title: Standardized binomial models for risk or prevalence ratios and differences.
abstract: BACKGROUND
  Epidemiologists often analyse binary outcomes in cohort and cross-sectional studies using multivariable logistic regression models, yielding estimates of adjusted odds ratios. It is widely known that the odds ratio closely approximates the risk or prevalence ratio when the outcome is rare, and it does not do so when the outcome is common. Consequently, investigators may decide to directly estimate the risk or prevalence ratio using a log binomial regression model.
  
  
  METHODS
  We describe the use of a marginal structural binomial regression model to estimate standardized risk or prevalence ratios and differences. We illustrate the proposed approach using data from a cohort study of coronary heart disease status in Evans County, Georgia, USA.
  
  
  RESULTS
  The approach reduces problems with model convergence typical of log binomial regression by shifting all explanatory variables except the exposures of primary interest from the linear predictor of the outcome regression model to a model for the standardization weights. The approach also facilitates evaluation of departures from additivity in the joint effects of two exposures.
  
  
  CONCLUSIONS
  Epidemiologists should consider reporting standardized risk or prevalence ratios and differences in cohort and cross-sectional studies. These are readily-obtained using the SAS, Stata and R statistical software packages. The proposed approach estimates the exposure effect in the total population.
authors.affiliation: Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA and david.richardson@unc.edu.
authors.forename: David B
authors.initials: DB
authors.lastname: Richardson
authors.affiliation: Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA and.
authors.forename: Alan C
authors.initials: AC
authors.lastname: Kinlaw
authors.affiliation: Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
authors.forename: Richard F
authors.initials: RF
authors.lastname: MacLehose
authors.affiliation: Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA and.
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Cole
mesh_terms: D002395:Catecholamines / Q000097:blood; D015331:Cohort Studies; D003327:Coronary Disease / Q000453:epidemiology; D003430:Cross-Sectional Studies / Q000592:standards*; D006801:Humans; D016015:Logistic Models; D008297:Male; D016017:Odds Ratio; D015995:Prevalence; D012015:Reference Standards; D018570:Risk Assessment
chemical_list: D002395:Catecholamines
keywords: Risk; prevalence; regression models; standardizations