies; D012306:Risk; D012984:Software